Modelling the distribution of São Tomé bird species ... › 5441 › 40e12180340...paisagem...
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UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE BIOLOGIA ANIMAL Modelling the distribution of São Tomé bird species: Ecological determinants and conservation prioritization Filipa Macedo Coutinho de Oliveira Soares Mestrado em Biologia da Conservação Dissertação orientada por: Doutor Ricardo Faustino de Lima Professor Doutor Jorge Palmeirim 2017
Transcript of Modelling the distribution of São Tomé bird species ... › 5441 › 40e12180340...paisagem...
UNIVERSIDADE DE LISBOA
FACULDADE DE CIÊNCIAS
DEPARTAMENTO DE BIOLOGIA ANIMAL
Modelling the distribution of São Tomé bird species: Ecological
determinants and conservation prioritization
Filipa Macedo Coutinho de Oliveira Soares
Mestrado em Biologia da Conservação
Dissertação orientada por:
Doutor Ricardo Faustino de Lima
Professor Doutor Jorge Palmeirim
2017
II
AGRADECIMENTOS
Quero começar por agradecer aos meus orientadores por todo o apoio incansável ao longo deste ano.
Este trabalho não seria possível sem todos os “brainstormings” durante as extensas reuniões ao longo
de várias semanas. Obrigada por me terem sempre incentivado a dar o meu melhor. Ricardo quero
agradecer-te toda a ajuda, logo desde o início quando esta tese era nada mais do que uma pequena ideia.
Não poderia ter pedido mais ou melhor orientação, obrigada pela tua infinita disponibilidade (eu sei o
quão “chata” eu consigo ser!). O meu obrigado também ao Professor Jorge Palmeirim, a sua ajuda foi
indispensável. Este trabalho não seria possível sem a incrível ajuda de ambos, o meu mais sincero
obrigado!
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”.
Quero ainda agradecer 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. Este
trabalho não teria sido possível 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 especial a Graeme M.
Buchanan, pelas orientações e pelo apoio no planeamento experimental deste trabalho.
Agradeço também à Associação Monte Pico, pelo alojamento durante a minha estadia em São Tomé.
Gostaria também de agradecer a todos os que contribuíram para o “Plano de acção internacional para a
conservação das espécies de aves Criticamente em Perigo de 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. Queria ainda agradecer em especial ao Eng. Arlindo
Carvalho, Diretor Geral do Ambiente por apoiar as nossas atividades em São Tomé. 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". Agradecemos a António Alberto, Nuno
Barros, Mariana Carvalho, Martin Dallimer, Hugulay Maia, Stuart Marsden, Martim Melo, Fábio Olmos
e Longtong Turshak por partilharem todas as suas observações.
Quero agradecer a Teotónio Soares pela disponibilidade e ajuda na construção dos loops para o script
dos modelos lineares generalizados.
III
Não posso deixar de agradecer a todas as pessoas que conheci em São Tomé. Obrigada Nity e Estevão
por terem sido os melhores ajudantes de campo. Aos dois, obrigada por terem respondido às minhas
perguntas, por terem sempre confiado em mim atrás do volante do nosso táxi (nem eu confiaria!), por
terem esperado sempre por mim em todas as nossas escaladas intermináveis. Obrigada por me terem
dado a conhecer todas as paisagens incríveis de São Tomé. Obrigada Lucy por nos teres recebido em
tua casa, por nos tratares praticamente como filhas quando não era tua obrigação, por teres sido para
mim a minha família longe de casa. Nunca conseguirei agradecer-te o suficiente tudo o que fizeste.
Obrigada Gégé por todas as conversas, por todos os risos e gargalhadas, por todos os cafés e bolachas,
por todas as caminhadas e passeios pela cidade. Obrigada por teres sido um grande amigo quando eu
mais precisava. Obrigada Adilécio por toda a ajuda com o carro, por vires sempre ao meu auxílio, ou
porque o carro não andava, ou porque andava pouco, ou porque a mala não fechava (acho que
praticamente tudo aconteceu àquele carro!). Obrigado Octávio por nos teres recebido em tua casa, ainda
hoje consigo lembrar-me dos teus famosos cozinhados. Obrigado Filipe e Fica por me terem recebido
de braços abertos e terem sempre mil e uma histórias para contar. Obrigada Mito e Sá também por me
terem acolhido, por me mostrarem Emolve e por todos os jantares à luz das velas cheios de gargalhadas
e boa disposição. Obrigada Juary, Gabi, Leonel, Catoninho, Lito, Lau, e todos os outros que me
ajudaram e tornaram a minha estadia em São Tomé uma das melhores experiências que até hoje vivi.
Quero agradecer aos meus pais, à minha irmã Rita e, também, às minhas duas avós por o apoio e
companhia ao longo deste ano (particularmente difícil!). Também, quero agradecer ao Afonso por ter
estado sempre lá, por ter aturado todas as minhas longas conversas sobre “bichos” (mesmo quando já
não conseguia ouvir mais!). Obrigada por seres quem és e por acreditares sempre em mim, mesmo
quando já nem eu acredito.
Obrigada a todos os meus companheiros e amigos pertencentes à “team cócós”. Obrigada Rita (e Zeus,
o melhor cão do mundo!), Manel, Catarina Vegy, Cátia, Catarina Vet, Marvel por toda as aventuras ao
longo deste ano (e que aventuras…desde atolar carros a perseguir assassinos em série!). Em especial,
quero agradecer ao Professor Francisco Petrucci-Fonseca, protagonista de grande parte das nossas
aventuras, por me ter dado a oportunidade de conhecer o que são talvez as serras mais bonitas de
Portugal!
Obrigada a todos os meus amigos e colegas que me ajudaram e apoiaram ao longo deste ano. Em
especial, um grande obrigado à Martina e à Bárbara por toda a companhia durante este longo ano e,
principalmente, durante a nossa aventura de dois meses em São Tomé. Foi difícil mas não a trocava por
nada, ou escolhia outras pessoas para irem comigo!
IV
RESUMO ALARGADO
O Homem tem vindo a alterar a ecologia do planeta, influenciando a distribuição das espécies e o
funcionamento dos ecossistemas. A comunidade científica tem dedicado muita atenção ao estudo do
impacto das atividades humanas na biodiversidade, uma vez que estas são largamente tidas como
responsáveis pela atual crise da perda de biodiversidade. Apesar da dificuldade em determinar com
exatidão os processos envolvidos, sabe-se que o aumento da população humana tem tido diversos
impactos negativos sobre os ecossistemas naturais. Há então necessidade de definir prioridades globais
de conservação, começando pela identificação das principais ameaças, como a alteração antropogénica
dos usos do solo. As florestas estão entre os ecossistemas terrestres mais ricos e também mais
ameaçados, sendo que nas últimas décadas a pressão humana tem vindo a aumentar sobretudo nas
florestas tropicais, estando muitas das suas espécies entre as mais ameaçadas do mundo.
A ocupação pelo Homem é sinónimo de fortes alterações na paisagem, tanto nos continentes como
em ilhas. No entanto, as ilhas tendem a possuir ecossistemas mais sensíveis, ricos em espécies
endémicas, que são particularmente vulneráveis à extinção. Posto isto, assumem uma elevada
importância na preservação da biodiversidade, principalmente dada a taxa de alteração do uso do solo
ser mais elevada nas ilhas do que nos continentes.
São Tomé é uma pequena ilha oceânica situada no Golfo da Guiné, África Central, a cerca de 255
km do continente. De origem vulcânica, possui uma topografia acidentada constituída por encostas de
declive acentuado e vales encaixados, com rios pontuados por grandes cascatas. Nas zonas costeiras
ocorrem estuários e mangais. Esta topografia explica o gradiente climático, caracterizado por elevados
níveis de humidade e chuvas frequentes trazidas pelos ventos fortes do sudoeste da ilha, que contrastam
com o nordeste semiárido. O forte gradiente climático tem vindo a moldar a distribuição dos
ecossistemas da ilha, mas a paisagem originalmente dominada por floresta tem sofrido alterações desde
a colonização humana, que teve início no final do século XV pelos Portugueses. As zonas planas de
baixa altitude são as mais intervencionadas, sendo constituídas maioritariamente por áreas não
florestadas, tais como savanas e áreas cultivadas. As florestas de baixa altitude foram substituídas por
plantações de sombra com árvores exóticas, como cafeeiro, cacaueiro e palmeiras. A floresta nativa mais
bem preservada está hoje restrita às áreas montanhosas no centro e sudoeste da ilha, rodeada por floresta
secundária, que resultou sobretudo da regeneração de plantações de sombra abandonadas. Apesar da
paisagem humanizada, São Tomé mantem uma flora e fauna muito diversas com um número muito
elevado de endemismos. As suas florestas têm um enorme interesse para a conservação, tendo sido
identificadas como as terceiras mais importantes no mundo para a conservação de espécies de aves
florestais.
Esta tese está dividida em dois capítulos com objetivos distintos, ambos relacionados com a
diversidade das aves de São Tomé. No primeiro capítulo, o objetivo principal é compreender como se
distribuem as aves ao longo da ilha, tendo como objetivos específicos: (1) identificar os principais
determinantes da distribuição das espécies de aves de São Tomé; (2) compreender como se relaciona o
endemismo com as respostas das espécies às variáveis ambientais; (3) analisar a relação entre as guildes
tróficas e a resposta das espécies às variáveis ambientais. No final, explorámos a relação entre as
respostas das espécies e os fatores determinantes da sua distribuição, dando um foco especial às espécies
endémicas e ameaçadas. Neste estudo foram realizados pontos de contagem de aves com duração de 10
minutos, onde foram registadas todas as espécies de aves. O período de amostragem foi de Janeiro a
Março de 2017, tendo sido a amostragem direcionada para as zonas não florestadas e de plantação de
sombra, bem como algumas zonas de floresta secundária. Estas observações foram agrupadas com
observações ocasionais e sistemáticas de estudos anteriores, que se tinham focado sobretudo nas áreas
V
florestais, atingindo um total de 3056 pontos amostrados em toda a ilha, onde foram registadas de forma
inequívoca 34 espécies de aves terrestres. Algumas variáveis ambientais, tais como o tipo de uso do
solo, a topografia, a precipitação, o declive, a altitude, a acessibilidade e a distância à costa, foram
mapeadas e utilizadas na construção dos modelos lineares generalizados para cada espécie. A ordenação
dos melhores modelos de distribuição potencial de cada espécie permitiu explorar a resposta de cada
espécie às variáveis ambientais. Uma análise de correspondência detrended foi realizada para avaliar a
relação entre endemismo, guildes tróficas e variáveis ambientais. O tipo de uso do solo foi identificado
como a variável mais importante para explicar a presença das espécies: as espécies endémicas tendem a
ocorrer preferencialmente na floresta, em zonas mais remotas, de elevada altitude e precipitação, por
sua vez as não endémicas preferem zonas não florestadas e mais humanizadas. A paisagem altamente
florestada de São Tomé permite, de uma forma geral, que haja uma dominância das espécies endémicas
na ilha. Muitas espécies endémicas estão ameaçadas, o que salienta a necessidade de proteger os habitats
florestais. Como tal, propomos um incremento da matriz florestal na paisagem, através da proteção da
floresta nativa remanescente e da expansão da floresta secundária, para a conservação das aves de São
Tomé.
No segundo capítulo, o objetivo principal é avaliar se o Parque Natural do Obô (PNO) inclui uma
representação adequada da diversidade de aves da ilha. Como tal, foi modelada a riqueza específica e a
composição das aves, dando especial atenção à distribuição de espécies endémicas e não endémicas. A
distribuição da diversidade de aves foi comparada com os limites da área protegida. Foi construída uma
base de dados com os pontos de contagem de aves de estudos anteriores, que foi complementada por
pontos adicionais realizados entre Janeiro e Março de 2017. Os pontos de contagem pertencentes à
mesma quadrícula de 1x1 km foram agrupados, criando conjuntos de cinco pontos de contagem por
quadrícula num total de 187 quadrículas, onde 36 espécies de aves terrestres foram registadas. Foram
utilizadas seis variáveis ambientais, tendo sido excluídas a rugosidade e a acessibilidade, para modelar
e mapear a riqueza específica total, das espécies endémicas e não endémicas, bem como a composição
da comunidade. Os resultados mostram que o número de espécies endémicas diminui nos habitats mais
humanizados, onde aumenta o de espécies não endémicas. O PNO não está a proteger as comunidades
mais ricas em aves, mas aquelas que têm mais aves endémicas, que ocorrem nas florestas mais bem
preservadas. Definidos com base na distribuição dos habitats e da população humana, os limites do
parque permitem a proteção das espécies endémicas ameaçadas, indiscutivelmente as de maior interesse
conservacionista. As florestas secundárias atuam como zona de transição para as zonas mais
humanizadas, protegendo as espécies endémicas das diversas ameaças antropogénicas. Deve ser
realizada uma revisão do zonamento do parque, de modo a integrar o atual conhecimento da distribuição
das espécies.
Este estudo permitiu aumentar o conhecimento atual sobre a distribuição das aves de São Tomé,
salientando a importância do tipo de uso do solo para a ocorrência das espécies e dando, pela primeira
vez, uma perspetiva sobre a distribuição da riqueza e da composição das comunidades de aves ao longo
da ilha. Esta informação deve ser utilizada na definição de estratégias de conservação e monitorização.
No entanto, é necessário aprofundar o conhecimento sobre a distribuição de cada espécie, ao longo do
ano e a escalas espaciais mais pormenorizadas, por forma a compreender melhor a resposta de cada
espécie à degradação florestal. Destacamos ainda a importância de quantificar o impacto de outras
ameaças, como a caça e as espécies introduzidas. Toda esta informação irá permitir definir ações
prioritárias de conservação para espécies-alvo, adequadas às necessidades ecológicas de cada espécie, o
que é especialmente importante no caso das espécies mais ameaçadas como a galinhola Bostrychia
bocagei, o picanço Lanius newtoni e o anjoló Neospiza concolor.
Palavras-chave: endemismo; guilde trófica; Parque Natural do Obô; espécies ameaçadas; riqueza
específica
VI
ABSTRACT
Human actions are rapidly changing ecosystems all over the world. Anthropogenic land use change
affects the structure and functioning of ecosystems, leading to irreversible biodiversity losses.
Understanding how human actions influence biodiversity is therefore key to prevent further biodiversity
loss. Tropical forests are among the most diverse and threatened ecosystems, and the increasing human
pressure, high number of threatened species and major habitat loss calls for conservation actions.
São Tomé is a small oceanic island located in the Gulf of Guinea, Central Africa. Despite the
human-dominated landscape, this island maintains a high biodiversity, rich in endemic species, and its
forests are of great conservation value. This study has the main goals of:
- Understanding how bird species are distributed throughout the island. Occasional and
systematic observations were gathered from previous studies and complemented by additional 10-
minute point counts. A total of 3056 sampling locations were used to understand the distribution of 34
terrestrial bird species. Species-specific generalized linear models and detrended correspondence
analysis based on presence-absence, were used to explore the links between endemism, feeding guilds
and environmental variables. Land use was the most important variable to explain bird species
occurrence. The endemics tended to prefer forests located in remote, wetter areas, on rugged terrain and
at higher altitudes, while the non-endemics favoured the drier flat lowlands, in more accessible locations
and devoid of forest. The change in bird species assemblage from forest endemics to open habitat non-
endemic granivores is clearly a result of the land use intensification gradient. The current overall
dominance of endemic species across the island is maintained by São Tomé’s forest-dominated
landscape. The dependency of endemics on forest highlights the urgent need for their protection. Based
on these results, we suggest that protecting remaining native forests and expanding secondary forests
will improve landscape matrix and contribute to the survival of the endemic-rich island avifauna
worldwide.
- Assessing how the São Tomé Obô Natural Park (STONP) represents the avifauna of the island.
The boundaries of the STONP were defined in 2006, based on the distribution of native ecosystems and
of the human population. We compared them to the distribution of bird diversity, by modelling species
richness and composition. We used systematic observations from previous studies supplemented by
additional bird counts. A total of 187 1x1 km quadrats were sampled by five 10-minute point counts
each. Thirty-six terrestrial bird species were identified unambiguously and considered for analyses. The
proportion of endemic species decreased along the land use intensification gradient. The STONP did
not protect the most species-rich bird assemblages, but included those that were richest in endemics, the
best-preserved forests. Thus, the STONP is focusing on the protection of endemic threatened birds,
which arguably have the highest global conservation interest. The secondary forests surrounding the
park act as a transition zone to areas with more intensive land use types, hence protecting it from
pervasive threats. We suggest the zonation of STONP is revised, using the same factors considered for
the delimitation of the protected area and the current knowledge on species distribution. This study
reveals that protecting well-preserved natural areas with low human density might be a good proxy to
identify areas of high conservation interest, when there is little information on the distribution of the
multiple components of biodiversity.
Keyword: community ecology; species distribution modelling; endemism; feeding guild;
threatened species
VII
TABLE OF CONTENTS
GENERAL INTRODUCTION ............................................................................................................... 1
CHAPTER 1: The role of natural gradients and ecosystem humanization in determining the
distribution of bird species in São Tomé ................................................................................................. 5
INTRODUCTION ............................................................................................................................... 5
METHODS .......................................................................................................................................... 6
Study Area ....................................................................................................................................... 6
Data Compilation ............................................................................................................................ 7
Field Methods .................................................................................................................................. 7
Sampling design .............................................................................................................................. 7
Bird sampling .................................................................................................................................. 7
Characterizing environmental variables ......................................................................................... 8
Data Analysis .................................................................................................................................. 9
Exploratory analysis ........................................................................................................................ 9
Generalized linear models ............................................................................................................... 9
Relative variable importance ........................................................................................................ 10
Response to environmental variables ............................................................................................ 10
RESULTS .......................................................................................................................................... 10
Relative variable importance ......................................................................................................... 10
Response of endemic and non-endemic species to environmental variables ................................ 11
Feeding guilds response to environmental variables ..................................................................... 14
Species land use type preferences ................................................................................................. 16
DISCUSSION ................................................................................................................................... 17
Determinants of bird species distribution ...................................................................................... 17
Differential response of endemic and non-endemic bird species .................................................. 17
Differential response of bird species based on feeding guilds ...................................................... 18
Consequences of land use intensification to the endemic-rich avifauna of São Tomé ................. 18
CHAPTER 2: Is the existing protected network adequate for the conservation of the endemic-rich
avifauna of São Tomé Island? ............................................................................................................... 20
INTRODUCTION ............................................................................................................................. 20
METHODS ........................................................................................................................................ 21
Study Area ..................................................................................................................................... 21
Data Compilation .......................................................................................................................... 22
Field Methods ................................................................................................................................ 22
VIII
Sampling design ............................................................................................................................ 22
Bird sampling ................................................................................................................................ 22
Characterizing environmental variables ....................................................................................... 23
Data Analysis ................................................................................................................................ 24
Exploratory analysis ...................................................................................................................... 24
Generalized linear models ............................................................................................................. 24
Generalized dissimilarity modelling.............................................................................................. 25
Generalized dissimilarity model categorization ............................................................................ 25
Assessing the adequacy of the Obô Natural Park to represent São Tomé bird diversity .............. 26
RESULTS .......................................................................................................................................... 26
Modelling bird species richness .................................................................................................... 26
Bird species compositional dissimilarity ....................................................................................... 28
Is the São Tomé Obô Natural Park adequate to protect the island’s avifauna? ............................. 29
DISCUSSION ................................................................................................................................... 31
Contrasting responses of endemic and non-endemic species to the environment ......................... 31
Species assemblages vary mostly in response to habitat humanization ........................................ 32
Is the São Tomé Obô Natural Park adequate to protect the island’s bird diversity? ..................... 33
Final remarks ................................................................................................................................. 33
FINAL CONSIDERATIONS ................................................................................................................ 35
REFERENCES ...................................................................................................................................... 36
SUPPLEMENTARY MATERIALS ..................................................................................................... 45
SECTION I: Environmental Variables .............................................................................................. 45
SECTION II: São Tomé Bird Species ............................................................................................... 60
SECTION III: Binomial Generalized Linear Models ........................................................................ 61
SECTION IV: Proportion of species occurrence per land use type .................................................. 67
SECTION V: Exploratory analysis for species richness and composition modelling....................... 68
SECTION VI: Poisson Generalized Linear Models .......................................................................... 70
SECTION VII: Generalized Dissimilarity Modelling ....................................................................... 73
SECTION VIII: R scripts .................................................................................................................. 77
IX
LIST OF TABLES Table 1.1. Response of endemic (E) and non-endemic (N), and of distinct feeding guilds (omnivores - O, granivores
- G, frugivores – F, and carnivores – C) to environmental variables………………………………………………12
Table 2.1. Species richness and endemic species richness estimated for each average point inside 1x1 quadrats,
called, respectively, species and endemic richness point estimate……………………………………………….30
Table S1. (Section I – Supp. Materials) Environmental variables description…………………………………….45
Table S2. (Section I – Supp. Materials) Environmental raster’s characteristics…………………………………...46
Table S3. (Section II – Supp. Materials) Bird species’ characteristics……………………………………………60
Table S4. (Section III – Supp. Materials) Validation of the best multivariable model……………………………..61
Table S5. (Section III – Supp. Materials) Relative variable importance (RVI)………………………………...…62
Table S6. (Section III – Supp. Materials) Single-variable model coefficients. …………………………………...63
Table S7. (Section III – Supp. Materials) Kruskal-Wallis rank test to analyse the difference in environmental
variables between endemic and non-endemic species, as well as among feeding guilds. ………………………….64
Table S8. (Section IV – Supp. Materials) Proportion of species occurrence per land use type and topography
class………………………………………………………………………………………………………......67
Table S9. (Section V – Supp. Materials) Bird species’ characteristics…………………………………………...68
Table S10. (Section VI – Supp. Materials) Validation of the best model…………………………………………70
Table S11. (Section VI – Supp. Materials) Species richness and environmental variables………………………...72
Table S12. (Section VII – Supp. Materials) Significance test of GDM model……………………………………73
Table S13. (Section VII – Supp. Materials) Significance test for each variable in GDM model…………………....74
Table S14. (Section VII – Supp. Materials) Importance of each predictor variable……………………………….75
LIST OF FIGURES Figure 1.1. Location of sampling point counts and occasional observations (n = 3056) in São Tomé Island………...8
Figure 1.2. Relative variable importance (RVI) of each environmental variable for each bird species generalized
linear model…………………………………………………………………………………………………..11
Figure 1.3. Response of endemic (E) and non-endemic (N) species to environmental variables…………………13
Figure 1.4. Detrended Correspondence Analysis (DCA) showing the relationship between endemism, feeding guilds
and environmental variables…………………………………………………………………………………...14
Figure 1.5. Feeding guild (omnivores - O, granivores - G, frugivores – F, and carnivores - C) response to
environmental variables……………………………………………………………………………………….15
Figure 1.6. Proportion of occurrence of each species by land use types…………………………………………..16
Figure 2.1. São Tomé Island sampling locations………………………………………………………………..23
Figure 2.2. Predictive maps of (a) total species richness, (b) endemic species richness and (c) non-endemic species
richness, shown in contrast to the boundaries of the Obô Natural Park and buffer zone……………………………27
Figure 2.3. (a) Continuous and (b) categorical composition dissimilarity maps, as obtained from generalized
dissimilarity modelling (GDM)………………………………………………………………………………..28
Figure 2.4. Total, endemic and non-endemic species richness inside (In) and outside (Out) Obô Natural Park…….29
Figure 2.5. Proportion of endemic species and frequency of endemic species for each GDM class (1 to 5)………...30
Figure S1. (Section I – Supp. Materials) Altitude in meters……………………………………………………...47
Figure S2. (Section I – Supp. Materials) Ruggedness…………………………………………………………...48
Figure S3. (Section I – Supp. Materials) Slope in degrees……………………………………………………….49
Figure S4. (Section I – Supp. Materials) Distance to coast line in degrees………………………………………..50
Figure S5. (Section I – Supp. Materials) Separation of flat plain areas and middle slope areas…………………….51
Figure S6. (Section I – Supp. Materials) Transforming continuous Topographic Position Index in a categorical
variable……………………………………………………………………………………………………….52
Figure S7. (Section I – Supp. Materials) Topography Position Index……………………………………………53
X
Figure S8. (Section I – Supp. Materials) Building remoteness index…………………………………………….54
Figure S9. (Section I – Supp. Materials) Remoteness Index…………………………………………………….55
Figure S10. (Section I – Supp. Materials) Rainfall in millimetres………………………………………………..56
Figure S11. (Section I – Supp. Materials) Land use map created by S. Mikulane (resolution of 10x10 meters)…….57
Figure S12. (Section I – Supp. Materials) Land use……………………………………………………………..58
Figure S13. (Section I – Supp. Materials) Correlogram between environmental variables
.....................................................................................................................................................................................................59
Figure S14. (Section III – Supp. Materials) Relative variable importance (RVI) of each continuous environmental
variable……………………………………………………………………………………………………….65
Figure S15. (Section III – Supp. Materials) Relative variable importance (RVI) of each continuous environmental
variable in endemic and non-endemic species…………………………………………………………………..65
Figure S16. (Section III – Supp. Materials) Relative variable importance (RVI) of each continuous environmental
variable in every feeding guild species group…………………………………………………………………...66
Figure S17. (Section V – Supp. Materials) Correlogram between environmental variables and response
variables.....................................................................................................................................................................................69
Figure S18. (Section VI – Supp. Materials) Pearson and Deviance Residuals……………………………………71
Figure S19. (Section VII – Supp. Materials) Overall model fit in explaining the observed dissimilarities………….73
Figure S20. (Section VII – Supp. Materials) K-fold cross-validation of GDM…………………………………...74
Figure S21. (Section VII – Supp. Materials) Response curves of each predictor variable…………………………76
LIST OF ABBREVIATIONS AND ACRONYMS
E Endemics
N Non-endemics
O Omnivores
G Granivores
F Frugivores
C Carnivores
NF Native forest
SF Secondary forest
SP Shade plantation
NFA Non-forested areas
F Flat areas
V Valleys and deep valleys
M, Middle Middle slope areas
U, Upper Upper slope areas
R Ridges
Amaboc Amaurocichla bocagei, São Tomé Short-tail
Ananew Anabathmis newtonii, São Tomé Sunbird
Bosboc Bostrychia bocagei, Dwarf Ibis
Colmal Columba malherbii, São Tomé Bronze-napped Pigeon
Coltho Columba thomensis, São Tomé Maroon Pigeon
Dretho Dreptes thomensis, Giant Sunbird
Lannew Lanius newtoni, São Tomé Fiscal
Neocon Neospiza concolor, São Tomé Grosbeak
Oricra Oriolus crassirostris, São Tomé Oriole
Otuhar Otus hartlaubi, São Tomé Scops Owl
Plogra Ploceus grandis, Giant Weaver
XI
Plosan Ploceus sanctithomae, São Tomé Weaver
Primol Prinia molleri, São Tomé Prinia
Serruf Serinus rufobrunneus, (São Tomé) Príncipe Seed-eater
Teratr Terpsiphone atrochalybeia, São Tomé Paradise Flycatcher
Tresan Treron sanctithomae, São Tomé Green Pigeon
Turoli Turdus olivaceofuscus, São Tomé Thrush
Zosfea Zosterops feae, São Tomé White-eye
Zoslug Zosterops lugubris, São Tomé Speirops
Agapul Agapornis pullaria, Red-headed Lovebird
Bubibi Bubulcus ibis, Cattle Egret
Chrcup Chrysococcyx cupreus, Emerald Cuckoo
Collar Columba larvata, São Tomé Cinnamon Dove
Cotdel Coturnix delegorguei, Harlequin Quail
Estast Estrilda astrild, Common Waxbill
Eupalb Euplectes albonotatus, White-winged Widowbird
Eupaur Euplectes aureus, Golden-backed Bishop
Euphor Euplectes hordeaceus, Fire-crowned Bishop
Loncuc Lonchura cucullata, Bronze Mannikin
Milmig Milvus migrans, Yellow-billed Kite
Onyful Onychognathus fulgidus, São Tomé Chestnut-winged Starling
Strsen Streptopelia senegalensis, Laughing Dove
Uraang Uraeginthus angolensis, Southern Cordon-bleu
Vidmac Vidua macroura, Pin-tailed Whydah
DistCoast Distance to coast
SR Species richness
ESR Endemic species richness
NSR Non-endemic species richness
STONP São Tomé Obô Natural Park
PNO Parque Natural do Obô
1
GENERAL INTRODUCTION
Human population is a major threat to biodiversity
Humans have been shaping the environment all over the planet, influencing the distribution of
species and functioning of ecosystems. Many studies have associated human activities to the current
crisis of biodiversity loss (Balmford & Bond 2005). Defining and measuring biodiversity is a complex
and difficult task, therefore studying how human actions affect biodiversity is a major challenge.
Additionally, biodiversity threats are unevenly distributed throughout the world, making it difficult to
allocate conservation efforts. The urgent need to establish global conservation priorities has been a hot
topic between conservationists (Brooks et al. 2006). Myers et al. (2000) identified 25 “biodiversity
hotspots”, characterized by having a high concentration of endemic species and also great levels of
habitat loss. Anthropogenic land use change is considered a main threat to species across all taxonomic
groups (Luck 2007). Tropical forests, known to have both high species diversity and human pressure,
are rapidly being converted for agriculture, timber production and other uses, generating human-
dominated landscapes and leading to forest degradation and destruction (Gardner et al. 2009). Habitat
loss is considered to be one of the main reasons for the extinction of many species in the past decades
(Sodhi et al. 2004; Stork 2010; Szabo et al. 2012). Many extinct species were island-endemics and
because the projected rate for land-cover changes in islands is expected to increase, these fragile
ecosystems are a growing global concern for conservationists (Manne et al. 1999). Many believe that
given their conservation risks, smaller areas and high endemic species richness, islands could offer high
returns for species conservation efforts, and therefore should be a high priority in global biodiversity
conservation (Johnson & Stattersfield 1990).
São Tomé Island as a study case
São Tomé is an oceanic island, which is an excellent model to study the factors influencing species
distribution, as well the adequacy of protected areas to represent biodiversity. It is an 857 km2 island,
holding a remarkable biodiversity with many endemic species and a wide gradient of land use
intensification.
Together with Príncipe, it constitutes the Democratic Republic of São Tomé and Príncipe, which is
in the Gulf of Guinea, Central Africa. At about 255 km from mainland Africa, São Tomé is of volcanic
origin, which explains its rugged topography composed of steep slopes, deep valleys and high ridges,
up to 2024 meters above sea level at the São Tomé Peak (Salgueiro & Carvalho 2001). Rivers are
intrinsically associated with these narrow valleys, creating multiple waterfalls. The water slows down
near the ocean creating small estuaries occasionally with mangroves. In the north-east, the terrain is
flatter, especially if compared to the centre and west of the island. This diverse topography explains the
incredibly varied climate found in São Tomé. The high mountains are a barrier to the strong winds,
bringing heavy rains and coming from the south-west of the island. Thus, the south-west is characterized
by high levels of humidity, having an almost permanent cloud cover, frequent rains and an annual
rainfall of over 6000 mm, while the north-east is much drier, some areas receiving less than 600 mm of
rain each year (Tenreiro 1961). São Tomé’s climate is characterized by a wet season, which occurs for
most of the year, and two drier seasons. The longer dry season, called “gravana”, starts in May and ends
in September, being more evident in the north of the island, and corresponding to the coldest months of
the year. The shorter dry season, the “gravanito”, lasts for a few weeks in January and February. The
2
strong altitudinal gradient influences the mean annual temperature; coastal areas can reach maximum
mean annual temperatures of 25.5º C, while at higher altitudes it might be as low as 9º C (Silva 1958).
The strong climatic gradient has shaped the distribution of ecosystems throughout São Tomé.
Having highly diverse landscapes with many different ecosystems, four land use types are usually
recognized: non-forested areas, shade plantations, secondary forests and native forests (Jones & Tye
2006). Native forests are characterized by having a high density of native flora and few exotic species
(e.g. Elaeis guineensis). Mangroves established along the lowest parts of the rivers and coastal lagoons
can also be considered native forests. Exell (1944) defined three distinct rainforest types following the
altitudinal gradient: lowland forests (up to 800 meters a.s.l), montane forests (between 800 and 1400
meters a.s.l.) and mist forests (above the 1400 meters a.s.l., along ridges of the central mountain range).
Secondary forests appeared with the regeneration of abandoned shade plantations and with the intensive
exploitation of timber, holding an assemblage poorer in forest species and with shade and fruit trees
(e.g. breadfruit Artocarpus altilis, African nutmeg Pycnanthus angolensis). Shade plantations initially
created as intensive monocultures by the Portuguese replaced most of the lower altitude forests. It is an
agroforestry system composed mostly of exotic trees, such as cocoa Theobroma cacao, coffee Coffea
sp. and coral trees Erythrina sp. (Salgueiro & Carvalho 2001). Nowadays, shade plantations have
become more varied and produce many other crops, mostly for the internal market (banana Musa sp.,
cocoyam Colocasia sculenta and Xanthosoma sp., oil palm Elaeis guineensis, avocado Persea
americana, papaya Carica papaya). Non-forested land uses include active and resting agricultural areas
with different systems, such as monocultures of sugar cane Saccharum sp, coconut Cocos nucifera or
oil palm, and artificial savannahs and smallholder horticultures (Diniz et al. 2002).
The human occupation of São Tomé started in the late 15th century, after the Portuguese discovered
the island, allegedly uninhabited and entirely covered by forest. Since then, the dried coastal lowland
forests have suffered the most, being first cleared for sugar cane (Tenreiro 1961). During the 19th and
20th century, extensive cocoa and coffee plantations were grown in shade plantations, in large
agricultural plantation systems, known as “roças”, further decreasing the area covered by native forests
(Oliveira 1993; Frynas 2003). Nowadays, many shade plantations rely on medium and smallholdings
that produce many subsistence products besides the main export crops. Swidden agriculture appeared to
meet the demand for horticultural foods, expanding in forest borders and replacing abandoned shade
plantations, being therefore included in non-forested land uses (Eyzaguirre 1986; Albuquerque et al.
2008). In the centre and south-west of the island a large patch of well-preserved native forest remains,
nowadays enclosed by secondary forest, which in turn is surrounded by active shade plantations mixed
with several non-forested land uses (Jones et al. 1991; Diniz et al. 2002).
São Tomé has an incredible diverse flora and fauna. The right amount of isolation allowed many
species to evolve in environments distinct from those found in the mainland (Miller et al. 2012). São
Tomé and Príncipe hold 28 endemic bird species in an area little over 1000 km2 (Melo 2006). Out of 45
resident terrestrial species, São Tomé alone has 17 single-island endemics, 3 endemics to the Gulf of
the Guinea oceanic islands (Annobón, São Tomé and Príncipe) and 8 widespread species represented in
the island by an endemic subspecies (Jones & Tye 2006). As is often the case in other islands, some
species are larger than their mainland relatives. That is the case of the Giant Sunbird Dreptes thomensis,
the Giant Weaver Ploceus grandis, the São Tomé Grosbeak Neospiza concolor, the São Tomé Speirops
Zosterops lugubris and the São Tomé Thrush Turdus olivaceofuscus. However, a few species, like the
Dwarf Ibis Bostrychia bocagei, become smaller (Melo 2006; Melo et al. 2017). The lack of natural
predators also made some species tame, such as the São Tomé Green Pigeon Treron sanctithomae, the
São Tomé Maroon Pigeon Columba thomensis and the Dwarf Ibis.
São Tomé is in a “biodiversity hotspot” and about 23.3% of its territory is included in Important
Bird Areas (Myers et al. 2000; Fishpool & Evans 2001). Its forests are of great conservation interest,
belonging to one of Earth’s biological ecoregions, named Gulf of Guinea Islands (Olson & Dinerstein
3
1998). Also, the forests were identified as the third most important in the world for forest bird species
conservation (Buchanan et al. 2011). The long history of human occupation has led to habitat destruction
and degradation, especially in the lower altitude forests, which were mostly converted to shade
plantations. Endemic species have a long relationship with native forest, and many are dependent on
these habitats (Rocha 2008; de Lima 2012). This way, the destruction or transformation of these forests
might make them into unsuitable habitats. Apart from land use change, the introduction of species and
direct exploitation are the main threats to São Tomé avifauna (Jones et al. 1991; de Lima 2012). Like in
many oceanic islands, free of native predators of birds, introduced land mammals like rats, mice, dogs,
cats, pigs, among others, become a serious threat to native bird species (Johnson & Stattersfield 1990;
Dutton 1994; Blackburn et al. 2004). Three endemic bird species are considered Critically Endangered,
the Dwarf Ibis, the São Tomé Fiscal Lanius newtoni and the São Tomé Grosbeak, the São Tomé Maroon
Pigeon is Endangered, while six other endemic bird species are Vulnerable, two are Near Threatened
and eight are Low Concern (IUCN 2017).
To protect both native fauna and flora species, as well as their natural habitats, from human
activities, the São Tomé Obô Natural Park (STONP) was created in 2006, covering 295 km2 (Direcção
Geral do Ambiente 2006). This protected area was born under the umbrella of the “Ecosystemes
Forestiers en Afrique Centrale” (ECOFAC) program, which started in 1992, funded by the European
Commission, to encourage the conservation and sustainable use of forests in Central Africa. A buffer
zone was also envisaged, but never official. The STONP action and management plan were first created
in 2008, and revised in 2014 (Albuquerque et al. 2008), but implementation remains weak (de Lima et
al. 2015).
Thesis scope
This thesis has two main goals, both related to understanding the bird diversity in São Tomé. In the
first chapter, we explore bird species distribution and their responses to several environmental variables,
using generalized linear models (GLMs) and paying close attention to the differences between endemic
and non-endemic species, as well as between feeding guilds. Predictive distribution models are used to
understand where species occur, which is essential to understand ecological requirements, as well as for
conservation and population management (Guisan & Zimmermann 2000; Rushton et al. 2004). Logistic
regressions are frequently used by ecologists to model species distribution, having gained a certain
appeal because presence-absence data is easy to collect in the field. We considered vegetation,
topographic, climatic and anthropogenic variables as potential predictors in logistic models, improving
our understanding of which factors condition species occurrence (Seoane et al. 2003; Thuiller et al.
2004).
In the second chapter, we model bird species richness and composition patterns to assess if the
STONP adequately covers the island’s diverse avifauna. Three generalized linear models with poisson
distribution were created to explain total, endemic and non-endemic species richness (Guisan &
Zimmermann 2000), while generalized dissimilarity modelling (GDM) was used to map composition
patterns (Ferrier et al. 2007). GDM is a novel statistical technique that analyzes and predicts spatial
patterns of turnover in community composition (beta diversity). Being an extension of matrix regression,
it is designed specifically to accommodate two types of nonlinearity commonly encountered in large-
scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and
observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional
turnover at different positions along environmental gradients (Ferrier et al. 2007; Arponen et al. 2008).
In short, this approach compares community composition and environmental variables at pairs of sites
to predict compositional difference as a function of environmental difference, extrapolating the
prediction beyond surveyed sites. The resulting models give a spatially continuous prediction of
4
turnover, and thus of the spatial structure of diversity (Fitzpatrick et al. 2013; Brown et al. 2014).
Predictive distribution maps are used nowadays to design protected areas, evaluate human impacts on
biodiversity and test biogeographical hypotheses (Seoane et al. 2004). In this study, maps describing
species richness and composition patterns were built to evaluate if the STONP is covering relevant
components of the bird assemblage in São Tomé.
5
CHAPTER 1: The role of natural gradients and ecosystem humanization in
determining the distribution of bird species in São Tomé
Abstract: Anthropogenic land use change is the main driver of the ongoing biodiversity crisis.
Understanding how species respond to land use changes is thus key to minimize the current species
extinction rate. São Tomé is a small oceanic island, where forest degradation is a main threat to the
endemic-rich avifauna. To preserve this invaluable avifauna, we tried to understand how bird species
are distributed throughout the island. We gathered occasional and systematic observations from previous
studies, which were later combined with additional 10-minute point counts, adding to a total of 2398
bird point counts and 658 occasional observations. Thirty-four terrestrial bird species were
unambiguously identified and considered in subsequent analyses. Species-specific generalized linear
models and detrended correspondence analysis based on presence-absence, were used to explore the
links between endemism, feeding guilds and environmental variables. Land use was the most important
variable to explain bird species occurrence. The endemics tended to prefer forests in wetter, rugged,
higher altitude, and remote areas, while the non-endemics favoured flat lowland non-forested areas and
shade plantations. São Tomé’s forest-dominated landscape ensures an overall dominance of endemic
species, but a change in bird species assemblage from forest endemic species to open habitat non-
endemic granivore species was found to be a result of the land use intensification gradient. Many of the
forest endemics are threatened, highlighting the urgent need to protected forested habitats. We suggest
landscape matrix improvement, through the protection of the remaining native forest and the expansion
of secondary forest, as the most important conservation measure to ensure the future of the endemic-
rich avifauna of the islands.
Keyword: endemism; feeding guild; generalized linear model; land use types; threatened species
INTRODUCTION
Understanding how animals and plants are distributed on Earth, in both space and time, is a
challenging task, especially in our constantly changing planet. A wide range of factors, such as food
availability, shelter, environmental abiotic factors (e.g. temperature, humidity), biotic interactions (e.g.
competition, predation, mutualism, host-parasite interactions, facilitation), physical barriers (e.g. rivers,
mountains), climate (e.g. global climate change), disturbances (e.g. fires, floods, pathogens), among
many others, are listed to influence species distribution (Brown 1984; Lawton 1999; Mackey &
Lindenmayer 2001; Thomas et al. 2004). All these factors interact at different spatial and temporal
scales, imposing limits on species distribution which are expressed from local to global spatial scales.
Our understanding of species distribution started with qualitative analyses: observing and recording
the relationship between species distributions and the physical environment. Today, numerical
techniques are widely used for describing species distribution patterns and making predictions (Elith &
Leathwick 2009). For example, species distribution models (SDMs), that combine observations of
species occurrence or abundance with environmental variables, allow the prediction of species
distributions across the landscape (Guisan & Zimmermann 2000; Rushton et al. 2004).
Human activities have been shaping ecosystems across the globe, especially by land use change
that is known to alter ecosystem patterns and processes, as well as species distributions (Blair 1996;
Cincotta et al. 2000). Anthropogenic land use changes have been considered a major driver of the
6
ongoing biodiversity crisis (Myers et al. 2000). Therefore, understanding species response to human-
induced land use change is essential to guide conservation actions (Maestas et al. 2003; Benton et al.
2003; Chacea & Walsh 2006). Agricultural demand is by far the main cause for land use change (Phalan
et al. 2011). Urban sprawling is also promoting the conversion of natural and even agricultural land,
further reducing the availability of habitats for wildlife (Assandri et al. 2017). Both are predicted to
continue growing in the nearby future. Land use change has consistently reduced overall habitat quality,
increased ecosystems fragmentation, isolation and degradation, and promoted the introduction of exotic
species (Cadenasso & Pickett 2001; Foley 2005; McKinney 2006; Stork 2010). A study conducted in
the north-eastern Brazilian Amazonia showed plantations had a relatively impoverished amphibian and
lizard communities, a frequently discussed consequence of land use change (Gardner et al. 2007). In
tropical forests, where the species diversity and human pressure is higher, land use change is expected
to cause great habitat loss (Sodhi et al. 2004; Walter et al. 2007; Gardner et al. 2009; Stork 2010; Szabo
et al. 2012).
Local extinction of birds and mammals have been described as a consequence of anthropogenic
land use change (Brooks et al. 1999; Sodhi et al. 2004; IUCN 2017). Extinctions have been far more
frequent on islands than on continents (Manne et al. 1999). The unique flora and fauna found on insular
ecosystems are extremely vulnerable to human actions, and with the increasing rate of land use change,
these fragile ecosystems are becoming a growing global concern among conservationists.
This main goal of this study is to understand how bird species are distributed in response to natural
and anthropogenic factors, using São Tomé, an endemic-rich oceanic island with a known land use
intensification gradient, as an example (Melo 2006; Miller et al. 2012; de Lima et al. 2015). We focus
on three specific goals: (1) identifying the key determinants of the distribution of bird species; (2)
understanding how endemism relates to the response of bird species to environmental variables; and (3)
analyse the relationship between feeding guilds and bird species response to environmental variables.
We will also explore the relationship between key determinants and species response, paying special
attention to endemic and threatened species.
METHODS
Study Area
São Tomé, together with the neighbouring island of Príncipe, form the Democratic Republic of São
Tomé and Príncipe, located in the Gulf of Guinea, Central Africa. This oceanic island is just north of
the Equator and about 255 km west of the African Continent. For an 857 km2 island, it has a remarkably
unique avifauna (Stattersfield et al. 1990; Peet & Atkinson 1994; Leventis & Olmos 2009). Out of 45
resident terrestrial species, 17 are single-island endemics, 3 are endemic to the Gulf of the Guinea
oceanic islands (Annobón, São Tomé and Príncipe) and 8 are widespread species represented in the
island by an endemic subspecies (Jones & Tye 2006). The high endemism rate is associated with its
location in relation to the African continent: close enough to allow migration, and far enough to allow
speciation by isolation (Melo 2006). This island is considered a “biodiversity hotspot” and, recently, its
lowland forest belong to one of Earth’s biological ecoregions, the Gulf of Guinea Islands (Olson &
Dinerstein 1998; Myers et al. 2000). Also, these forests were identified as the third most important in
the world for forest bird species conservation (Buchanan et al. 2011).
As in many other oceanic islands, human occupation in São Tomé led to the introduction of several
species, namely several bird species, most of which native from the African Continent. Before human
intervention, the island was almost entirely covered by forest and the topography was responsible for
7
the strong climatic gradients that shaped the distribution of ecosystems. Human colonization, resulted
in much of the lowland forests and some montane forests being replaced by plantations (Jones et al.
1991). Only the inaccessible rugged wet areas in the south-west and centre of the island remain covered
by native forest, which is currently surrounded by secondary forest, resulting from logging and
plantation abandonment. Enclosing this land use type are extensive areas of active shade coffee and
cocoa plantations, a type of agroforestry, which is mixed with non-forested areas, such as oil palm
monocultures, horticultures and open savannahs (Exell 1944; Tenreiro 1961; Jones et al. 1991).
Despite the long history of intensive conversion to anthropogenic land use, São Tomé’s landscape
is still dominated by forested ecosystems. The native forest is almost entirely classified as São Tomé
Obô Natural Park (STONP), which covers almost one third of the island (Albuquerque et al. 2008).
Unfortunately, the protection and conservation efforts have not been effective and in the last decades
human pressure on natural resources has been increasing fast, and the area covered by native forest and
shade plantations has decreased, while secondary forest and non-forested areas have been expanding
(Salgueiro & Carvalho 2001).
Data Compilation
In this study, we gathered all records from a single observer, obtained in 2009 and in 2010, for a
total of 300 point counts (de Lima 2012), plus 1653 point counts and 677 occasional from BirdLife
International São Tomé and Príncipe Initiative (BISTPI), collected between 2013 and 2015 (de Lima et
al. 2017). In both studies, point counts were separated by at least 200 meters, to ensure independence,
and all birds detected during 10 minutes were registered, regardless of the distance. This information
was compiled in a single bird species occurrence database, which had a GIS component.
Field Methods
Sampling design
To identify under-sampled areas in previous studies from which we compiled data, we over-
imposed the bird occurrence database on the map of São Tomé. The island was then divided in 1x1 km
quadrats, grouped in groups of four to form 2x2 km quadrats (de Lima et al. 2017). All 2x2 km quadrats
that had more than half of their area occupied by the ocean were excluded. We considered sampled all
the 2x2 km quadrats that had at least one 1x1 km quadrat with five 10-minute point counts sampled.
Between January and March 2017, we sampled 91 out of the remaining unsampled 96 2x2 km quadrats,
located mostly in non-forested low-altitude areas across the island.
Bird sampling
Each of the 2x2 quadrats were sampled by performing five bird point counts in a randomly selected
1x1 km quadrat (Fig. 1.1), largely following the BISTPI methodology (de Lima et al. 2017). The location
of the point counts was chosen to ensure a distance of at least 200 meters between point counts, thereby
ensuring independence and that the environmental variability inside each quadrat was sampled in the
approximate proportion in which they occurred in the quadrat.
In each point count, all bird species detected visually and aurally were registered by an experienced
observer, during a 10 minute period, and regardless of the distance. To maximize the number of sampled
points during our short sampling period, counts were made throughout the day, from approximately 6
am until 5 pm.
8
Characterizing environmental variables
To model the distribution of bird species, we obtained geographically explicit information on
altitude, ruggedness, slope, distance to the coast, topography, remoteness, rainfall and land use across
São Tomé, using Quantum GIS v. 2.8.3 and v. 2.14.8 (Quantum GIS Development Team 2009a; Table
S1 & S2).
The variable altitude was derived in meters from a 90 meters resolution Digital Elevation Model
(DEM) (Silva 1958; Salgueiro & Carvalho 2001; NASA Jet Propulsion Laboratory 2016; Fig. S1). The
ruggedness and slope were also calculated from the DEM raster, using the “raster terrain analysis” QGIS
plugin (Quantum GIS Development Team 2009b; Fig. S2 & S3). Slope was primarily calculated in
decimal degrees and then transformed to percentage. Distance to the coast was calculated as the
minimum linear distance in decimal degrees between each pixel and the nearest point on the coast line,
using the DEM and the QGIS “distance matrix” tool (Quantum GIS Development Team 2009a; Fig.
S4). The topography was represented using a Topography Position Index (TPI) which allows comparing
of each cell’s elevation to the mean elevation of a specified neighbourhood (Jenness 2007). The TPI was
calculated using the DEM and the “topography position index” tool in the QGIS GDAL algorithm
provider (Quantum GIS Development Team 2009c) and a 0.05º radius neighbourhood, which allows for
a good representation of terrain ruggedness and elevation in São Tomé. TPI was later transformed in a
five-category discrete variable: flat areas, valleys, middle slopes, upper slopes and ridges (Fig. S5, S6
& S7). Remoteness is expressed as an index that translates the difficulty of movement through the
landscape, and it was created using the “accumulated cost” QGIS GDAL algorithm provider (Quantum
Figure 1.1. Location of sampling point counts and occasional observations (n = 3056)
in São Tomé Island. The lines in the background represent the 100 m elevation isolines.
9
GIS Development Team 2009d). This index is a cost accumulated surface based on a friction surface
derived from slope and weighted by the human population density (Tobler 1993; Instituto Nacional de
Estatística 2016; Fig. S8 & S9). Rainfall was obtained by digitizing a map with the island’s mean annual
precipitation in millimetres (Silva 1958; Fig. S10). The land use map (Fig. S12) was created mostly by
visual interpretation of 2014 satellite images (Google Earth 2017), supplemented by 2009-2017 field
land cover information (de Lima 2012; de Lima et al. 2012), 1970 land use map (de Carvalho Rodrigues
1974), military maps (Missão Hidrográfica de Angola e S. Tomé 1958), a 2011-13 preliminary land use
map (S. Mikulane, unpublished data - see Fig. S11) and expert knowledge.
All variables were standardised to a common in raster grid, using the nearest neighbour sampling
method and the TPI raster as a geometric reference. This standardization was made using QGIS “align
rasters” tool (Quantum GIS Development Team 2009a), and resulted in a pixel’s size of 0.000833º x
0.000833º and a raster with 359 x 471 cells. Each point count was characterized for each environmental
variable using the “point sampling tool” QGIS plugin (Quantum GIS Development Team 2009e).
Data Analysis
All statistical analyses were made in R v. 3.3.2 using RStudio v. 1.0.143 (R Development Core
Team 2017).
Exploratory analysis
All bird data was compiled in a single database of 2408 point counts and 677 occasional
observations. We excluded all species that are aquatic, difficult to identify or had less than 20 presences
(Table S3), obtaining a total of 34 species that was considered for subsequent analyses. Point counts
with no record of these species or that had inconsistencies between the field land cover classification
and the 2014 land use map were also removed, leading to a final of 2398 point counts, plus 658
occasional observations.
Multicollinearity was tested using Spearman’s rank correlation coefficient, and visualized in a
correlogram built using the “corrgram” package (Wright 2016; Part I, Section VIII). Ruggedness was
excluded, since its correlation coefficient with slope was higher than 0.8 (Fig. S13).
Variance homogeneity and no outliers were identified by the boxplots drawn for each
environmental variable using the “vegan” package (Oksanen 2015).
Generalized linear models
The data were divided in training and testing sets, using the “caTools” package: 70% of the points
were used to create binomial generalized linear model (GLM) to explain species presence (Rushton et
al. 2004), while the remaining 30% were used to validate the models (Tuszynski 2014; Part II, Section
VIII).
We used Variance Inflation Factors (VIF) to double-check multicollinearity, and, once again,
ruggedness was chosen to be excluded from all species models for being the only predictor variable
having VIFs larger than 10. For each species, we generated all possible models based on the different
combinations of explanatory variables, and ranked based on the Akaike Information Criterion corrected
for small sample sizes (AICc), using the “dredge” function from the “MuMIn” package (Barton 2016).
The goodness of fit was analysed with the McFadden’s index in the “pscl” package (Jackman et al.
2015). We validated the predicted values and calculated the receiving operating characteristic (ROC)
curve. The area under the curve (AUC) was calculated to examine the model’s performance with the
“ROCR” package (Sing et al. 2015; Table S4).
10
Relative variable importance
To identify which variables best explain the presence of each species, we ran the “model averaging”
function of the “MuMIn” package to obtain relative variable importance (RVI). Bird species were
separated in endemic and non-endemic species, and by feeding guild: carnivore (including insectivore),
frugivore, granivore and omnivore (Jones & Tye 2006; HBW Alive 2017; Table S3). For each
explanatory variable, we used Kruskal-Wallis rank tests to evaluate the difference in RVI values
between endemic and non-endemic, and between feeding guilds (Table S5). To perform post hoc
pairwise comparisons between feeding guilds we used Dunn-tests with Benjamini-Hochberg corrections
(Thissen et al. 2002). These analyses were done using the “stats” and “FSA” packages (Ogle 2017; Part
V, Section VIII).
Response to environmental variables
To analyse the response of each species to continuous variables, single-variable logistic regression
models were created to explain species’ presence and obtain coefficient values (Table S6).
The proportion of occurrence in each land use type and in each TPI class was calculated for every
species, correcting for sampling effort. Then, it was calculated for each group of species: endemics, non-
endemics, carnivores, frugivores, granivores and omnivores. To evaluate the differences between
endemic and non-endemic species, and between feeding guilds, among each land use type and
topography class, Kruskal-Wallis rank tests were performed using the “stats” package. As previously,
Dunn-tests with the Benjamini-Hochberg correction for multiple comparisons were run to analyse
differences between feeding guilds (Part V, Section VIII). Both these tests were also used to evaluate
the differences in coefficient values between endemic and non-endemic species, and between feeding
guilds.
To visualize the links between endemism, feeding guilds and environmental variables, a detrended
correspondence analysis (DCA) was made. The proportion of occurrence of each species in each land
use type was also explored graphically, to gain a better understanding of how endemism and threat status
relate to land use types.
RESULTS
Only 658 out of the 3056 final data points referred to occasional observations. On average, each
species appeared in 24.4% of the systematic point counts, ranging from 88.4% for the São Tomé Sunbird
Anabathmis newtonii to 0.6% for the São Tomé Grosbeak Neospiza concolor (Table S3).
Relative variable importance
The most important variable to explain the occurrence of bird species in São Tomé was land use,
followed by rainfall and remoteness (Fig. 1.2 & S14). Distance to coast, altitude and topography had
intermediate importance, while slope was the least important.
When looking at the species individual responses to environmental variables, it is clear that land
use was more important to the endemic species than to the non-endemic. On the other hand, rainfall was
more important to non-endemic species. Topography seemed relevant to endemic species distribution,
but was the least important variable to non-endemic species.
11
When comparing the RVI of endemic and non-endemic species, only land use (H = 6.19, df = 1, p
= 0.013) and topography (H = 5.674, df = 1, p = 0.017) had significant differences, and both were more
important to the endemics (Table S7 & Fig. S15).
Among feeding guilds, altitude (H = 8.3603, df = 3, p = 0.039) and slope (H = 10.373, df = 3, p =
0.016) were the only variables having significantly different RVI values. Altitude was more important
to explain the presence of carnivores than that of omnivores, while slope was less important to frugivores
than to any other feeding guild (Table S7 & Fig. S16).
Response of endemic and non-endemic species to environmental variables
The endemic species tended to have significantly higher values for all continuous environmental
variables, when compared to the non-endemic (rainfall: H = 14.295, df = 1, p = 0.0002; remoteness: H
= 12.765, df = 1, p = 0.0004; distance to coast: H = 11.555, df = 1, p = 0.0007; altitude: H = 12.032, df
= 1, p = 0.0005; slope: H = 13.519, df = 1, p = 0.0002; Table 1.1, Fig. 1.3 & 1.4).
The proportion of occurrence in almost all land use types was significantly different between
endemic and non-endemic species. Endemics tended to occur preferentially in native (H = 17.794, df =
Figure 1.2. Relative variable importance (RVI) of each environmental variable for each bird species generalized linear
model. The RVI is represented by a colour gradient, in which: darker cells indicate higher values. The RVI values range
from 0 to 1. Endemic (E) and non-endemic (N) species are grouped together and separated by a black line.
E
N
12
1, p = 2.461 x 10-5; Table 1.1 & S8, Fig. 1.3 & 1.4) and secondary forest (H = 11.672, df = 1, p = 0.0006),
while non-endemic species preferred non-forested areas (H = 17.206, df = 1, p = 3.355 x 10-5).
Endemic and non-endemic species occurrence among each topography class was also almost
significantly different for all classes (Table 1.1, Fig. 1.3 & 1.4). Endemics tended to occur mostly in
valleys, middle and upper slope areas, and also ridges (valleys: H = 13.911, df = 1, p = 0.0002; middle
slope: H = 16.328, df = 1, p = 5.328 x 10-5; upper slope: H = 16.609, df = 1, p = 4.593 x 10-5; ridges: H
= 18.115, df = 1, p = 2.08 x 10-5), while the non-endemic species occur in a bigger proportion in flat
areas (H = 17.468, df = 1, p = 2.922 x 10-5).
Variables Endemism (KW test) Feeding Guilds (Dunn-test)
Rainfall 0.0002 E >>> N 0.0219 C > G
Remoteness 0.0004 E >>> N 0.0048 C >> G
Distance to Coast 0.0007 E >>> N 0.0052 C >> G
Altitude 0.0005 E >>> N 0.0185 C > G
Slope 0.0002 E >>> N 0.0240
0.0420
C > G
O > G
Land Use
Native Forest 2.461 x 10-5 E >>> N 0.020
0.023
C > G
F > G
Secondary Forest 0.0006 E >>> N 0.021
0.015
F > G
O > G
Shade Plantation - - - -
Non-Forested Areas 3.355 x 10-5 E <<< N 0.038
0.038
C < G
F < G
Topography
Flat Plain Areas 2.922 x 10-5 E <<< N
0.024
0.019
0.047
C < G
F < G
O < G
Valleys 0.0002 E >>> N - -
Middle Slope 5.328 x 10-5 E >>> N
0.033
0.020
0.024
C > G
F > G
O > G
Upper Slope 4.593 x 10-5 E >>> N 0.036 F > G
Ridges 2.08 x 10-5 E >>> N 0.021
0.028
C > G
F > G
Table 1.1. Response of endemic (E) and non-endemic (N), and of distinct feeding guilds (omnivores - O, granivores
- G, frugivores – F, and carnivores – C) to environmental variables. For continuous variables, the differences
between E and N coefficients were assessed using Kruskal-Wallis rank tests (KW), while between feeding guild
coefficients were assessed using Dunn-tests with Benjamini-Hochberg correction. For categorical variables, land
use and TPI, Kruskal-Wallis rank tests were used to analyse differences between endemic and non-endemic species,
while between feeding guilds Dunn-tests with Benjamini-Hochberg correction were used. Only p-value < 0.05 are
shown.
13
Fig
ure
1.3
. R
espo
nse
of
end
emic
(E
) an
d n
on
-en
dem
ic (
N)
spec
ies
to e
nvir
on
men
tal
var
iab
les.
Th
e b
oxp
lots
rep
rese
nt
the
con
tin
uo
us
var
iab
les
coef
fici
ents
ob
tain
ed f
rom
sin
gle
-
var
iab
le m
od
els:
th
e th
ick l
ine
sho
ws
the
med
ian
, th
e b
ox t
he
firs
t an
d t
hir
d q
uar
tile
s, t
he
wh
isker
s th
e ex
trem
es,
and
th
e d
ots
th
e o
utl
iers
. T
he
bar
-plo
ts r
epre
sen
t th
e st
and
ard
ized
pro
po
rtio
n o
f o
ccu
rren
ce i
n e
ver
y l
and u
se t
yp
e (n
ativ
e fo
rest
- N
F,
seco
nd
ary f
ore
st -
SF
, sh
ade
pla
nta
tion
– S
P a
nd n
on
-fo
rest
are
as -
NF
A)
and
to
po
gra
ph
y c
lass
(fl
at a
reas
– F
,
val
leys
- V
, m
idd
le s
lop
e ar
eas
- M
, up
per
slo
pe
area
s -
U,
rid
ges
– R
).
14
Feeding guilds response to environmental variables
The feeding guilds showed significant differences in all coefficients obtained from single-variable
models (rainfall: H = 8.706, df = 3, p = 0.033; remoteness: H = 11.232, df = 3, p = 0.011; distance to
coast: H = 11.96, df = 3, p = 0.008; altitude: H = 8.764, df = 3, p = 0.033; slope: H = 8.714, df = 3, p =
0.033; Table 1.1, Fig. 1.4 & 1.5). The granivores tended to have lower values for all continuous
environmental variables. These differences were always significant, when comparing to carnivores (Z
= 3.331 for distance to coast and Z = 3.351 for remoteness with p < 0.01, and Z = 2.879 for slope, Z =
2.959 for altitude and Z = 2.901 for rainfall with p < 0.05), and also when comparing to omnivores for
slope (Z = 2.456, p = 0.042). Granivores had the most distinct land use type and topography preferences.
They tended to use less native forest than carnivores (Z = 2.928, p = 0.020) and frugivores (Z = 2.660,
p = 0.023), less secondary forest than frugivores (Z = 2.699, p = 0.021) and omnivores (Z = -3.021, p =
0.015), and more non-forested areas than carnivores (Z = -2.733, p = 0.038) and frugivores (Z = -2.495,
p = 0.038; Table 1.1, Fig. 1.4 & 1.5). They were also clearly associated with flat areas (Z = -2.873 for
carnivores, Z = -2.731 for frugivores, Z = 2.269 for omnivores, with p < 0.05).
Figure 1.4. Detrended Correspondence Analysis (DCA) showing the relationship between endemism, feeding guilds and
environmental variables. Each point represents a species, which is identified by the corresponding acronym (See List of
Abbreviations and Acronyms, pages IX to X). The black dots represent the endemic and the grey the non-endemic species. The
shape of the points represents the feeding guilds (F - frugivores, G- granivores, O - omnivores, and C – carnivores). The panel
on the top right corner shows how are environmental variables related to the DCA axes: land use type (NF - native forest, SF -
secondary forest, SP - shade plantation, and NFA - non-forested areas), TPI (Flat - flat areas, Valleys - valleys, Middle -
intermediate slope areas, Upper - upper slope areas, Ridges – ridges), Slope, Altitude, Rainfall, Distance to coast (DistCoast),
and Remoteness.
15
Fig
ure
1.5
. F
eed
ing g
uil
d (
om
niv
ore
s -
O,
gra
niv
ore
s -
G,
fru
giv
ore
s – F
, an
d c
arn
ivo
res
- C
) re
spo
nse
to e
nvir
on
men
tal
var
iab
les.
Th
e b
oxp
lots
rep
rese
nt
the
conti
nuo
us
var
iab
les
coef
fici
ents
ob
tain
ed f
rom
sin
gle
-var
iab
les
mo
del
s: T
he
thic
k l
ine
sho
ws
the
med
ian
, th
e bo
x t
he
firs
t an
d t
hir
d q
uar
tile
s, t
he
wh
isker
s th
e ex
trem
es, an
d t
he
dots
th
e ou
tlie
rs. T
he
bar
-plo
ts
rep
rese
nt
the
stan
dar
diz
ed p
rop
ort
ion
of
occu
rren
ce i
n e
ver
y l
and
use
typ
e (n
ativ
e fo
rest
- N
F,
seco
nd
ary f
ore
st -
SF
, sh
ade
pla
nta
tio
n -
SP
, no
n-f
ore
st a
reas
- N
FA
) an
d t
opo
gra
ph
y c
lass
(fla
t ar
eas
- F
, val
leys
- V
, m
idd
le s
lop
e ar
eas
M, u
pp
er s
lop
e ar
eas
- U
, ri
dges
- R
).
16
Species land use type preferences
Most of the 19 endemic species clearly preferred forested land use types. Nine had more than 75%
of their presences in forest, seven had more than 50% in native forest and four had more than 75% in
native forest (Fig. 1.6).
Some endemic species like the Green Pigeon Treron sanctithomae, the Scops Owl Otus hartlaubi
and the Oriole Oriolus crassirostris are also frequently found inside secondary forests. A few endemic
species, like the São Tomé Thrush Turdus olivaceofuscus, the São Tomé Prinia Prinia molleri or the
São Tomé Sunbird, were almost evenly distributed among all land use types. The Giant Weaver Ploceus
grandis, on the other hand, is an exception inside endemic species, being an omnivorous, can be
commonly found inside plantations, such as palm plantations (Atkinson et al. 1991). In contrast with
the endemics, the 15 non-endemics were clearly associated with non-forested areas, and avoided forests.
Ten had more than half of their presences in non-forested areas, while only four even occurred in native
forest. The Pin-tailed Whydah Vidua macroura, the Southern Cordon-bleu Uraeginthus angolensis and
the Bronze Mannikin Lonchura cucullata are examples of non-endemic granivores found mostly in non-
forested areas.
Figure 1.6. Proportion of occurrence of each species by land use types. Species are grouped by endemism (E – endemic;
N – non-endemic), and by conservation status (CR – critically endangered; EN – endangered; VU – vulnerable; NT – near
threatened; LC – least concern). Within each group, species are ranked according land use type preferences (native forest
– black, secondary forest – dark grey, shade plantation – light grey, and non-forested areas – white).
EN
LC
E N
NT
VU
CR
17
The Cinnamon Dove Columba larvata and the Chestnut-winged Starling Onychognathus fulgidus
were the non-endemic species that clustered with the endemic (de Lima et al. 2012). These two species
are represented in São Tomé by endemic subspecies that are fairly different from the continental ones,
and that might warrant being classified as distinction species (Peet & Atkinson 1994; Leventis & Olmos
2009; Pereira 2013). Our results show the Emerald Cuckoo Chrysococcyx cupreus, also represented in
São Tomé by another endemic subspecies, in a similar position (Fig. 1.4 & 1.6). All other non-endemics
cluster away from the endemics (Fig. 1.4) and clearly avoid forested land use types (Fig. 1.6), including
the endemic subspecies of Harlequin Quail Coturnix delegorguei.
All threatened species were endemic, and species with higher threat status tended to have stronger
links to native forest, except for the São Tomé White-eye Zosterops feae, which had less than 25% of
its presences in this land use type.
DISCUSSION
We identified land use as the most important environmental variable to model the distribution of
34 bird species in São Tomé.
Determinants of bird species distribution
Considering all São Tomé bird species, land use was without a doubt the most important variable
to explain their distribution, followed by rainfall and remoteness (Fig. 1.2). All three variables are related
to each other, and with the topography of the island.
Early studies had already suggested that land use was an important determinant of São Tomé bird
species distribution (Jones & Tye 2006), but our results suggest it is actually the most important.
Worldwide, habitat has also been repeatedly identified as the primary determinant of species distribution
and abundance (Seoane et al. 2004; Tejeda-Cruz & Sutherland 2004; Dallimer & King 2007; Rocha et
al. 2015). Flora composition and structure, characteristics clearly dependent on land use, have been
considered important factors to explain the distribution and abundance of many passerine species
(Maestas et al. 2003).
Differential response of endemic and non-endemic bird species
The endemics were clearly associated with forested land uses, usually located in remote areas, away
from the coast, and where the rainfall is higher. They also tended to prefer higher altitudes and steeper
slopes, namely valleys and ridges (Table 1.1, Fig. 1.3 & 1.4). On the contrary, the non-endemics
preferred more intensive land uses, such as shade plantations and non-forested areas. Notably they were
associated with drier regions of the island, in the accessible lowlands near the coast.
The species response to land use change is congruent with previous work, which had already
observed a rise in non-endemics and a decrease in endemics along the land use intensification gradient
(de Lima et al. 2012). A pattern that also makes sense, considering that the native endemic-rich avifauna
of São Tomé evolved in a forest-dominated landscape (Atkinson et al. 1991).
Shade plantations were the only land use type where there was no clear preferences associated with
endemism (Table 1.1). This agroforestry system usually consists of several agricultural crops shaded by
high canopy trees. Despite being almost entirely composed by introduced plant species, it provides
ecosystems with intermediate environmental conditions that are both suitable for endemic and non-
endemic species (Rocha 2008; de Lima et al. 2014). These findings coincide with studies performed
across the globe, showing that shade plantations and other agroforestry systems support a depleted
18
proportion of the native biodiversity, often mixed with introduced species (Thiollay 1999; Waltert et al.
2004; Foley 2005; de Lima et al. 2014).
Differential response of bird species based on feeding guilds
Being almost entirely composed of endemic species, the frugivores also had a strong association
with forested land uses (Table 1.1, Fig. 1.4 & 1.5). They preferred remote areas, far from the coast, with
high levels of rainfall and steeper slopes, such as valleys and ridges. Most carnivores are also endemic
species, meaning their response resembled that of the endemics. Out of 13 omnivores, eight are endemic
species, and so their response was the sum of different species response, having no clear pattern linked
to endemism. On the contrary, all granivores are non-endemic species, and therefore were associated
with the more intensive land uses in the drier, lowlands of the island.
Having evolved in a forest-dominated landscape, most endemic species are frugivores and
carnivores that rely on forest resources, and therefore might not be capable of adapting to land use
intensification. The endemic species tended to avoid the non-forested areas and shade plantations, where
the lack of suitable habitat and other resource limitations, e.g. food, were responsible for their
disappearance (de Lima et al. 2012). In contrast, the non-endemics, especially granivores that are open
habitat specialists, occurred preferentially in more intensive land uses. Other authors had too stated that
primarily frugivorous and insectivorous forest specialists were less likely to occur and less abundant in
more intensively used habitats, where habitat generalists thrive (Newbold et al. 2013)
As in other studies, the granivore species response and apparent avoidance of forested land uses
suggested that non-endemic species were introduced during the colonization, quickly occupying the
more intensively managed habitats (Atkinson et al. 1991; Jones & Tye 2006; Rocha 2008; de Lima et
al. 2012). The low occurrence of granivore non-endemics inside forested land uses reinforces the idea
of no direct competition with forest endemic species, also stated in a different study on avian community
responses (Thiollay 1999).
More intensive land uses tended to have a higher human pressure, which negatively impacts and
conditions the endemic species occurrence (Rocha 2006; de Lima et al. 2012; Andren 1994). Previous
authors stated that hunting might be an important threat, especially to frugivore endemic birds, like the
two most favoured quarry species, the São Tomé Maroon Pigeon Columba thomensis and the São Tomé
Green Pigeon (Carvalho 2015; Margarido 2015).
Mammals, such as pigs Sus domesticus, cats Felis catus, black and brown rats Rattus sp., mona
monkeys Cercopithecus mona, amongst others, were also brought to the island during colonization
(Dutton 1994). The introduction of mammal species in insular ecosystems is considered a great threat
to the native avifauna (Johnson & Stattersfield 1990; Blackburn et al. 2004; Szabo et al. 2012). In São
Tomé, it is thought the introduced mammal species have a wide distribution among all land use types
and thus have an overall negative impact on endemic bird species.
Consequences of land use intensification to the endemic-rich avifauna of São Tomé
Endemic species were clearly associated with São Tomé forested landscape, declining towards the
more intensive land uses, where on the contrary the non-endemic species found suitable conditions.
Other studies had already observed a decay in the number of endemic species with greater land use
intensification (Rocha 2006; de Lima et al. 2012).
Since the colonization, lowland forests and some montane forests were progressively replaced by
coffee and cocoa plantations, leaving only the more inaccessible, wet areas of the southwest and central
of the island covered by relatively undisturbed and well-preserved forest. We believe that with the
discovery of offshore oil reserves (Frynas et al. 2003) and the rapid human population growth (Instituto
19
Nacional de Estatística 2016), the pressure on forest habitats will continue to rise. Our results suggest
that the increasing land use intensification, whether by converting São Tomé forests into intensively
managed land uses, or by promoting forest degradation, will compromise the long-term persistence of
endemic species (Ndang’ang’a et al. 2014; de Lima et al. 2017).
As found in similar studies, the gradient of land use intensification is the main responsible for the
changes in bird species assemblages, from forest endemic species to non-native open habitat specialists
(Hughes et al. 2002; Naidoo 2004; Waltert et al. 2005). Most forest endemic species are frugivores and
carnivores, therefore the more intensive land uses, such as non-forested areas and shade plantations,
lack suitable conditions essential for these species survival (e.g. habitat, food availability, among
others). Other studies also found that insectivores were associated with reduced resilience to habitat
conversion (Thiollay 1995; Waltert et al. 2005).
Land use intensification had strong negative impacts on São Tomé endemic-rich avifauna. The
endemic species, highly dependent on the forested habitats, have been replaced by the non-endemic
species inside the intensively managed land uses (Pardini et al. 2010). Non-endemic species were able
to colonize these disturbed areas, being mostly granivore and omnivore, open habitat species (Naidoo
2004; Tejeda-Cruz & Sutherland 2004), which suggest they were introduced to the island (Jones & Tye
2006). This change from endemic to non-endemic species also suggests the gradient of land use
intensification is acting as a facilitator of the spread of non-native species (Didham et al. 2007).
The most threatened endemic species in São Tomé are also the ones with the higher association to
the native forest, thus rising even more their already high conservation value (Margarido 2015; de Lima
et al. 2017). The Fiscal Lanius newtoni, the Grosbeak and the Dwarf Ibis Bostrychia bocagei, all
occurred almost uniquely inside native forests, being considered critically endangered by IUCN (IUCN
2017). In order to protect São Tomé threatened endemic species and their forested habitats, we urge the
need to reduce and ultimately cease land use intensification, thus preventing further conversion and
degradation of forested land uses.
At last, São Tomé provides a good example of how a strong gradient of land use intensification,
inside small historical forest-dominated islands, can rapidly reduce the proportion of forested land uses,
while simultaneously acting as a facilitator of the spread of non-native species. Given our findings, we
suggest focusing first on the full understanding of the native threatened species response to land use
intensification, and just then, define specific conservation measures to protect indigenous forests and
restore already degraded land uses. This strategy promotes the maintenance of an endemic-rich avifauna,
while preventing the spread of non-native species facilitated by land use intensification.
20
CHAPTER 2: Is the existing protected network adequate for the
conservation of the endemic-rich avifauna of São Tomé Island?
Abstract: Tropical forests are some of the most diverse and threatened terrestrial ecosystems. The
increasing human pressure, high number of threatened species and major habitat loss forces conservation
action prioritization. São Tomé is a small oceanic island with an endemic-rich avifauna. It has a single
protected area: the São Tomé Obô Natural Park (STONP), whose boundaries were defined in 2006,
based on ecosystem and human population distribution. We compared the distribution of bird diversity
with the boundaries of the park to assess how it represented the island’s avifauna. Systematic
observations from previous studies were gathered and supplemented by additional bird counts. Five 10-
minute point counts were grouped in 1x1 km quadrats (n = 187). Thirty-six terrestrial bird species were
identified unambiguously and considered for analyses. The proportion of endemic bird species decreases
along the land use intensification gradient: forest endemics decline towards humanized habitats, where
non-endemic granivores are most abundant. The STONP did not protect the most species-rich bird
assemblages, but covered most of the best-preserved forests, which are the richest in endemic species.
The STONP boundaries are well located for the protection of endemic threatened birds, arguably those
of higher global conservation interest. Secondary forests act as a transition zone to humanized areas,
and protect the park from pervasive threats. The zonation of the STONP should be revised, using the
same factors considered for the delimitation of the protected area and the current knowledge on species
distribution. This study suggests that protecting well-preserved natural areas with low human density
might be a good proxy to identify areas of high conservation interest, when there is little information on
the distribution of the multiple components of biodiversity.
Keywords: São Tomé Obô Natural Park; species richness; generalized dissimilarity modelling;
species distribution modelling; conservation planning
INTRODUCTION
Human activities are causing a biodiversity crisis (Brooks et al. 2006), through the transformation
and sometimes complete destruction of natural habitats (Stork 2010). Temperate forests are a living
proof of the devastating impact of humans (Pimm & Askins 1995), but few species have been considered
extinct in continental tropical forests. Tropical forests include some of the most diverse terrestrial
ecosystems but, in recent decades, also some of the most threatened (Myers et al. 2000), due to the
increasing human pressure, which is expected to rise in upcoming years with the growing human
population (Cincotta et al. 2000; Luck 2007). Nowadays many tropical species are threatened by habitat
loss and degradation (IUCN 2017). The rise of extinction rates in tropical forests is therefore likely to
occur in the near future (Brooks et al. 1999).
The high number of threatened species, the great diversity of threats and the limited funding force
conservationists to establish priorities. Twenty-five “biodiversity hotspots” have been identified by
exceptional concentrations of endemic species and habitat loss, containing 44% of the Earth's plant
species and 35% of its vertebrates in just 1.4% of its land surface (Myers et al. 2000). These hotspots
are the focus of many conservation programs, aiming to reduce the current rate of biodiversity loss
(Cincotta et al. 2000).
21
Protected areas are one of the main conservation actions to safeguard threatened species and their
habitats. About 38% of the “biodiversity hotspots” are already protected in parks and reserves, which
range from highly restrictive areas where all human activities are excluded to more inclusive
management strategies involving local communities (Schwartzman et al. 2000).
São Tomé is an oceanic island located in the Gulf of Guinea. It is included in a “biodiversity
hotspot” (Myers et al. 2000), and the high concentration of avian endemism contributes to its unique
biodiversity (Melo 2006; Miller et al. 2012; de Lima et al. 2015). Its forests, together with Príncipe and
Equatorial Guinea, belong to the Earth’s biological ecoregions named Gulf of Guinea Islands forests,
which has a critical/endangered conservation status (Olson & Dinerstein 1998). Most recently, its
lowland forests were identified as the third most important in the world for the conservation of forest
bird species (Buchanan et al. 2011).
All this incredible biodiversity urged the creation of a protected area. In August 2006, the São Tomé
Obô Natural Park (STONP) became official, covering almost one third of the island. A buffer zone
surrounding the park was later added for further protection (Direcção Geral do Ambiente 1999). Due to
the lack of resources and enforcement capacity, illegal activities are still a regular sight within the
protected area (Albuquerque et al. 2008).
Our main goal is to assess how the STONP represents the island’s avifauna. We will start by
modelling bird species richness and composition, in order to capture its spatial patterns, while paying
special attention to the distribution of endemic and non-endemic species. Then, we will compare the
distribution of bird diversity with the boundaries of the STONP to assess if the protected area includes
an adequate representation of the multiple aspects of the island’s bird diversity.
METHODS
Study Area
São Tomé Island is in the Gulf of Guinea, Central Africa, and together with Príncipe Island forms
the Democratic Republic of São Tomé and Príncipe. It is a small oceanic island, lying just north of the
Equator and about 255 km west of Gabon. Covering only 857 km2, it has an incredible unique avifauna
(Peet & Atkinson 1994; Leventis & Olmos 2009): out of 45 resident terrestrial species, 17 are single-
island endemics, 3 are endemic to the Gulf of the Guinea oceanic islands (Annobón, São Tomé and
Príncipe) and 8 are endemic subspecies of widespread species (Jones & Tye 2006). The high endemism
results from the island’s location relative to the African continent: close enough to allow frequent
migration, but far enough to allow speciation by isolation (Melo 2006).
The mountainous topography is responsible for strong environmental gradients, which still shape
the distribution of natural and anthropogenic ecosystems. The island was almost entirely covered by
forest when Portuguese navigators first discovered it and started its occupation in the late 15th century.
Nowadays, most lowland areas and some montane regions have been converted to plantations, while the
best-preserved patches of native forest occur mostly in the rugged rainy areas in the south-west and
centre of the island. This forest is surrounded by large extents of secondary forest, which result mostly
from agricultural abandonment and logging activities. This forest is in turn enclosed by active shade
plantations of coffee and cocoa, mixed with non-forested areas, such as oil palm monocultures,
horticultures and savannahs (Exell 1944; Tenreiro 1961; Jones et al. 1991).
Despite the increasingly humanized landscape, São Tomé is still dominated by forested ecosystems.
The native forest is almost entirely included in the STONP, which covers approximately one third of
the island (Albuquerque et al. 2008). There is growing awareness at local and international levels about
22
the value of the unique biodiversity of the island, and about the urgent need for effective conservation
efforts. However, human pressure on natural resources is increasing fast, as shown by the decreasing
area of native forest (Salgueiro & Carvalho 2001), and much conservation work is still needed.
Data Compilation
We gathered systematic observations of São Tomé bird species obtained in 300 point counts, during
2009 and 2010 (de Lima 2012) and in 1653 point counts sampled between 2013 and 2015 (BirdLife
International São Tomé and Príncipe Initiative – BISTPI) (de Lima et al. 2017).
All records were obtained during a 10-minute sampling sessions, in which an experienced observer
recorded all birds detected aurally and visually, regardless of distance. A minimum distance of 200
meters was kept between point counts. All information was compiled in a bird species occurrence
database, which included a GIS component.
Field Methods
Sampling design
To identify under-sampled areas in previous studies from which we compiled data, we over-
imposed the bird occurrence database on the map of São Tomé Island, which was divided in 2x2 km
quadrats. Each of these quadrats was subdivided in four 1x1 km quadrats, following the BISTPI
methodology (de Lima et al. 2017).
We eliminated all 2x2 km quadrats that had more than half of their area occupied by the ocean, and
identified all quadrats that had at least five point counts sampled. The remaining 96 2x2 km quadrats,
located mostly in non-forested low-altitude areas, were identified for surveying. Subsequently, we
randomly ranked each of the 1x1 km quadrats in each of the larger quadrats for sampling, to determine
sampling priority (de Lima et al. 2017).
Bird sampling
To complement the previously compiled bird database, we sampled 91 out of the previously
identified 96 quadrats, between January and March 2017.
Following previous work (de Lima 2012; de Lima et al. 2017), the quadrats were sampled by
conducting five 10-minute point counts. The points were at least 200 meters apart, to ensure
independence and that the environmental variability inside each quadrat was sampled in the proportion
that they occurred within the quadrat. In each point count, we registered all bird species detected visually
and aurally. To maximize the number of sampled points during our short sampling period, counts were
made throughout the day.
All bird data were compiled in a database totalling 263 1x1 km quadrats. All species that are aquatic
or difficult to identify were excluded (Table S9), leaving a total of 36 species for the analyses. Point
counts with zero presences for these species or with inconsistencies between the field land cover
classification and the 2014 land use map were also excluded. Only five point counts in each sampled
quadrat were considered to ensure a balanced sampling effort between quadrats and a good spatial
distribution of sampling effort throughout the year (n = 187; Fig. 2.1). For each 1x1 km quadrat, an
average point count was calculated based on the average of the coordinates of all five point counts. All
bird species records found in each five point counts were considered for the average point count and
later transformed into presence/absence data. Total species richness, endemic species richness and non-
endemic species richness was calculated for every average point count.
23
Characterizing environmental variables
To model and map species richness and compositional dissimilarity, we assembled geographically
explicit information on altitude, ruggedness, slope, distance to the coast, topography, remoteness,
rainfall and land use across São Tomé, using Quantum GIS v. 2.8.3 and v. 2.14.8 (Quantum GIS
Development Team 2009a; Table S1 & S2).
The variable altitude was derived from a 90 meters resolution Digital Elevation Model (DEM)
(Salgueiro & Carvalho 2001; NASA Jet Propulsion Laboratory 2016; Fig. S1). Ruggedness and slope
were also calculated from this DEM raster, using the “raster terrain analysis” QGIS plugin (Quantum
GIS Development Team 2009b; Fig. S2 & S3). The slope was initially calculated in decimal degrees
and then transformed to percentage. Distance to the coast was calculated as the minimum linear distance
in decimal degrees between each pixel and the nearest coast line point, using the DEM and the QGIS
“distance matrix” tool (Quantum GIS Development Team 2009a; Fig. S4). Topography was represented
using a Topography Position Index (TPI) which allows comparing of each cell’s elevation to the mean
elevation of a specified neighbourhood (Jenness 2007). TPI was calculated using the DEM and the
“topography position index” tool in the QGIS GDAL algorithm provider (Quantum GIS Development
Team 2009c) and a 0.05º radius neighbourhood, which allows for a good representation of terrain
ruggedness and elevation in São Tomé. The continuous TPI thus obtained was transformed in a five-
category discrete variable: flat areas (1), valleys (2), middle slopes (3), upper slopes (4) and ridges (5)
(Fig. S5, S6 & S7). Still, given the nature of further analyses, the TPI variable was considered
Figure 2.1. São Tomé Island sampling locations. The lines in the background
represent the 100 m elevation isolines. Each dot corresponds to the average point
count for every 1x1 km quadrat sampled (n = 187).
24
continuous, reflecting an altitudinal gradient with 2 being lower than the referential flat areas (1) and 5
the highest situation. Remoteness is expressed as an index that translates the difficulty of movement
through the landscape, and it was created using the “accumulated cost” QGIS GDAL algorithm provider
(Quantum GIS Development Team 2009d). This index is a cost accumulated surface based on a friction
surface derived from slope and weighted by the human population density (Tobler 1993; Instituto
Nacional de Estatística 2016; Fig. S8 & S9). Rainfall was obtained by digitizing the island’s mean annual
precipitation map in millimetres (Silva 1958; Fig. S10). The land use map was created based on 2014
satellite images (Google Earth 2017), supplemented by 2009-2017 field information (de Lima 2012; de
Lima et al. 2017), 1970 land use map (de Carvalho Rodrigues 1974), military maps (Missão Hidrográfica
de Angola e S. Tomé 1958), a 2011-13 preliminary land use map (S. Mikulane, unpublished data - see
Fig. S11) and expert knowledge (Fig. S12). First, it was considered a four-category discrete variable:
native forest (1), secondary forest (2), shade plantations (3) and non-forested areas (4). Later, this same
variable was transformed in a continuous variable reflecting a gradient of habitat degradation: 1 being
the pristine habitat and 4 the habitat with highest level of humanization.
All variables were considered continuous and standardised to a common in raster grid, using the
nearest neighbour sampling method and the TPI raster as a reference. This standardization was made
using QGIS “align rasters” tool (Quantum GIS Development Team 2009a), and resulted in a pixel’s size
of 0.000833º x 0.000833º and a raster with 359 x 471 cells.
The average point count of each 1x1 km quadrat was characterized for each environmental variable
using the “point sampling tool” QGIS plugin (Quantum GIS Development Team 2009e).
Data Analysis
All statistical analyses were made using R v. 3.3.2 in RStudio v. 1.0.143 (R Development Core
Team 2017).
Exploratory analysis
Multicollinearity was tested using Spearman’s rank correlation coefficient, and visualized in a
correlogram built using the “corrgram” package (Wright 2016; Part I, Section VIII). Remoteness index
and ruggedness were excluded from further analyses, having correlation coefficients with land use and
slope, respectively, equal to or higher than 0.8 (Fig. S17). To identify potential outliers and analyse
variance homogeneity, boxplots were drawn for each environmental variable, using the “vegan” package
(Oksanen 2015). No outliers were removed from the analysis. Under-dispersion was tested for species
richness, endemic species richness and non-endemic species richness, using the “AER” package
(Kleiber & Zeileis 2017). The data were divided into a training and a testing set, using the “caTools”
package (Tuszynski 2014): 70% of the quadrats were used to create the models and 30% to validate
them.
Generalized linear models
Three generalized linear models (GLMs) with poisson distribution were created to explain total,
endemic and non-endemic species richness, respectively (Part III, Section VIII).
For each GLM, all possible combinations of explanatory variables were ranked based on the Akaike
Information Criterion corrected for small sample sizes (AICc), using the “dredge” function from
“MuMIn” package (Barton 2016). The models were validated using the testing data. Goodness of fit
was analysed with the McFadden’s index in the “pscl” package and with the Residual Deviance
(Jackman et al. 2015). Validation was also explored by plotting the Pearson and Deviance residuals
25
against the predicted values, using the “stats” package (R Development Core Team 2017; Table S10 &
Fig. S18).
To identify which variables best explain the species richness models, we ran the “model averaging”
function from the “MuMIn” package to obtain relative variable importance (RVI). To evaluate the
response of total, endemic and non-endemic species richness to each continuous variables, we calculated
the Spearman’s rank correlation coefficient (Table S11). Finally, a map with predictions from each of
the three fitted models was generated, using the “raster” package and the environmental variables in
raster format (Hijmans et al. 2016).
Generalized dissimilarity modelling
Generalized dissimilarity modelling (GDM) was used to map beta diversity using the “gdm”
package (Manion et al. 2017; Part IV, Section VIII). GDM compares community composition and
environmental variables at pairs of sites to predict compositional difference as a function of
environmental difference, extrapolating the prediction beyond surveyed sites. The resulting models give
a spatially continuous prediction of turnover, and thus of the spatial structure of diversity.
To quantify the compositional dissimilarity between different sites, a dissimilarity matrix was
calculated using the Bray–Curtis dissimilarity statistics. The model fit was examined by the total
deviance explained by the model and by plotting the observed dissimilarities against the predicted values
(Fig. S19). To assess the model significance of each variable a significance test was made using 100
permutations. The significance testing in the “gdm” package is still in the early phase of development,
and it is therefore rather computationally intensive. The variable importance was measured as the
percent change in deviance explained by the full model and the deviance explained by a model fit with
that variable permuted. The significance was estimated using the bootstrapped p-value when the variable
was permuted (Table S12 & S13).
A robust assessment of model’s capacity to generate predictions was made by validating the
independent testing set. A k-fold cross-validation was used to test the predictive accuracy of the model,
using 100 permutations. The output of the cross-validation was the correlation between the observed
and predicted compositional dissimilarities, for the testing set of sites (Fig. S20).
To generate spatially explicit GDM model predictions for São Tomé Island, we created transformed
environmental layers for each predictor using the spline functions from the fitted model. A principal
components analysis (PCA) was made on the dissimilarities between classes to reduce dimensionality
and assign the first three components to an RGB colour palette (red, green and blue). This way, similar
colours represent a similar avifauna composition. The output was a raster image composed of three
single rasters representing the three ordination axes.
The relative importance of each predictor variable was determined by summing the coefficients of
the I-splines from the fitted generalized dissimilarity model (Table S14). The response curves were used
to evaluate the response of predicted compositional dissimilarity to each predictor variable (Fig. S21).
Generalized dissimilarity model categorization
An unsupervised classification method was applied to the continuous GDM, using modified k-
means classification in the Whitebox Geospatial Analysis Tools v. 3.4.0 “Image Classification” menu
(Lindsay 2016; Fuss et al. 2016).
The algorithm was limited to Euclidian distances smaller than 75, a value that ensured the creation
of robust composition categories. We allowed for a maximum of 50 iterations, a 2% pixel class change
threshold and a 500 minimum number of pixels per class. The initial cluster centres were generated
randomly.
26
Assessing the adequacy of the Obô Natural Park to represent São Tomé bird diversity
We assessed how the STONP represented two distinct aspects of the island’s bird diversity: species
richness and composition. To explore the differences in species richness inside and outside the STONP,
we used the “random points” QGIS tool in “vector” menu (Quantum GIS Development Team 2009a) to
sample 3996 random points from the total, endemic and non-endemic species richness maps previously
created.
Total and endemic species richness were calculated for each GDM class. Total and endemic
average species richness were calculated for each quadrat. These were used to calculate the proportion
of endemic species (number of endemic species / total number of species) and the frequency of endemic
species (number of endemic species detections / total number of detections) for each quadrat. Both
median and quartiles were plotted in a single scatterplot to explore the relation between endemic species
proportion and detection rate (Part V, Section VIII). Finally, the percentage of each GDM classes
included inside the STONP was calculated, using “count raster cells” QGIS plugin.
RESULTS
Bird data used to map total, endemic and non-endemic species richness was under dispersed (total:
z = -14.375, p = 2.2 x 10-16; endemic: z = -13.498, p = 2.2 x 10-16; non-endemic: z = -15.311, p = 2.2 x
10-16).
Modelling bird species richness
Total species richness was highest in the centre south of São Tomé Island. These particularly rich
areas were located inside the STONP. Some of the poorest areas were also found inside the park,
coinciding with higher altitudes and steeper slopes (Fig. 2.2). Endemic species richness pattern was
clear: richer areas located inside the protected area with the number of species declining with greater
proximity to the coast. Non-endemic species followed the opposite pattern: areas with a lower number
of species were found inside the park which progressively increased with humanization and towards the
coast.
In the south-east of the island, an area can be identified in all three predictive maps, characterized
by a smaller number of species than surrounding areas, and it corresponds to a large oil palm plantation.
In the total species richness model, none of the environmental variables was statistically significant
and relative variable importance (RVI) was always smaller than 0.50. To model endemic species
richness, land use was the most important variable. On the other hand, several environmental variables
were significant and important to the distribution of non-endemic species richness. The most important
variable was rainfall, followed by altitude and land use.
Endemic species responded negatively to more intensive land uses, but positively to forested
habitats, like native and secondary forests. Whereas non-endemic species had an opposite response and
therefore a strong connection to non-forested habitats and humanized landscapes (Table S11).
27
Figure 2.2. Predictive maps of (a) total species richness, (b) endemic species richness and (c) non-endemic species richness,
shown in contrast to the boundaries of the Obô Natural Park and buffer zone.
28
Bird species compositional dissimilarity
Generalized dissimilarity modelling (GDM) was used to identify areas with similar avifauna
composition (Fig. 2.3). The GDM allowed explaining 43.6% of the deviance. The most important
environmental predictor was land use, followed by rainfall and altitude (Table S14). A larger rate of
species turnover was found for high values of land use, meaning that the biggest changes in bird
community composition occurred in humanized habitats, like shade plantations and non-forested areas.
In forested habitats, the species composition was similar (Fig. S21). Smaller values were associated to
bigger species composition turnover rates for slope, altitude, rainfall and TPI.
Figure 2.3. (a) Continuous and (b) categorical composition dissimilarity maps, as obtained from generalized dissimilarity
modelling (GDM). (c) Links and (d) distances between the five GDM classes obtained using modified k-means
classification. Class 1 corresponds to the large oil palm monoculture, class 2 to the open areas surrounded by agro-forest
habitats in slightly wetter regions, class 3 to the most humanized habitats in the driest parts of the island, class 4 to mixed
of forested habitats like shade plantations and secondary forest in the north-east and class 5 to secondary and native
forests in the centre and south
Cla
ss
Euclidean distance
0.0 100.0 200.0
d)
c)
2
1
5
4
3
29
From the continuous GDM, a categorical map was produced and five classes were identified. The
first class to be separated was class 1, suggesting the existence of a very distinctive bird species
assemblage in the oil palm plantation, previously identified in all species richness maps (Fig. 2.3).
Subsequently, there was also an obvious separation between bird assemblages that inhabit more
forested habitats (classes 5 and 4) and those living in non-forested areas (classes 2 and 3).
Is the São Tomé Obô Natural Park adequate to protect the island’s avifauna?
Of the 3996 random points generated to assess the number of species, both total, endemic and non-
endemic, 1097 were located in the park. The predicted number of species was similar inside and outside
Figure 2.4. Total, endemic and non-endemic species richness inside (In) and outside (Out) Obô Natural Park. The boxplots
represent the median (thick line), the first and third quartiles (box), the extremes (whiskers) and the outliers (dots).
30
STONP (inside STONP: x̄ = 13.6; outside STONP: x̄ = 13.8). Even so, a bigger range of values was
found inside the park (Fig. 2.4), suggesting a wider variety of areas inside the protected area.
Endemic species richness had higher values inside the STONP (inside STONP: x̄ = 11.4; outside
STONP: x̄ = 9.3), while non-endemic species richness presented the opposite pattern (inside STONP: x̄
= 2.3; outside STONP: x̄ = 4.5).
There were no major differences in average species richness between all five GDM classes (Table
2.1). However, there were several differences in average endemic species richness: class 3 showing the
lowest value (4.9), followed by classes 1 (8.5) and 2 (9), and the remaining having similar values. There
were also differences in terms of total number of species and total number of endemic species. Classes
1 and 2 had identical values, namely the lowest total number of species (20) and an intermediate total
number of endemics (12). Class 3 had an intermediate total number of species (23), but the lowest total
number of endemics (8). Classes 4 and 5 had the highest total number of species (28), but class 5 had a
higher total number of endemics (19 against 15). The class 5 of the GDM had by far the largest area
included inside the STONP, having more than half of its area protected (54.9%). The remaining four
classes had only 3% or less of their area protected.
Table 2.1. Species richness and endemic species richness estimated for each average point inside 1x1 quadrats,
called, respectively, species and endemic richness point estimate. Species richness and endemic species richness
calculated for each GDM class (1 to 5). Percentage of class included in Obô Natural Park.
Class
1 2 3 4 5
Endemic richness point estimate 8.5 9 4.9 10.1 10.7
Species richness point estimate 13.3 12.7 13.2 13.5 14.2
Endemic species richness 12 12 8 15 19
Species richness 20 20 23 28 28
% Class Protected 1.9 1.9 2.7 3.1 54.9
Figure 2.5. Proportion of endemic species and frequency of endemic species for each GDM class (1 to 5). The bars
represent the first and third quartiles of the median values estimated for each quadrat.
3
1
2
4
5
31
Regarding endemic species, four different groups of classes can be found (Fig. 2.5): class 3 was
clearly distinct from other classes, by having fewer endemic species; classes 1 and 2 had identical
intermediate proportions and frequencies of endemic species; class 4 had a proportion of endemics only
slightly higher than the previous group, but significantly higher frequencies; class 5 had the highest
proportion and frequency of endemics.
DISCUSSION
We modelled and mapped bird species richness and composition to understand if the STONP
represented the island’s bird diversity. We found that the STONP did not protect necessarily the richest
assemblages, but did protect those that were richest in endemic species.
Contrasting responses of endemic and non-endemic species to the environment
Bird species richness presented a narrow gradient that was not evenly distributed throughout São
Tomé and a complex pattern (Fig. 2.2 a), which was not strongly related to any of the environmental
variables used in the modelling. The STONP included the richest, but also the poorest areas for avifauna
in the island. The highest values of species richness were found in the centre-south of the island, inside
native forest, and were almost entirely included in the STONP. Right next to them, two large species-
poor areas can be identified, also mostly included inside the park: the São Tomé Peak and surrounding
high altitude areas, and the Cabumbé Peak and the nearby Quija and Xufexufe river valleys. Both are
located in the heart of São Tomé’s rainforest, and represent remote mountainous landscapes.
Endemic species richness was clearly associated with the best-preserved forest in São Tomé (Fig.
2.2 b). This result coincides with previous findings, indicating that endemic species are associated with
forest-dominated habitats and avoid humanized landscapes (de Lima et al. 2012). The highest values of
endemic species richness also tended to occur further away from the coast line. These endemic-rich
forests were almost entirely inside STONP (Albuquerque et al. 2008). Secondary forests are found
mostly around native forests, both inside the STONP and in the buffer zone. Although they shelter less
endemic species than native forests, they seem to be acting as a transition zone to more humanized areas
(Atkinson et al. 1991), protecting the STONP from pervasive threats (Dallimer et al. 2009).
The greatest number of non-endemic species was found in the more humanized habitats near the
coast in the north-east of the island (Fig. 2.2 c). A pattern that is rather contrasting to that of the endemic
species richness. The northern exclave of the STONP is the only protected area including areas rich in
non-endemic bird species. Most of these species are small granivores assumed to have been introduced
to the island, well-known for being associated with non-forested areas under strong anthropogenic
influence (Jones & Tye 2006). Since non-endemic birds tend to avoid forested areas and to use distinct
food resources, they do not seem to be competing with the endemics. Instead, the gradient between
endemic and non-endemic dominated bird assemblages seems to be facilitated by the gradient of native
forest degradation (Didham et al. 2007).
Comparing the distribution of total, endemic and non-endemic species richness (Fig. 2.2), a distinct
area can be seen in all maps, located in the south-east of the island. This area corresponds to a large oil
palm plantation, characterized by having few bird species, and notably fewer endemics and,
proportionally, more non-endemics than the surrounding landscape. Most endemics rely on complex
forest environments and do not find the required resources to subsist in these monocultures (Turner et
al. 2008; Nájera & Simonetti 2010). Being mostly granivores, the non-endemics also struggle to persist
in these plantations, due to the severely impoverished vegetation. Moreover, the extremely wet
conditions are not favourable to the production of grains on which they often rely. Studies suggest that
32
the spontaneous development of understory vegetation should be allowed in these oil palm plantations,
to function as corridors between natural ecosystems and to promote the appearance of a more varied
avifauna, namely of insectivore birds that contribute to pest control (Savilaakso et al. 2014).
The maps of total, endemic and non-endemic species richness (Fig. 2.2) also show that an apparent
lack of overall obvious pattern in total bird species richness is concealed by the contrasting distribution
patterns of endemic and non-endemic species richness. Furthermore, the contrasting response of
endemic and non-endemic species richness to land use, also obscures the importance of this
environmental variable in explaining patterns of bird diversity in São Tomé (de Lima et al. 2012; Table
S11).
Species assemblages vary mostly in response to habitat humanization
Modelling species composition dissimilarity revealed that bird assemblages were strongly
determined by the same humanization gradient that had been identified when analysing species richness:
the bird community associated with lowland humanized areas changes progressively towards the forest-
dominated landscapes, culminating in the native forest (Fig. 2.3 a). This pattern can be seen in the north
and south regions of the island, both of which hold rather distinctive species assemblages linked to a
wide rainfall gradient. The large oil palm plantation in the south-east once more reveals a very distinctive
species assemblages.
Land use was again considered the most important variable, followed by rainfall and altitude, which
are also intrinsically linked to the distribution of land use in São Tomé Island (Peet & Atkinson 1994;
Table S14). The analyses showed a bigger species composition turnover within non-forested habitats
(Fig. 2.3 c & S21). Composition response curve to land use suggested that bird assemblages were more
distinct within humanized than in natural habitats, as already indicated by previous studies (de Lima et
al. 2012). This pattern has been associated with stronger differences between intensive agricultural areas,
holding a simplified vegetation, compared to natural ecosystems (Waltert et al. 2004; Rocha 2006).
The categorical GDM (Fig. 2.3 b & 2.3 c) separates forested habitats (classes 4 and 5) and non-
forested habitats (classes 1, 2 and 3), further supporting that land use is vital in the differentiation of
bird species assemblages (de Lima et al. 2014). Class 1 represents the most distinctive bird community
to be isolated, and corresponds to the large oil palm plantation already identified in the species richness
maps. Although located in the south, where rainfall is much higher, this class is closer to classes 2 and
3, all of which corresponding to non-forested habitats, where the non-endemic species prevail.
Classes 2 and 3 represent lowland non-forested areas, where non-endemic species are frequent.
Class 3 includes the most humanized habitats, in the driest parts of the island, while the similar class 2
appears in open areas surrounded by agro-forest habitats, in slightly wetter regions.
Class 4 is a mixed of forested habitats like shade plantations and secondary forest in the north-east
of São Tomé. Class 5 covers secondary and native forests in the centre and south, and holds without a
doubt the community with the highest proportion of endemic species.
There is an obvious species turnover from forests, where the endemics are clearly dominant, to
more open habitats, where non-endemics become more numerous (Lima et al. 2012). Most non-
endemics are small granivores, believed to have been introduced (Leventis & Olmos 2009), which
suggests that land use change might be promoting the spread of non-native species (Rocha 2006). Islands
are known to have a limited pool of species available to colonize disturbed areas (Atkinsons et al. 1991).
In São Tomé, urban and non-forested agricultural fields are the most transformed and humanized areas.
They have been widely colonized by introduced granivore species, since these are better adapted to non-
forested habitats than the native, mostly endemic avifauna (de Lima et al. 2012; Ndang’ang’a et al.
2014). On the other hand, the introduced granivores seem to be much less frequent in forested habitats,
including the cocoa and coffee shade plantations, even though the vegetation of these agroforestry
33
systems is almost exclusively composed by introduced plant species (de Lima et al. 2014). Our results
seem to provide further support for the hypothesis that the landscape being dominated by forested
habitats is involved in maintaining and ensuring the overall dominance of the endemic avifauna (de
Lima et al. 2012, 2017).
Other factors, such as hunting and the introduction of non-avian forest species might be affecting
the avifauna. Hunting has been shown to affect the distribution of birds, and notably large frugivores
(Carvalho et al. 2015a; Carvalho et al. 2015b). The introduction of non-avian vertebrates, such as feral
pigs Sus domesticus and cats Felis catus, rats Rattus sp., and the mona monkeys Cercopithecus mona,
have also been implied in having negative impacts in the endemic-rich native avifauna, namely through
predation and habitat changes (Atkinson et al. 1991; Dutton 1994). Despite little empirical evidence,
both of these threats, are linked to the land use degradation gradient, which is, without a doubt the key
determinant of bird diversity in São Tomé.
Is the São Tomé Obô Natural Park adequate to protect the island’s bird diversity?
The boundaries of the STONP were established, mostly based in a habitat field survey, and our
work represents the first assessment of its adequacy to protect the island’s biodiversity. To do so, we
evaluated if bird species richness and assemblage composition was well represented within the
boundaries of the protected area, paying special attention to the endemic and non-endemic components
of avifauna.
The STONP covered some of the highest values of total species richness, but also some of the
lowest, resulting in no significant differences when compared with areas outside the park (Fig. 2.4).
However, endemic species richness was clearly higher inside the STONP, and non-endemic richness
higher outside. These results show that using species richness on its own can be misleading as an
indicator of conservation value and that it should be used in combination with other metrics (Le Saout
et al. 2013). These results are also encouraging, since the park limits seem to be well established for the
protection of the endemic species, which are the most threatened (IUCN 2017) and the most interesting
species, in terms of global conservation goals (de Lima et al. 2017).
The STONP is almost entirely composed by areas covering the class 5 we identified by GDM,
which represents the richest bird assemblage, having the highest number of species and being mostly
composed of endemic (Atkinson et al. 1991; Fig. 2.5). This class includes almost all native forest and is
the bird assemblage best represented inside the STONP (54.9%; Table 2.1). All other classes have a
poor representation inside the protected area, regardless of how many endemics they hold. This is of
little concern in terms of global species protection, since all endemic and threatened species are included
in class 5.
Final remarks
The STONP did not represent the diversity of São Tomé avifauna very well, but focused on the
endemic and threatened species, arguably those of higher global conservation interest.
The boundaries of the STONP were primarily defined based on native forest distribution, natural
barriers and small levels of human pressure, but coincide with the distribution of the bird assemblages
that are richest in endemics (Albuquerque et al. 2008). This match is due to the key determinants of bird
diversity patterns being the same environmental factors that were used to define STONP boundaries
(Rocha 2006; de Lima et al. 2012; Chapter I).
Our work also highlighted the importance of secondary forests for the avifauna of São Tomé,
holding a high proportion of endemic species and providing a valuable buffer zone for many of the
small-ranged endemics. Therefore helping to mitigate many negative impacts of human activities, like
hunting and logging (Atkinson et al. 1991; de Lima et al. 2017).
34
We advocate STONP zonation should be revised, taking into account the same factors used to
define the boundaries of the protected area, and also the current knowledge about bird species
distribution, especially that of those of higher conservation interest. This way, key STONP will gain a
higher level of protection, contributing to the conservation of threatened small-ranged endemic species,
like the Dwarf Ibis Bostrychia bocagei (Dallimer et al. 2009; Leventis & Olmos 2009; Ndang’ang’a et
al. 2014; de Lima et al. 2017).
At last, STONP provides a good example that areas of higher conservation interest can be identified
using the distribution of natural habitats and human population. Protected areas should prioritize natural
ecosystems supporting high species richness and high proportions of endemic and threatened species.
However, in most cases this information is not available when the boundaries are being defined. Our
results suggest focusing first on identifying key natural ecosystems, and then zoning based on the
distribution of the different biodiversity components, when these become better known, eventually
extending the initial boundaries. This strategy allows for assessing if protected areas are still achieving
their key conservation goals, and adjust them while knowledge on their biodiversity increases.
35
FINAL CONSIDERATIONS
Anthropogenic land use change is considered the biggest threat to global biodiversity (Foley et al.
2005; Stork 2009; Szabo et al. 2012). Understanding how human actions affect biodiversity is therefore a
first step to minimize and prevent further impacts on species and ecosystems. Human population is expected to grow
exponentially within upcoming years, making it crucial to learn how to coexist and share world ecosystems and
natural resources (Cincotta et al. 2000; Luck 2007). Our study exemplifies how human occupation can
influence species distribution.
São Tomé is a small, highly forested island with strong natural and anthropogenic gradients
(Salgueiro & Carvalho 2001; Jones & Tye 2006), both of which shape the distribution of species and
ecosystems. We have shown that the strong gradient of land use intensification is the main responsible
for the changes found in bird species assemblages: from forest endemic species, extremely associated
with best-preserved forests, to open habitat non-endemic species, commonly found in more intensively
managed land uses (Rocha 2008; de Lima et al. 2012). In São Tomé, the forest-dominated landscape
ensures and maintains the overall dominance of endemic avifauna (de Lima et al. 2012). Given these
results, forested patches are vital for the persistence of endemic birds inside a landscape increasingly
dominated by intensive land uses. Thus, we recommend the protection of the remaining native forest
and the expansion or improvement of secondary forest, to provide a landscape matrix more suitable for
the endemic species. Non-native birds will have the opposite response, since they tend to avoid forested
habitats (Atkinson et al. 1991). Therefore, increasing the forest cover will have the additional benefit of
preventing the spread of introduced birds throughout the island.
The establishment of protected areas is one the most important and common conservation measures
(Myers et al. 2000; Buchanan et al. 2011; Le Saout et al. 2013). The STONP was created based on native
forest distribution, natural barriers and small levels of human pressure (Albuquerque et al. 2008). With
our study, we concluded that most of the areas with endemic-rich assemblages are well represented
inside the park, even though these do not necessarily correspond to the richest bird assemblages. Many
endemic species are considered threatened and their reliance on forested habitats is a growing concern,
given the increasing destruction and degradation of São Tomé native forests (Ndang’ang’a et al. 2014;
IUCN 2017). The conservation of São Tomé endemic bird species relies on the protection and
preservation of the remaining native forest. Given the limited resources in São Tomé, STONP is not
receiving the active conservation management required, also because most environmental laws do not
come with legal force (Albuquerque et al. 2008). We emphasize the need to transform these
environmental laws into active conservation actions on the field, setting up a monitoring program to
stop or at least minimize the still ongoing threats inside and nearby the park, e.g. uncontrolled hunting,
forest burning and intensive logging (Peet & Atkinson 1994; Dallimer et al. 2009; Carvalho et al. 2015a;
Carvalho et al. 2015b; de Lima et al. 2017). The expansion and management of secondary forests for
conservation could improve the quality of ecosystems in the STONP buffer zone, which has an important
role in the conservation of endemic bird species, helping to minimize possible human impacts inside the
park and surrounding areas, while providing additional habitat to many of the endemics.
The current study is an important basis for future studies, and to establish specific monitoring
activities and conservation strategies. However, further research is needed to gain a more detailed
knowledge about the distribution of each bird species, namely regarding seasonality and single species
response to forest degradation. That information would enable us to define target species actions, more
adequate to each species ecological requirements, which is especially important for the most threatened,
such as the Dwarf Ibis, the São Tomé Fiscal and the São Tomé Grosbeak. We also highlight the need to
gain a better understanding of the impact of other threats, such as hunting and introduced species.
36
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Huerta MA, Peterson AT, Phillips OL, Williams SE. 2004. Extinction risk from climate change. Nature
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Thuiller W, Araújo MB, Lavorel S. 2004. Do we need land-cover data to model species distributions in
Europe? Journal of Biogeography 31:353–361.
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Biodiversity Assessment. PLoS ONE (e1572) DOI:10.1371/journal.pone.0001572
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44
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45
SUPPLEMENTARY MATERIALS
SECTION I: Environmental Variables
Table S1. Environmental variables description. List of environmental variables used to model each species potential
distribution, species richness and species compositional dissimilarity. All variables were built in Quantum Gis program.
Variables Description Type Units
Altitude
Digital Elevation Model based on
NASA's Shuttle Radar Topography
Mission (SRTM) with 90 meters of
horizontal resolution
Continuous Meters
Topography Position
Index
Index representing the position of
each pixel regarding the mean
elevation of a neighbourhood within
a 0.05º radius
(Fig. S5 & S6)
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 with 90
meters of resolution
Continuous -
Slope Slope calculated from the Digital
Elevation Model Continuous Decimal Degrees
Land use
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
Mean Annual
Precipitation
Vectorised map obtained from a
map with 30 years compiled data
throughout the island, 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
Continuous Decimal Degrees
Remoteness Index
Cost accumulated surface created
with a friction surface derived from
slope and weighted by the
population density
Continuous -
46
Table S2. Environmental raster’s characteristics. All variables are in raster format and projected in the same coordinate
reference system, WGS 84 (EPSG 4326). Pixel size is 0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
Variable Minimum value Mean value Maximum value
Altitude 1 345.372 1962
Topography Position Index 1 - 5
Ruggedness 1.414 67.647 451.537
Slope 0 14.383 65.093
Land use 1 - 4
Mean Annual Precipitation 700 3133.940 7000
Distance to Coast 1.606 x 10-5 0.040 0.114
Remoteness Index 0 2.042 6.138
47
Figure S1. Altitude in meters. Altitude is projected in WGS 84 (EPSG 4326). Pixel size is 0.000833º
x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
48
Figure S2. Ruggedness. Ruggedness is projected in WGS 84 (EPSG 4326). Pixel size is
0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
49
Figure S3. Slope in degrees. Slope is projected in WGS 84 (EPSG 4326). Pixel size is
0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
50
Figure S4. Distance to coast line in degrees. Distance to coast is projected in WGS84 (EPSG
4326). Pixel size is 0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
51
x
TPI continuous
Mask TPI 0/1
TPI < 0.5
Mask TPI 0/1
TPI > -0.5
Mask TPI 0/1
-0.5 < TPI < 0.5
Mask Slope 0/1
Slope > 5º
-0.5 < TPI < 0.5
Slope > 5º
Middle Slope Areas
-0.5 < TPI < 0.5
Slope <= 5º
Flat Plain Areas
TPI continuous
-0.5 < TPI < 0.5
Slope continuous Mask Slope 0/1
Slope <= 5º
x TPI continuous
Figure S5. Separation of flat plain areas and middle slope areas. Both flat and middle slope areas have
topography index values comprised between -0.5 and 0.5. Flat areas are characterized by having slope values
smaller or equal to 5º degrees and middle slope areas values bigger than 5º degrees. These rasters are
projected in WGS 84 (EPSG 4326), have a pixel size of 0.000833º x 0.000833º and dimensions of 471 x
359 cells (rows x columns).
52
Figure S6. Transforming continuous Topographic Position Index in a categorical variable. Continuous TPI was transformed to take only positive values before categorization. Flat plain
areas were then combined with the categorical topography index to separate flat areas from
middle slope areas. Valleys and deep valleys were joint together to form a more representative
class. Topography was reclassified so flat areas were considered the reference class with a value
of 1.
+ 10
Mask 0/1
Flat Plain Areas
TPI categorical
3 to 7.5 → Class 6
7.4 to 9.5 → Class 5
9.5 to 10.5 → Class 3
10.5 to 12.5 → Class 2
12.5 to 18 → Class 1
-0.5 < TPI < 0.5
Slope <= 5º
Flat Plain Areas
TPI continuous
x 4
Mask 0/4
Flat Plain Areas
Sum of class 5 and 6
Reclassification
+
TPI categorical
Class 1 (Ridges)
Class 2 (Upper Slope)
Class 3 (Middle Slope)
Class 7 → Class 4 (Flat Plain Areas)
Class 5 (Valleys)
Class 6 (Deep Valleys)
TPI categorical
Class 1 → Class 5 (Ridges)
Class 2 → Class 4 (Upper Slope)
Class 3 → Class 3 (Middle Slope)
Class 4 → Class 1 (Flat Plain Areas)
Class 5 and 6 → Class 2 (Valleys and Deep
Valleys)
53
Figure S7. Topography Position Index. TPI is projected in WGS 84 (EPSG 4326). Pixel
size is 0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
54
Figure S8. Building remoteness index. Slope in percentage was used as a base raster for the calculation of
remoteness index. The Tobler Hiking function was applied to slope raster (%) resulting in a friction surface.
This friction surface was reversed to give larger values to remote areas and combined with a road map. A
human population density raster based on a kernel density filter applied to 2001 localities was used to weight
the cost accumulated surface. A logarithmic transformation was used to get a better representation of reality. 1To avoid the division of the cost accumulated raster by extremely low values. 2To avoid concentration of
difficult access only inside the island, having low resolution in the areas outside the centre.
Cost Accumulated
Raster weighted by
Population Density
Ln Transformation
1Reclassify to 1 all the
values < 1
Slope (%)
Applying Tobler Hiking Function
6^ (-3.5*(Slope (%)/100+0.05))
Friction Surface
Reversed Friction Surface
Reverse
Cost Accumulated Function
Cost Accumulated Raster
Target: Road Map
Friction Surface: Reversed
Friction Surface
Road Map
Division
Remoteness Index
2(Log Transformation) + 1
Human Population
Density
(5 km radius)
55
Figure S9. Remoteness Index. Remoteness index is projected in WGS 84 (EPSG 4326). Pixel size
is 0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
56
Figure S10. Rainfall in millimetres. Rainfall is projected in WGS 84 (EPSG 4326). Pixel size is
0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).
57
Figure S11. Land use map created by S. Mikulane (resolution of 10x10 meters). Map obtained from the following PhD dissertation “Degradationsrisiken tropischer
Waldökosysteme - Modellierung der Landschaftsvulnerabilität zum Schutz des
Biodiversitätspotenzials auf São Tomé”, Ruprecht-Karls-Universität Heidelberg.
Author: Signe Mikulane. Land use types: “nao-florestal” – non-forested areas,
“plantacao de sombra” – shade plantation, “floresta secundária” – secondary forest,
“floresta native” – native forest.
58
Figure S12. Land use. Land use is projected in WGS 84 (EPSG 4326). Pixel size is 0.000833º x
0.000833º. Dimensions are 471 x 359 cells (rows x columns).
59
Fig
ure
S1
3.
Corr
elog
ram
bet
wee
n e
nvir
on
men
tal
vari
ab
les.
Sp
earm
an’s
ran
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orr
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ion
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anel
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60
SECTION II: São Tomé Bird Species
Table S3. Bird species’ characteristics. For each bird species the percentage of presences per total data points was calculated
(n = 3056). Species were divided in endemic and non-endemic, and according to feeding guilds. 1Endemic subspecies were
grouped with non-endemic species. 2Insectivores and carnivores form the carnivores group.
Species Nº of
Presences
Presences per
Total Point
Counts (%)
Endemism Feeding
Guild
Agapornis pullaria 233 7.559 Non-endemic Frugivore
Amaurocichla bocagei 257 8.41 Endemic Insectivore2
Anabathmis newtonii 2126 69.372 Endemic Omnivore
Bostrychia bocagei 123 4.025 Endemic Carnivore
Bubulcus ibis 25 0.818 Non-endemic Insectivore2
Chrysococcyx cupreus 399 13.056 Endemic subspecies1 Insectivore2
Columba larvata 1072 35.013 Endemic subspecies1 Omnivore
Columba malherbii 1045 34.097 Endemic Frugivore
Columba thomensis 220 7.199 Endemic Frugivore
Coturnix delegorguei 47 1.538 Endemic subspecies1 Omnivore
Dreptes thomensis 244 7.984 Endemic Omnivore
Estrilda astrild 321 10.471 Non-endemic Granivore
Euplectes albonotatus 24 0.785 Non-endemic Omnivore
Euplectes aureus 29 0.949 Non-endemic Omnivore
Euplectes hordeaceus 32 1.047 Non-endemic Omnivore
Lanius newtoni 164 5.366 Endemic Insectivore2
Lonchura cucullata 56 1.8 Non-endemic Granivore
Milvus migrans 37 12.435 Non-endemic Carnivore
Neospiza concolor 385 1.211 Endemic Frugivore
Onychognathus
fulgidus 1195 39.103 Endemic subspecies Omnivore
Oriolus crassirostris 902 29.516 Endemic Omnivore
Otus hartlaubi 204 6.675 Endemic Omnivore
Ploceus grandis 268 8.737 Endemic Omnivore
Ploceus sanctithomae 1920 62.664 Endemic Omnivore
Prinia molleri 1905 62.075 Endemic Insectivore2
Serinus rufobrunneus 1662 54.156 Endemic Omnivore
Streptopelia
senegalensis 220 7.166 Non-endemic Granivore
Terpsiphone
atrochalybeia 880 28.665 Endemic Insectivore2
Treron sanctithomae 784 25.556 Endemic Frugivore
Turdus olivaceofuscus 1111 36.289 Endemic Omnivore
Uraeginthus
angolensis 75 2.389 Non-endemic Granivore
Vidua macroura 34 1.113 Non-endemic Granivore
Zosterops feae 566 18.521 Endemic Omnivore
Zosterops lugubris 2042 66.623 Endemic Omnivore
61
SECTION III: Binomial Generalized Linear Models
Table S4. Validation of the best multivariable model. The best model was selected based on the Akaike Information Criterion
corrected for small sample sizes (AICc). The goodness of fit was analysed with McFadden’s index. The receiving operating
characteristic curve (ROC) was calculated, as well as the area under the curve (AUC) to examine the model’s performance.
Species AICc McFadden’s Index (R2) AUC
Agapornis pullaria 844.72 0.267 0.880
Amaurocichla bocagei 1016.64 0.181 0.804
Anabathmis newtonii 2331.27 0.115 0.739
Bostrychia bocagei 518.34 0.265 0.852
Bubulcus ibis 170 0.275 0.882
Chrysococcyx cupreus 1641.21 0.065 0.639
Columba larvata 2635.88 0.058 0.647
Columba malherbii 2584.41 0.069 0.698
Columba thomensis 1014.93 0.112 0.751
Coturnix delegorguei 134.86 0.604 0.985
Dreptes thomensis 1055.9 0.131 0.781
Estrilda astrild 778.8 0.459 0.917
Euplectes albonotatus 73.68 0.484 0.956
Euplectes aureus 102.11 0.578 0.984
Euplectes hordeaceus 109.18 0.501 0.982
Lanius newtoni 528.27 0.453 0.939
Lonchura cucullata 166.71 0.505 0.955
Milvus migrans 1272.05 0.226 0.839
Neospiza concolor 241 0.101 0.831
Onychognathus fulgidus 2647.65 0.081 0.657
Oriolus crassirostris 2345.55 0.107 0.695
Otus hartlaubi 1006.93 0.086 0.680
Ploceus grandis 1110.3 0.149 0.791
Ploceus sanctithomae 2553.24 0.099 0.675
Prinia molleri 2556.16 0.100 0.743
Serinus rufobrunneus 2766.88 0.067 0.667
Streptopelia senegalensis 547.47 0.501 0.953
Terpsiphone atrochalybeia 2543.24 0.030 0.657
Treron sanctithomae 2259.57 0.082 0.663
Turdus olivaceofuscus 2716.91 0.0443 0.633
Uraeginthus angolensis 232.22 0.521 0.978
Vidua macroura 117.5 0.555 0.961
Zosterops feae 1841.91 0.110 0.695
Zosterops lugubris 2407.28 0.121 0.743
62
Table S5. Relative variable importance (RVI). The relative variable importance was obtained for every environmental
variable from the multivariable species model (RVI values range from 0 to 1). Ruggedness was excluded from these models
given a variance inflation factor (VIF) bigger than 10. A relative importance value of 1 means the variable is included in all
best models.
Species Rainfall Remoteness
Index
Distance to
Coast Altitude Slope
Agapornis pullaria 1 1 0.93 0.45 0.27
Amaurocichla bocagei 0.29 1 0.3 0.8 0.3
Anabathmis newtonii 0.98 0.99 0.52 0.57 0.44
Bostrychia bocagei 0.92 0.6 1 1 1
Bubulcus ibis 0.28 0.45 0.41 0.84 0.28
Chrysococcyx cupreus 0.76 1 1 0.99 1
Columba larvata 0.35 0.99 0.36 0.3 0.84
Columba malherbii 0.99 0.98 0.47 1 0.27
Columba thomensis 1 0.3 0.62 1 0.29
Coturnix delegorguei 1 0.3 0.28 0.28 0.4
Dreptes thomensis 0.56 0.84 0.88 0.33 0.29
Estrilda astrild 0.8 1 0.78 0.5 043
Euplectes albonotatus 1 0.29 0.31 0.29 0.29
Euplectes aureus 1 0.36 0.46 0.29 0.3
Euplectes hordeaceus 1 0.33 0.69 0.43 0.3
Lanius newtoni 1 1 0.69 1 0.6
Lonchura cucullata 1 0.28 0.88 0.4 0.9
Milvus migrans 0.28 1 0.33 1 0.33
Neospiza concolor 0.47 0.42 0.44 0.35 0.28
Onychognathus fulgidus 0.9 0.95 0.36 1 0.38
Oriolus crassirostris 0.97 0.82 1 0.47 0.28
Otus hartlaubi 0.63 0.99 0.47 0.46 0.29
Ploceus grandis 0.29 1 0.32 0.28 0.29
Ploceus sanctithomae 0.27 1 0.28 0.32 0.78
Prinia molleri 1 0.29 0.81 0.33 0.29
Serinus rufobrunneus 0.28 1 0.86 0.3 0.73
Streptopelia senegalensis 1 0.76 0.28 0.29 0.28
Terpsiphone atrochalybeia 0.31 1 0.77 0.74 0.59
Treron sanctithomae 3.40 x 10-1 0.63 1 0.35 0.27
Turdus olivaceofuscus 9.50 x 10-1 0.28 1 1 0.31
Uraeginthus angolensis 1 0.97 0.73 0.46 0.35
Vidua macroura 1 0.32 0.38 0.74 0.4
Zosterops feae 2.80 x 10-1 1 0.29 0.4 0.35
Zosterops lugubris 1 0.88 0.73 0.67 0.42
63
Table S6. Single-variable model coefficients. The coefficients were obtained from single-variable models. Positive
coefficients indicate a positive relation between the variable in question and the species response. On the contrary, negative
coefficients translate a negative relation between variables and species response, indicating a decrease in species occurrence
with an increase in variable values. The degree of increase or decrease is given by the coefficients value.
Species Rainfall Remoteness
Index
Distance
to Coast Altitude Slope
Agapornis pullaria -6.003 x 10-4 -0.867 -36.503 -0.004 -0.055
Amaurocichla bocagei 3.302 x 10-4 0.632 21.144 3.119 x 10-4 0.025
Anabathmis newtonii -3.124 x 10-4 -0.365 -11.784 -2.686 x 10-4 -0.005
Bostrychia bocagei 4.549 x 10-4 0.402 23.662 -3.384 x 10-4 -0.038
Bubulcus ibis -6.408 x 10-4 -0.814 9.596 7.640 x 10-4 -0.059
Chrysococcyx cupreus -2.175 x 10-4 -0.179 2.595 -1.761 x 10-4 0.015
Columba larvata 4,179 x 10-5 -0.088 -7.898 -3.765 x 10-4 -0.009
Columba malherbii -1.983 x 10-4 -0.288 -14.863 -0.001 -0.019
Columba thomensis 2.146 x 10-4 0.322 10.708 0.002 0.027
Coturnix delegorguei -0.006 -1.892 -60.871 -0.015 -0.366
Dreptes thomensis 2.872 x 10-4 0.537 23.457 8.702 x 10-4 0.031
Estrilda astrild -7.662 x 10-4 -1.536 -34.706 -0.004 -0.128
Euplectes albonotatus -0.005 -1.774 -67.054 -0.016 -0.343
Euplectes aureus -0.006 -1.676 -79.339 -0.014 -0.224
Euplectes hordeaceus -0.004 -1.678 -82.726 -0.016 -0.227
Lanius newtoni 5.643 x 10-4 2.048 39.291 0.002 0.066
Lonchura cucullata -0.002 -1.939 -80.254 -0.018 -0.391
Milvus migrans -3.404 x 10-4 -0.659 -34.425 -0.005 -0.069
Neospiza concolor -0,002369583 -1.939 -80.254 -0.018 -0.391
Onychognathus fulgidus -1.599 x 10-5 -0.127 -10.912 -9.932 x 10-4 0.008
Oriolus crassirostris 9.871 x 10-5 0.294 17.459 0.001 0.034
Otus hartlaubi -2.098 x 10-4 -0.280 -6.128 -3.516 x 10-4 -0.007
Ploceus grandis -3.142 x 10-4 -0.522 -14.819 -0.002 -0.029
Ploceus sanctithomae -1.187 x 10-4 -0.238 -10.958 -4.796 x 10-4 0.006
Prinia molleri 1.825 x 10-4 0.382 15.220 5.275 x 10-4 0.025
Serinus rufobrunneus -3.901 x 10-4 -0.316 -8.169 -9.627 x 10-5 -0.007
Streptopelia
senegalensis -0.002 -1.467 -21.579 -0.002 -0.135
Terpsiphone
atrochalybeia -1.229 x 10-4 -0.166 -2.680 -4.105 x 10-4 -0.004
Treron sanctithomae 7.591 x 10-5 0.142 10.501 7.848 x 10-4 0.024
Turdus olivaceofuscus -2,282 x 10-5 -0.015 1.796 -3.104 x 10-4 0.010
Uraeginthus angolensis -0,003 -2.740 -54.206 -0.010 -0.225
Vidua macroura -0,005 -1.856 -73.957 -0.021 -0.396
Zosterops feae -9,150 x 10-5 -0.239 -10.759 -9.788 x 10-4 0.002
Zosterops lugubris -2.675 x 10-4 -0.283 -9.708 2.489 x 10-5 0.008
64
Table S7. Kruskal-Wallis rank test to analyse the difference in relative importance of each environmental variable
between endemic and non-endemic species, as well as among feeding guilds. A post hoc Dunn-test with the Benjamini-
Hochberg correction was performed to evaluate the differences between feeding guilds. Differences were considered significant
with p-value < 0.05.
Variables Endemism Feeding Guild
KW test KW test Dunn-test
Land use 0.013* E > N - - -
Rainfall - - - - -
Remoteness - - - - -
Distance to Coast - - - - -
Altitude - - 0.039* 0.031* C > O
Topography 0.017* E > N - - -
Slope - - 0.016*
0.013*
0.024*
0.017*
F < C
F < O
F < G
Note: ‘*’ p-value ≤ 0.05; ‘**’ p-value ≤ 0.01; ‘***’ p-value ≤ 0.001.
65
Figure S14. Relative variable importance (RVI) of each continuous environmental variable. Boxplots drawn with RVI
values of all bird species (See List of Abbreviations and Acronyms, pages IX to X), representing the median (thick line), the
first and third quartiles (box), the extremes (whiskers) and the outliers (dots). Endemic species are represented in bold. The
RVI values range from 0 to 1.
Figure S15. Relative variable importance (RVI) of each continuous environmental variable in endemic and non-
endemic species. Separate boxplots were drawn with RVI values of endemic and non-endemic species, representing the median
(thick line), the first and third quartiles (box), the extremes (whiskers) and the outliers (dots). The RVI values range from 0 to
1.
66
Fig
ure
S1
6.
Rel
ati
ve
va
ria
ble
im
po
rta
nce
(RV
I) o
f each
co
nti
nu
ou
s en
vir
on
men
tal
vari
ab
le i
n e
ver
y f
eed
ing
gu
ild
sp
ecie
s g
rou
p.
Bo
xp
lots
wer
e d
raw
n w
ith
RV
I val
ues
of
each
fee
din
g g
uil
d (
O –
om
niv
ore
s, G
– g
ran
ivo
res,
F –
fru
giv
ore
s, C
– c
arn
ivo
res)
, re
pre
sen
tin
g t
he
med
ian
(th
ick
lin
e),
the
firs
t an
d t
hir
d q
uar
tile
s (b
ox),
th
e ex
trem
es (
wh
isker
s) a
nd
th
e ou
tlie
rs (
do
ts).
Th
e R
VI
val
ues
ran
ge
fro
m 0
to
1.
67
SECTION IV: Proportion of species occurrence per land use type
Table S8. Proportion of species occurrence per land use type and topography class. The standardized proportion of species
occurrence in every land use type and topography class for endemic and non-endemic species (E - endemic species, N – non-
endemic species), as well as for each feeding guild (O – omnivores, G – granivores, F – frugivores, C – carnivores). All values
in percentage. Total proportion of species occurrence was calculated.
Total E N F G C O
Native Forest 23.920 39.835 3.759 37.369 0.021 33.684 17.595
Secondary Forest 19.155 26.332 10.064 25.909 2.860 20.264 22.739
Shade Plantation 24.756 20.485 30.165 21.066 31.279 25.616 25.061
Non-Forested Areas 32.170 13.347 56.012 15.656 65.840 20.435 34.606
Flat Areas 54.709 34.455 80.364 41.197 92.650 42.505 54.818
Valleys 6.899 9.959 3.024 7.619 0.982 10.077 6.907
Middle Slope 10.303 13.634 6.083 12.361 3.113 12.152 10.835
Upper Slope 26.760 39.760 10.295 35.429 3.255 33.796 26.462
Ridges 1.329 2.193 0.234 3.394 0 1.469 0.978
68
SECTION V: Exploratory analysis for species richness and composition modelling
Table S9. Bird species’ characteristics. For each species the total number of presences was calculated. Species were divided
in endemic and non-endemic, and according to feeding guilds. 1Endemic subspecies were grouped with non-endemic species. 2Insectivores and carnivores form the carnivore group.
Species Nº of
Presences Endemism
Feeding
Guild
Agapornis pullaria 36 Non-endemic Frugivore
Amaurocichla bocagei 26 Endemic Insectivore2
Anabathmis newtonii 186 Endemic Omnivore
Bostrychia bocagei 7 Endemic Carnivore
Bubulcus ibis 13 Non-endemic Insectivore2
Chrysococcyx cupreus 70 Endemic subspecies1 Insectivore2
Columba larvata 154 Endemic subspecies1 Omnivore
Columba malherbii 120 Endemic Frugivore
Columba thomensis 26 Endemic Frugivore
Coturnix delegorguei 7 Endemic subspecies1 Omnivore
Dreptes thomensis 41 Endemic Omnivore
Estrilda astrild 41 Non-endemic Granivore
Euplectes albonotatus 5 Non-endemic Omnivore
Euplectes aureus 4 Non-endemic Omnivore
Euplectes hordeaceus 3 Non-endemic Omnivore
Francolinus afer 5 Non-endemic Omnivore
Lanius newtoni 21 Endemic Insectivore2
Lonchura cucullata 9 Non-endemic Granivore
Milvus migrans 79 Non-endemic Carnivore
Neospiza concolor 3 Endemic Frugivore
Onychognathus fulgidus 156 Endemic subspecies Omnivore
Oriolus crassirostris 117 Endemic Omnivore
Otus hartlaubi 38 Endemic Omnivore
Ploceus grandis 64 Endemic Omnivore
Ploceus sanctithomae 180 Endemic Omnivore
Prinia molleri 185 Endemic Insectivore2
Psittacus erithacus 1 Non-endemic Frugivore
Serinus rufobrunneus 179 Endemic Omnivore
Streptopelia senegalensis 28 Non-endemic Granivore
Terpsiphone atrochalybeia 148 Endemic Insectivore2
Treron sanctithomae 123 Endemic Frugivore
Turdus olivaceofuscus 157 Endemic Omnivore
Uraeginthus angolensis 11 Non-endemic Granivore
Vidua macroura 5 Non-endemic Granivore
Zosterops feae 103 Endemic Omnivore
Zosterops lugubris 186 Endemic Omnivore
69
Fig
ure
S1
7. C
orr
elog
ram
bet
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n e
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on
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tal
vari
ab
les
an
d r
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– S
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70
SECTION VI: Poisson Generalized Linear Models
Table S10. Validation of the best model of species richness, endemic species richness and non-endemic species richness. The best model was selected based on the Akaike Information Criterion corrected for small sample sizes (AICc). The goodness
of fit was analysed with McFadden’s index and with the Residual Deviance Goodness of Fit Test. The null hypothesis of the
Residual Deviance Goodness of Fit Test is that our model is correctly specified. Differences were considered significant with
p-value < 0.05.
AICc
McFadden’s Index Residual Deviance
R2 Residual
Deviance p-value df
Species Richness 628.42 7.696x10-3 44.233 1 123
Endemic Species Richness 593.30 0.035 42.860 1 123
Non-Endemic Species
Richness 429.41 0.119 44.858 1 123
Note: ‘*’ p-value ≤ 0.05; ‘**’ p-value ≤ 0.01; ‘***’ p-value ≤ 0.001.
71
Fig
ure
S18
. P
ears
on
an
d D
evia
nce
Res
idu
als
. V
alid
atio
n w
as e
xp
lore
d b
y p
lott
ing t
he
Pea
rso
n a
nd D
evia
nce
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idu
als
agai
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th
e p
redic
ted
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ues
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ch f
itte
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del
(sc
atte
rplo
ts o
n t
he
firs
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d c
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s).
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as a
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od
el (
his
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ms
on t
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nd f
ort
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R –
Sp
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– E
nd
emic
sp
ecie
s ri
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ess;
NS
R –
Non
-en
dem
ic s
pec
ies
rich
nes
s).
72
Table S11. Species richness and environmental variables. The relative importance (RVI) was obtained for every variable
from each species richness model. The RVI values range from 0 to 1. A relative importance value of 1 means the variable is
included in all best models. The response of total, endemic and non-endemic species richness to each environmental variable
was analysed with the Spearman’s rank correlation coefficient (rho). Differences were considered significant with p-value <
0.05.
Species
Richness
Endemic Species
Richness
Non-Endemic Species
Richness
Land use RVI 0.29 0.94 0.83
rho 0.016 -0.449*** 0.653***
Rainfall RVI 0.31 0.31 0.92
rho -0.124 0.172 -0.424***
Topography RVI 0.27 0.30 0.29
rho -0.050 0.220* -0.402***
Altitude RVI 0.47 0.33 0.90
rho -0.039 0.270** -0.475***
Distance to
Coast
RVI 0.34 0.39 0.31
rho 0.065 0.318*** -0.328***
Slope RVI 0.37 0.37 0.27
rho 0.129 0.359*** -0.276**
Note: ‘*’ p-value ≤ 0.05; ‘**’ p-value ≤ 0.01; ‘***’ p-value ≤ 0.001.
73
SECTION VII: Generalized Dissimilarity Modelling
Table S12. Significance test of GDM model. A significance test was made using 100 permutations to explore model
significance. Model fit was examined by the total deviance explained in each model. The full model contains all environmental
variables. Further models have a bigger model deviance and less explanatory variables. Differences were considered significant
with p-value < 0.05.
Full Model Model 1 Model 2 Model 3 Model 4 Model 5
Model Deviance 432.170 434.287 438.267 445.539 445.594 468.650
Percent Deviance Explained 43.577 43.301 42.781 41.832 41.825 38.814
Model p-value 0.000 0.000 0.000 0.000 0.000 0.000
Fitted permutations 100 100 99 99 97 94
Figure S19. Overall model fit in explaining the observed dissimilarities. The
observed composition dissimilarity values were plotted against the predicted
composition dissimilarity values.
74
Table S13. Significance test for each variable in GDM model. A significance test for each variable was made using 100
permutations. The full model contains all environmental variables. Further models have less explanatory variables. Variable
importance (VI) was measured as the percent change in deviance explained by the full model and the deviance explained by a
model fit with that variable permuted. The significance (Sig) was estimated using the bootstrapped p-value when the variable
was permuted. Differences were considered significant with p-value < 0.05.
Full Model Model 1 Model 2 Model 3 Model 4 Model 5
VI Sig VI Sig VI Sig VI Sig VI Sig VI Sig
Land use 10.824 0.00 13.373 0.00 14.502 0.00 19.317 0.00 19.353 0.00 34.888 0.00
Rainfall 6.748 0.00 7.369 0.00 7.364 0.00 7.721 0.00 13.510 0.00 15.508 0.00
Topography 0.634 0.13 - - - - -
Altitude 3.316 0.01 4.263 0.01 6.074 0.00 7.107 0.00 7.197 0.00 -
Distance to Coast 1.144 0.07 1.199 0.07 - - - -
Slope 1.191 0.12 2.255 0.05 2.219 0.04 - - -
Geographic Distance 0.005 0.00 0.009 0.00 0.009 0.00 0.017 0.00 - -
Figure S20. K-fold cross-validation of GDM. A k-fold cross-validation was made using 100
permutations and 30% as testing data. Histogram representing Pearson correlation between the
observed and the predicted compositional dissimilarities, for the testing set of sites. The red line
indicates the mean correlation.
75
Table S14. Importance of each predictor variable. The relative importance equals the sum of I-splines coefficients from the
fitted generalized dissimilarity model.
Environmental Gradient Relative Importance
Land use 0.220
Rainfall 0.217
Topography 0.041
Altitude 0.181
Distance to Coast 0.073
Slope 0.079
Geographic Distance 0.015
Deviance Explained (%) 43.577
76
Fig
ure
S2
1. R
esp
on
se c
urv
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of
each
pre
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. T
he
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le. T
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1 –
Fla
t ar
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2 –
Val
leys,
3 –
Mid
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.
77
SECTION VIII: R scripts
‘*’ command applied to every variable.
‘**’ command applied to every species.
‘***’ command also applied to endemic and non-endemic species richness.
Part I. Exploratory Analysis ### Exploratory Analysis ### # Import data VarL <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/MatrizPresencas/BLFilExtnPhDS0_VarL.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) library(vegan) # Defining categorical variables VarL$LU2016 <- as.factor(VarL$LU2016) VarL$TPI005 <- as.factor(VarL$TPI005) # Analyse outliers and variance homogeneity with a boxplot* boxplot(VarL$SRTM, data = VarL, main = "SRTM") # Evaluate multicollinearity with Spearman’s rank correlation coefficient # See result in Figure S13 and another example in Table S17 z<-cbind(VarL$Topography, VarL$Remoteness,VarL$LandUse, VarL$Altitude,VarL$Ruggedness, VarL$Rainfall, VarL$DistCoast) colnames(z)<-c("Topography", "Remoteness","LandUse", "Altitude","Ruggedness","Rainfall","DistCoast") panel.smooth2<-function (x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = "red", span = 2/3, iter = 3, ...) { points(x, y, pch = pch, col = col, bg = bg, cex = cex) ok <- is.finite(x) & is.finite(y) if (any(ok)) lines(stats::lowess(x[ok], y[ok], f = span, iter = iter), col = 1, ...) } panel.cor<-function(x, y, digits=1, prefix="", cex.cor) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) r1=cor(x,y,use="pairwise.complete.obs") r <- abs(cor(x, y,use="pairwise.complete.obs")) txt <- format(c(r1, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep="") if(missing(cex.cor)) cex <- 0.9/strwidth(txt) text(0.5, 0.5, txt, cex = cex * r) } panel.hist<-function(x, ...) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col="white", ...) } pairs(z,lower.panel=panel.smooth2,upper.panel=panel.cor,diag.panel=panel.hist)
Part II. Generalized linear models with binomial distribution ### Generalized linear models with binomial distribution ### VarL <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/MatrizPresencas/BLFilExtnPhDS0_VarL.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) names(VarL) Esp <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/MatrizPresencas/BLFilExtnPhDS0_Esp.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE)
78
names(Esp) data <- cbind(VarL,Esp) # Divide sample in test and train data library(caTools) set.seed(101) sample = sample.split(data$Code, SplitRatio = .70) train = subset(data, sample == TRUE) test = subset(data, sample == FALSE) # Defining categorical variables test$LU2016 <- as.factor(test$LU2016) train$LU2016 <- as.factor(train$LU2016) test$cTPI_005 <- as.factor(test$cTPI_005) train$cTPI_005 <- as.factor(train$cTPI_005) # Look for missing values (NAs) na.fail(train) # Build logistic model between dependent variable and independent variables library(MuMIn) # Species to analyze species <- c("Agapul","Amaboc","Ananew","Bosboc","Bubibi","Chrcup","Collar","Colmal","Coltho","Cotdel","Dretho","Estast","Eupalb","Eupaur","Euphor","Lannew","Loncuc","Milmig","Neocon","Onyful","Oricra","Otuhar","Plogra","Plosan","Primol","Serruf","Strsen","Teratr","Tresan","Turoli","Uraang","Vidmac","Zosfea","Zoslug") # Create list to contain models lista_modelos <- list() # Create list to contain dredge result lista_dredge <- list() for(specie in species){ # Considerando que você quer usar o mesmo modelo inicial para todas as Especies explanatory <- c("Tobler","SRTM","Slope","Chuva","DistCosta","cTPI_005","LU2016") # Criando a formula de acordo com o nome da especie em questao formula <- as.formula(paste(specie, "~", paste(explanatory, collapse = "+"))) lista_modelos[[specie]] <- glm(formula, data = train, family = binomial, na.action = "na.fail") print(paste("#####", "Result for species:", specie, "#####")) print(summary(lista_modelos[[specie]])) print("\n\n\n") lista_dredge[[specie]] <- dredge(lista_modelos[[specie]]) summary(lista_dredge[[specie]]) } warnings() ### GLM with binomial distribution - Model validation ### # Goodness of fit Pseudo R^2 – McFadden’s Index** # See results in Table S4 and another example in Table S10 library(pscl) pR2(lista_modelos[["Agapul"]]) # ROC curve and AUC ** # tpr - True positive rate = Sensitivity # fpr - False positive rate = 1 – Specificity # See results in Table S4 and another example in Table S10 library(ROCR) predAgapul <- predict(lista_modelos[["Agapul"]], newdata = test, type = "response") ROCRpredAgapul <- prediction(predAgapul,test$Agapul) ROCRperfAgapul <- performance(ROCRpredAgapul, 'tpr','fpr') plot(ROCRperfAgapul, colorize = TRUE, text.adj = c(-0.2,1.7)) auc <- performance(ROCRpredAgapul,measure = "auc") auc <- [email protected][[1]] auc # Pearson residuals** library(boot) resid(lista_modelos[["Agapul"]], type="pearson") # Deviance residuals** residuals.glm(lista_modelos[["Agapul"]])
79
Part III. Generalized linear models with poisson distribution ### Generalized linear models with poisson distribution ### Quad <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/MatrizPresencas/Quad_S0Dup_FranPsi.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) library(caTools) set.seed(101) sample = sample.split(Quad$Code, SplitRatio = .70) train = subset(Quad, sample == TRUE) test = subset(Quad, sample == FALSE) library(MASS) library(AER) library(VGAM) library(MuMIn) # Species richness models and under-dispersion test*** pGLMSR <- glm (SpeciesRichness ~ SRTM + Slope + cTPI_005 + DistCosta + LU2016 + Chuva, family = "poisson", data = train) summary(pGLMSR) dispersiontest(pGLMSR,alternative = "less") # Data is underdispersed # Mapping species richness # Importing environmental variables* library(raster) SRTM = raster("C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/VariaveisRaster/Uniformizados/VarLocais/CorrectedNames/NoData/SRTM.tif") NAvalue(SRTM) <- -32768 # Stack rasters rasters <- stack(SRTM, Slope, cTPI_005, DistCosta, LU2016, Chuva, bands=NULL) # Species richness map # See results in Figure 2.2 SRp <- predict(rasters, pGLMSR, type="response") plot(SRp, xaxt='n', yaxt='n', main = "Species Richness") writeRaster(SRp, 'SRp.tif') # Calculate relative variable importance ddpGLMSR <- dredge(pGLMSR) avgpGLMSR <- model.avg(ddpGLMSR) summary(avgpGLMSR) ### GLM with poisson distribution - Model validation ### # Deviance Residuals Goodness of Fit Test*** # See results in Table S10 with(pGLMSR, cbind(res.deviance = deviance, df = df.residual, p = pchisq(deviance, df.residual, lower.tail=FALSE))) # Plotting residuals*** # See results in Figure S18 # Pearson residuals par(mfrow=c(3,4),mar=c(4,4,2,2)) ppGLMSR <- predict(pGLMSR, type = "response") pearsonpGLMSR <- resid(pGLMSR, type = "pearson") plot(x = ppGLMSR, y = pearsonpGLMSR, main = "Pearson residuals - SR", ylab= "Residuals", xlab = "Predicted Values") hist(pearsonpGLMSR,main="Pearson residuals - SR",xlab="Residuals") # Deviance residuals deviancepGLMSR <- resid(pGLMSR, type = "deviance") plot(x = ppGLMSR, y = deviancepGLMSR, main = "Deviance residuals - SR", ylab= "Residuals", xlab = "Predicted Values") hist(deviancepGLMSR,main="Deviance residuals - SR",xlab="Residuals")
Part IV. Generalized dissimilarity modelling ### Generalized dissimilarity modelling (GDM) ### # See results in Figure 2.3, Figure S19 and Figure S21 STbirds <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/MatrizPresencas/Quad_S0Dup_FranPsi.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) library(caTools)
80
set.seed(101) sample = sample.split(STbirds$Code, SplitRatio = .70) train = subset(STbirds, sample == TRUE) test = subset(STbirds, sample == FALSE) library(gdm) # Get columns with xy, site ID, and species data sppTab <- train[, c("Code", "Agapul", "Amaboc", "Ananew", "Bosboc", "Bubibi", "Chrcup", "Collar", "Colmal", "Coltho", "Cotdel", "Dretho", "Estast", "Eupalb", "Eupaur", "Euphor", "Fraafe","Lannew", "Loncuc", "Milmig", "Neocon", "Onyful", "Oricra", "Otuhar", "Plogra", "Plosan", "Primol", "Psieri", "Serruf", "Strsen", "Teratr", "Tresan", "Turoli", "Uraang", "Vidmac", "Zosfea", "Zoslug", "Latitude", "Longitude")] # Import environmental variables* library(raster) library(rgdal) SRTM = raster("C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/VariaveisRaster/Uniformizados/VarLocais/CorrectedNames/NoData/SRTM.tif") NAvalue(SRTM) <- -32768 # Environmental raster data envRast <- stack(rasters) gdmTab.rast <- formatsitepair(sppTab, bioFormat=1, XColumn="Longitude", YColumn="Latitude", siteColumn="Code", predData=envRast) sum(is.na(gdmTab.rast)) gdmTab.rast <- na.omit(gdmTab.rast) summary(gdmTab.rast) gdmTab.rast[1:3,] # Fit gdm using the table with environmental data gdm.rast <- gdm(gdmTab.rast, geo=T) summary.gdm(gdm.rast) plot.gdm(gdm.rast) # Transform rasters and plot pattern rastTrans <- gdm.transform(gdm.rast, envRast) rastTrans[1:3,] plot(rastTrans) # Visualizing multi-dimensional biological patterns rastDat <- na.omit(getValues(rastTrans)) pcaSamp <- prcomp(rastDat) summary(pcaSamp) pcaRast <- predict(rastTrans, pcaSamp, index=1:3) pcaRast # Scale rasters pcaRast[[1]] <- (pcaRast[[1]]-pcaRast[[1]]@data@min) / (pcaRast[[1]]@data@max-pcaRast[[1]]@data@min)*255 pcaRast[[2]] <- (pcaRast[[2]]-pcaRast[[2]]@data@min) / (pcaRast[[2]]@data@max-pcaRast[[2]]@data@min)*255 pcaRast[[3]] <- (pcaRast[[3]]-pcaRast[[3]]@data@min) / (pcaRast[[3]]@data@max-pcaRast[[3]]@data@min)*255 par(mfrow = c(1,1)) plotRGB(pcaRast, r=1, g=2, b=3) ### GDM – Model significance and validation ### # See results in Table S12, Table S13 and Figure S20 # Test the significance of the model (100 permutes) model.rast.test<- gdm.varImp(gdmTab.rast, geo=TRUE, fullModelOnly = FALSE, nPerm = 100, parallel = TRUE) # show the results model.rast.test str(model.rast.test) # Validate the GDM n.tests<-100 proportion.training <- 0.7 n.sites<-nrow(sppTab) n.train<-floor(n.sites*proportion.training) n.eval<-n.sites-n.train site.sampler<-c(rep(1,times=n.train),rep(0,times=n.eval)) Pearsons.correlation<-rep(0,length=n.tests) for(i.test in 1:n.tests) {
81
# permute the site sampler site.sampler<-site.sampler[sample(length(site.sampler))] # create the input table for training the GDM sppTab.train <- sppTab[site.sampler==1,] gdmTab.rast.train <- formatsitepair(bioData=sppTab.train, bioFormat=1, dist="bray", siteColumn="Code", XColumn="Longitude", YColumn="Latitude", predData=envRast) # and create the data table for testing the GDM sppTab.test <- sppTab[site.sampler==0,] gdmTab.rast.test <- formatsitepair(bioData=sppTab.test, bioFormat=1, dist="bray", siteColumn="Code", XColumn="Longitude", YColumn="Latitude", predData=envRast) # Fit the model on the training set of sites train.mod <- gdm(gdmTab.rast.train, geo=TRUE) # now predict the dissimilarity for the test sites (pairs) pred.test <- predict(train.mod, gdmTab.rast.test) # assess the correlation between the observed and predicted evaluation dissimilarities Pearsons.correlation[i.test] <- cor(pred.test , gdmTab.rast.test[,1] , method = "pearson") } # end for i.test hist(Pearsons.correlation, main ="Histogram of Pearson Correlation", ylab = "Frequency", xlab = "Pearson Correlation") abline(v=mean(Pearsons.correlation), col="red")
Part V. Statistical analyses and output figures ### Statistical analyses and Figures of Chapter 1 and 2 ### # Figure 1.2 RVI_Origin <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/RVI_Origin.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) RVIspecies <- RVI_Origin[1:34,2:8] Especies <- cbind("Vidua macroura","Uraeginthus angolensis","Streptopelia senegalensis","Onychognathus fulgidus","Milvus migrans","Lonchura cucullata","Euplectes hordaceus","Euplectes aureus","Euplectes albonotatus","Estrilda astrild","Coturnix delegorguei","Columba larvata","Chrysococcyx cupreus","Bubulcus ibis","Agapornis pullaria","Zosterops lugubris","Zosterops feae","Turdus olivaceofuscus","Treron sanctithomae","Terpsiphone atrochalybeia","Serinus rufobrunneus","Prinia molleri","Ploceus sanctithomae","Ploceus grandis","Otus hartlaubi","Oriolus crassirostris","Neospiza concolor","Lanius newtoni","Dreptes thomensis","Columba thomensis","Columba malherbi","Bostrychia bocagei","Anabathmis newtoni","Amaurocichla bocagei") Variables <- cbind("Land Use", "Rainfall", "Remoteness", "Dist.Coast", "Altitude", "Topography", "Slope") library(plotrix) op <- par(mar = c(1,15,3,4)) color2D.matplot(x=(1-RVIspecies), axes=FALSE, ann=FALSE, vcol=NA, vcex=0.7, border=NA) axis(side = 2, at = 0.5:33.5, labels = Especies, las = 2, cex.axis = 1, line = 0, font = 3, family="serif") axis(side = 3, at = 0.5:6.5, labels = Variables, cex.axis = 1, line = 0, family="serif") # Kruskal Wallis rank test and Dunn-tests with Benjamini-Hochberg corrections* # See results in Table S7 # Example for Trophic Guilds library(FSA) RVI_Trophic <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/RVI_Trophic.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) kruskal.test(RVI_Trophic$Slope~RVI_Trophic$TrophicGuild) dunnTest(RVI_Trophic$Slope~RVI_Trophic$TrophicGuild, method="bh")
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# Figure 1.3 # See another example in Figure 1.5 coef <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/COEF.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) dat1 <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/LU_TPIp.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) dat2 <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/LU_TPIp2.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) par(mfrow=c(7,1),mar = c(2,4,1,0.4)) # Barplot for categorical variables* plot(dat2$Total,xlim = c(0,1.325),ylim = c(-0.5,1.5),type="n",yaxt="n",ylab="", family = "serif") legend(x=1.02,y=1.11, legend = c("NF", "SF", "SP", "NFA"), fill = c("black", "darkgrey", "lightgrey", "white"),cex=1.2, horiz=T) mtext(side=2,text="LandUse",line=2,cex=0.8, family = "serif") mtext(side=2,text="N",line=1,las=2,adj=0.5,padj=-.75,cex=0.8, family = "serif") mtext(side=2,text="E",line=1,las=2,adj=0.5,padj=1.5,cex=0.8, family = "serif") rect(0, 0.8, as.numeric(dat1[3,2])/100, 1.2, col ="black") rect(as.numeric(dat1[3,2])/100, 0.8, as.numeric(dat1[3,2]+dat1[3,3])/100, 1.2, col ="darkgrey") rect(as.numeric(dat1[3,2]+dat1[3,3])/100, 0.8, as.numeric(dat1[3,2]+dat1[3,3]+dat1[3,4])/100, 1.2, col ="lightgrey") rect(as.numeric(dat1[3,2]+dat1[3,3]+dat1[3,4])/100, 0.8, as.numeric(dat1[3,2]+dat1[3,3]+dat1[3,4]+dat1[3,5])/100, 1.2, col ="white") rect(0, -0.2, as.numeric(dat1[2,2])/100, 0.2, col ="black") rect(as.numeric(dat1[2,2])/100, -0.2, as.numeric(dat1[2,2]+dat1[2,3])/100, 0.2, col ="darkgrey") rect(as.numeric(dat1[2,2]+dat1[2,3])/100, -0.2, as.numeric(dat1[2,2]+dat1[2,3]+dat1[2,4])/100, 0.2, col ="lightgrey") rect(as.numeric(dat1[2,2]+dat1[2,3]+dat1[2,4])/100, -0.2, as.numeric(dat1[2,2]+dat1[2,3]+dat1[2,4]+dat1[2,5])/100, 0.2, col ="white") # Boxplot for continuous variables* par(font.lab=6) par(font.axis=6) boxplot(coef$Chuva~coef$Origin1,las=1,horizontal=T,yaxt="n") mtext(side=2,text="Rainfall",line=2,cex=0.8, family = "serif") mtext(side=2,text="N",line=1,las=2,adj=0.5,padj=-0.75,cex=0.8, family = "serif") mtext(side=2,text="E",line=1,las=2,adj=0.5,padj=1.5,cex=0.8, family = "serif") # Detrended correspondence analysis # Figure 1.4 datasp <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/MatrizPresencas/BLFilExtnPhDS0_Esp.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) datavar <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/VarL.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) species <- read.csv("C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/species.csv", , header = TRUE, sep = ";", dec = ",", fill = TRUE) sp <- datasp[,6:39] library(vegan) varlocal <- datavar[1:2398,] sp_ext <- sp[1:2398,] DCA <- decorana(sp_ext, iweigh = 0, ira = 0) summary(DCA) DCA.fit <- envfit(DCA ~ Remoteness + Altitude + Slope + Rainfall + DistCoast + Ridges + Upper + Middle + Flat + Valleys + NF + SF + SP + NFA, data=varlocal, perm=100) DCA.fit par(mfrow=c(1,1),mar = c(2,2,2,2)) plot(DCA,type="n", display = "spec",cex=2,xlim=c(-2.5,5),ylim=c(-3,3.5)) with(sp_ext, text(DCA, display = "spec", pos = 4, cex=1.2, font=1, col = "black", family="serif")) points(DCA, display="spec", col="grey", pch = c(15, 19, 17, 18)[as.numeric(species$Trophic)], cex=1.2) points(DCA, display="spec", pch = c(15, 19, 17, 18)[as.numeric(species$Trophic)], col = "black", cex=species$Origin*1.2) legend(x=2,y=-2.3, pt.cex =1.3, inset = c(0.2,0.03), cex = 0.9, legend = c("F", "C", "O", "G"), text.col = "black", col = "black", pch = c(15, 19, 17, 18),horiz=T) par(fig = c(.475, .925, .625, .925), mar=c(0,0,0,0), new=TRUE) plot(DCA,type="n", display = "site",cex=2, xlim = c(-2.5,1.8),ylim = c(-1.8,2),xaxt="n",yaxt="n", family="serif") plot(DCA.fit, col="black",cex=1.2, family="serif")
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# Figure 1.6 LU_EndNEnd <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/LU_EndNEnd.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) LU_IUCN <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/DATA_csv/LU_IUCN1.csv", header = TRUE, fill = TRUE) LU_EndNEnd <- LU_EndNEnd[1:4,2:35] LU_IUCN <- LU_IUCN[1:4,2:35] par(mfrow=c(1,1), mar = c(3, 12, 1, 12)) colnamesbarplot1 <- cbind("Lanius newtoni", "Neospiza concolor", "Bostrychia bocagei", "Columba thomensis", "Amaurocichla bocagei", "Dreptes thomensis", "Otus hartlaubi", "Oriolus crassirostris", "Treron sanctithomae", "Zosterops feae", "Turdus olivaceofuscus", "Columba malherbi", "Ploceus sanctithomae", "Zosterops lugubris", "Terpsiphone atrochalybeia", "Prinia molleri", "Anabathmis newtoni", "Serinus rufobrunneus", "Ploceus grandis", "Columba larvata", "Onychognathus fulgidus","Chrysococcyx cupreus", "Milvus migrans", "Agapornis pullaria", "Estrilda astrild", "Bubulcus ibis", "Euplectes aureus", "Vidua macroura", "Euplectes hordaceus", "Euplectes albonotatus", "Coturnix delegorguei", "Uraeginthus angolensis", "Streptopelia senegalensis", "Lonchura cucullata") barplot(as.matrix(LU_IUCN),horiz=TRUE, xlab="", ylab="", xaxt='n', axes=TRUE, names.arg=colnamesbarplot1, font = 3, cex.names=1, las=1, family="serif") axis(1, at = seq(0, 100, by = 25), las=1, cex=0.9, family="serif") abline(v=25, cex= 0.7, lty = 2) abline(v=50, cex= 0.7, lty = 2) abline(v=75, cex= 0.7, lty = 2) # Figure 2.4 SR <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/2ndArticle/Boxplots_SR.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) par(mfrow=c(3,1),mar = c(3,6,2,1)) par(family = "serif") boxplot(SR$Species.Richness~SR$ONP_1, ylim = c(1,16),xaxt="n", horizontal=T, las = 2, cex.axis= 1.7) mtext(side=2,text="Total",line=4, cex = 1.3) axis(1, las=0, cex.axis = 1.5) boxplot(SR$Endemic.Species.Richness~SR$ONP_1, ylim = c(1,16), xaxt="n", horizontal=T, las = 2, cex.axis= 1.7) mtext(side=2,text="Endemic",line=4, cex= 1.3) axis(1, las=0, cex.axis = 1.5) boxplot(SR$Non.Endemic.Species.Richness~SR$ONP_1, ylim = c(1,16), xaxt="n", horizontal=T, las = 2, cex.axis= 1.7) mtext(side=2,text="Non-Endemic",line=4, cex = 1.3) axis(1, las=0, cex.axis = 1.5) # Figure 2.5 par(mfrow=c(1,1),mar = c(5,5,1,1)) PropDet <- read.csv(file = "C:/Users/soares/Desktop/TratamentoDados/DADOS_RASTERS_UltimaVersao/FINAL/2ndArticle/PropDet.csv", header = TRUE, sep = ";", dec = ",", fill = TRUE) plot(PropDet$MedianProp, PropDet$MedianDet, ylim=range(c(PropDet$X1QuartilDet, PropDet$X3QuartilDet)), xlim=range(c(PropDet$X1QuartilProp, PropDet$X3QuartilProp)), pch=19, xlab="Endemic Species Proportion", ylab="Endemic Species Frequency", family="serif", cex.axis= 1.5, cex.lab = 1.5) # hack: we draw arrows but with very special "arrowheads" arrows(PropDet$MedianProp, PropDet$X1QuartilDet, PropDet$MedianProp, PropDet$X3QuartilDet, length=0.05, angle=90, code=3) arrows(PropDet$X1QuartilProp, PropDet$MedianDet, PropDet$X3QuartilProp, PropDet$MedianDet, length=0.05, angle=90, code=3)
