Evolutionary history and its relevance in understanding and
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Evolutionary history and its relevance in understanding and conserving southern African biodiversity Thèse de doctorat és Science de la vie (PhD) Presentée à la Faculté de Biologie et Médicine de l’Université de Lausanne Par Dorothea Pio Diplômée en Ecologie et Conservation (University of Aberdeen and University of East Anglia) Jury de Thèse: Prof. Edward E. Farmer, Président Prof. Antoine Guisan, Directeur de thèse Dr Nicolas Salamin, Co-directeur de thèse Dr Richard Grenyer, Expert Prof. Luca Fumagalli, Expert Lausanne 2010
Evolutionary history and its relevance in understanding and
IntroductionEvolutionary history and its relevance in understanding
and conserving southern
African biodiversity
Presentée à la
Faculté de Biologie et Médicine de l’Université de Lausanne
Par
(University of Aberdeen and University of East Anglia)
Jury de Thèse:
Prof. Antoine Guisan, Directeur de thèse
Dr Nicolas Salamin, Co-directeur de thèse
Dr Richard Grenyer, Expert
Prof. Luca Fumagalli, Expert
Abstract Understanding how biodiversity is distributed is central
to any conservation effort and has
traditionally been based on niche modeling and the causal
relationship between spatial
distribution of organisms and their environment. More recently, the
study of species’
evolutionary history and relatedness has permeated the fields of
ecology and conservation
and, coupled with spatial predictions, provides useful insights to
the origin of current
biodiversity patterns, community structuring and potential
vulnerability to extinction.
This thesis explores several key ecological questions by combining
the fields of niche
modeling and phylogenetics and using important components of
southern African
biodiversity. The aims of this thesis are to provide comparisons of
biodiversity measures, to
assess how climate change will affect evolutionary history loss, to
ask whether there is a clear
link between evolutionary history and morphology and to investigate
the potential role of
relatedness in macro-climatic niche structuring.
The first part of my thesis provides a fine scale comparison and
spatial overlap quantification
of species richness and phylogenetic diversity predictions for one
of the most diverse plant
families in the Cape Floristic Region (CFR), the Proteaceae. In
several of the measures used,
patterns do not match sufficiently to argue that species
relatedness information is implicit in
species richness patterns.
The second part of my thesis predicts how climate change may affect
threat and potential
extinction of southern African animal and plant taxa. I compare
present and future niche
models to assess whether predicted species extinction will result
in higher or lower
phylogenetic diversity survival than what would be experienced
under random extinction
processes. I find that predicted extinction will result in lower
phylogenetic diversity survival
but that this non-random pattern will be detected only after a
substantial proportion of the
taxa in each group has been lost.
The third part of my thesis explores the relationship between
phylogenetic and
morphological distance in southern African bats to assess whether
long evolutionary
histories correspond to equally high levels of morphological
variation, as predicted by a
4
neutral model of character evolution. I find no such evidence; on
the contrary weak negative
trends are detected for this group, as well as in simulations of
both neutral and convergent
character evolution.
Finally, I ask whether spatial and climatic niche occupancy in
southern African bats is
influenced by evolutionary history or not. I relate divergence time
between species pairs to
climatic niche and range overlap and find no evidence for clear
phylogenetic structuring. I
argue that this may be due to particularly high levels of
micro-niche partitioning.
5
Résumé
Comprendre la distribution de la biodiversité représente un enjeu
majeur pour la
conservation de la nature. Les analyses se basent le plus souvent
sur la modélisation de la
niche écologique à travers l’étude des relations causales entre la
distribution spatiale des
organismes et leur environnement. Depuis peu, l'étude de l'histoire
évolutive des organismes
est également utilisée dans les domaines de l'écologie et de la
conservation. En combinaison
avec la modélisation de la distribution spatiale des organismes,
cette nouvelle approche
fournit des informations pertinentes pour mieux comprendre
l'origine des patterns de
biodiversité actuels, de la structuration des communautés et des
risques potentiels
d'extinction.
Cette thèse explore plusieurs grandes questions écologiques, en
combinant les domaines de
la modélisation de la niche et de la phylogénétique. Elle
s’applique aux composants
importants de la biodiversité de l'Afrique australe. Les objectifs
de cette thèse ont été 1) de
comparer différentes mesures de la biodiversité, 2) d'évaluer
l’impact des changements
climatiques à venir sur la perte de diversité phylogénétique, 3)
d’analyser le lien potentiel
entre diversité phylogénétique et diversité morphologique et 4)
d’étudier le rôle potentiel de
la phylogénie sur la structuration des niches macro-climatiques des
espèces.
La première partie de cette thèse fournit une comparaison spatiale,
et une quantification du
chevauchement, entre des prévisions de richesse spécifique et des
prédictions de la diversité
phylogénétique pour l'une des familles de plantes les plus riches
en espèces de la région
floristique du Cap (CFR), les Proteaceae. Il résulte des analyses
que plusieurs mesures de
diversité phylogénétique montraient des distributions spatiales
différentes de la richesse
spécifique, habituellement utilisée pour édicter des mesures de
conservation.
La deuxième partie évalue les effets potentiels des changements
climatiques attendus sur les
taux d’extinction d’animaux et de plantes de l'Afrique australe.
Pour cela, des modèles de
distribution d’espèces actuels et futurs ont permis de déterminer
si l'extinction des espèces se
traduira par une plus grande ou une plus petite perte de diversité
phylogénétique en
6
comparaison à un processus d'extinction aléatoire. Les résultats
ont effectivement montré
que l'extinction des espèces liées aux changements climatiques
pourrait entraîner une perte
plus grande de diversité phylogénétique. Cependant, cette perte ne
serait plus grande que
celle liée à un processus d’extinction aléatoire qu’à partir d’une
forte perte de taxons dans
chaque groupe.
La troisième partie de cette thèse explore la relation entre
distances phylogénétiques et
morphologiques d’espèces de chauves-souris de l’Afrique australe.
Il s’agit plus précisément
de déterminer si une longue histoire évolutive correspond également
à des variations
morphologiques plus grandes dans ce groupe. Cette relation est en
fait prédite par un modèle
neutre d'évolution de caractères. Aucune évidence de cette relation
n’a émergé des analyses.
Au contraire, des tendances négatives ont été détectées, ce qui
représenterait la conséquence
d'une évolution convergente entre clades et des niveaux élevés de
cloisonnement pour
chaque clade.
Enfin, la dernière partie présente une étude sur la répartition de
la niche climatique des
chauves-souris de l’Afrique australe. Dans cette étude je rapporte
temps de divergence
évolutive (ou deux espèces ont divergé depuis un ancêtre commun) au
niveau de
chevauchement de leurs niches climatiques. Les résultats n’ont pas
pu mettre en évidence de
lien entre ces deux paramètres. Les résultats soutiennent plutôt
l’idée que cela pourrait être
dû à des niveaux particulièrement élevés de répartition de la niche
à échelle fine.
7
Aknowledgements First and foremost I would like to thank my two
supervisors Antoine Guisan and Nicolas Salamin, for their guidance,
their patience and their competence. Working with them was in more
ways than one a privilege and I thank them for all that they have
taught me in the last few years. Amongst other things, I greatly
admire how they both balance very busy professional and private
lives and still manage to be such thoroughly pleasant people. My
gratitude goes to the two examiners Richard Grenyer and Luca
Fumagalli who kindly agreed to participate in the reviewing process
of this thesis and for all the constructive criticism they already
provided during the intermediate evaluation. Amongst the people
without whom this thesis would never have been written I would like
to mention Robin Engler and Olivier Broennimann, I owe them a few
hundred beers for their time, their patience, their infinite
expertise and their British sense of humour. I would also like to
thank Peter Pearman, Julien Pottier, Gwenaëlle Lelay, Christophe
Randin, Luigi Maiorano and Blaise Petitpierre for their stimulating
conversation, support and advice. Pascal Vittoz, Glenn Litsios,
Patricio Pliscoff, Anne Dubuis, Loïc Pellissier, Maryam Zaheri,
Charlotte Ndiribe and Anna Kostikova all contributed to making one
of the best working environments anyone could hope for. My labwork
would not have been possible without the expertise and help of
Nadia Bruyndonckx, Nelly di Marco, Dessislava Savova Bianchi, Chloe
Andrey, Sabrina Joye, Pascal-Antoine Christin and Guillaume
Besnard. I would like to thank the students I had over the past
couple of years for helping me discover the pleasure of teaching,
for being keen learners and for challenging me. I would like to
express my gratitude to all the external collaborators I had the
pleasure of working with during my thesis. In particular, my thanks
go to Ara Monadjem, Michael Curran and Mirjam Kopp who accompanied
me on tough but fabulous bat catching trips to southern Africa.
Amongst the many people whose friendliness softened the culture
shock when I first moved to Switzerland, Christophe Randin, Daniel
Croll, Philippe Christe, Nicole Galland and Luc Gigord definitely
stand out. My thanks also go to France Pham, Virginie Cantamessa,
Felicidad Jaquiéry, Giuseppina Rota, Marinette Donadeo and Corinne
Bolle who tirelessly helped me navigate the local bureaucracy and
FBM doctoral school requirements. Thanks to all of the Hotspots
project students, for their energy, their drive, their eccentric
ways and their quirky sense of humour, which made me feel
comfortable from the start. I also thank the Hotspots Consortium
for accepting me onto their programme and giving me this amazing
opportunity. A big thanks goes to Tropical Biology Association
(TBA) director Rosy Trevelyan, who organized two courses I was
lucky enough to go on as part of the Hotspots applied conservation
training programme. Her commitment to capacity building for
conservation in developing countries, her infinite energy and
indomitable character are a huge inspiration.
8
Other people who have inspired me over the last few years are: Koen
Meyers, Alfie Alexander, Siti Rachmania, Paul Racey, Frank Clarke
and William Sutherland. Outside the University I would like to
thank Carole Revelly, Marielle Fraser, Duncan Fraser, Christine de
Luca, Anne-Laure Pernet of Yogaworks, and Sarah Zahno of Khatoon
Dance, who have taught me so much and made life in Lausanne that
much more enjoyable. Amongst the people who made my stay in
Lausanne particularly special I would like to mention Sébastian
Gay, Krister Swenson, Marie-Noëlle Wurm, Daniele Fraboulet, Cedric
Wurm, Christopher Cianci, Carmen Cianfrani, Federica Sandrone,
Paroma Basu and Rajat Mukherjee. Amongst my dearest friends here in
Lausanne it would be impossible not to mention Karen Sanguinet.
Thanks for sharing pain and joy for the past few years. Saya cinta
anda. I would like to thank my parents, Anna and Julian and my two
sisters, Miranda and Carolina, who have come to accept my
restlessness over the past 12 years, I thank you for your
encouragement, your faith, your patience and most of all for your
unconditional and unwavering love. Finally, I thank Yannick for
being so loving, understanding and for being my rock during these
last few months.
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Chapter 1 - Spatial predictions of phylogenetic diversity challenge
conservation decision making 27
Chapter 2 - Climate change effects on phylogentic diversity
50
Chapter 3 - Exploring the relationship between morphology and
phylogenetic diversity 72
Chapter 4 - No macro-climatic niche conservatism in southern
African bats 83
Conclusions 104
Annexes - A recent inventory of the bats of Mozambique with
documentation of seven new 112 species to the country Bats of
Borneo: diversity, distributions and representation in protected
areas 149
10
Introduction Studying how the components of diversity are related
to each other and spatially distributed
is relevant to conservation for several reasons. Firstly, an
understanding of the evolutionary
mechanisms which have generated and currently rule diversity
patterns is essential if we are
to ensure their future through conservation. Secondly, knowledge of
how particular lineages
have responded to challenges in the past may help us understand how
they now respond or
will soon respond to environmental changes. Thirdly, the way
diversity is spatially and
climatically distributed can tell us a lot about species
requirements, community structuring
and potential vulnerability to such changes. This thesis explores
the relationship between
spatial and phylogenetic patterns of several biodiversity
components in southern Africa, a
region of high biogeographic and conservation interest.
In this introductory chapter I summarize some of the key findings
on the origins of current
diversity patterns in the southern African region. I then describe
some of our knowledge
about past and present biodiversity loss. Finally, I illustrate how
the study of species
distributions has gradually merged with the study of evolutionary
relationships to understand
why specific biodiversity patterns establish, why some taxa occupy
the niches they do and
why certain species may go extinct before others.
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The southern African biodiversity hotspots Understanding the
origins of diversity can assist in its protection by contrasting
current and
historical patterns. Ultimately such understanding can help
preserve the conditions required
for the establishment of diverse communities. Why are some areas so
much more diverse
than others? What are the respective roles of environmental and
historical factors in the
radiation of particularly diverse clades? We have few satisfactory
answers to these and other
questions, but we do know that past climate change and refugia
locations have had an
enormous impact on how diversity is distributed today (Moritz et
al., 2005). Furthermore, we
know that lineages often differ in their evolutionary responses to
the same environmental
history, thus complicating the use of one lineage as a surrogate
model for another (Moritz et
al., 2005).
Southern Africa contains 4 out of 7 biodiversity hotspots
identified on the African continent
(Myers et al., 2000). These are the coastal forests of eastern
Africa, Mapotaland-Pondoland-
Albany, the Succulent Karoo and the Cape Floristic Region (CFR).
All, by definition, display
very high levels of floral species richness, endemism and have lost
over 70% of their original
extent due to human activities. The flora of the south-western tip
of southern Africa is made
up of over 9,000 species in an area of 90,000 km2 and is much more
speciose than would be
expected from its area or latitude (Goldblatt, 1978). Endemism
levels of almost 70% are
comparable only to those found on islands (Linder, 2003) and most
likely accounted for by
the ecological and geographical isolation of the CFR (Linder,
2003). Explanations for the
high species richness, resulting from extreme radiation of 33 Cape
floral clades, however are
harder to find (Linder et al., 1992; Linder & Hardy, 2004;
Linder, 2005; Linder, 2008;
Verboom et al., 2009; Valente et al., 2010).
The historical events underlying the origin of this diversity, as
well as the time frame over
which it occurred, have been the subject of considerable debate in
the literature (Levyns,
1964; Linder et al., 1992; Linder, 2003; Linder et al., 2005). A
recent study using succulent
karoo- and fynbos-endemic lineages across 17 groups of plants,
found that all succulent
karoo-endemic lineages are less than 17.5 My old, the majority
being younger than 10 My
(Verboom et al., 2009). This is largely consistent with suggestions
that this biome is the
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product of recent radiation in the late Miocene (Levyns, 1964). In
contrast, the even richer
fynbos-endemic lineages were found to display a broader age
distribution, with some
lineages originating in the Oligocene, but most being more recent
(Verboom et al., 2009).
The massive speciation in the Cape flora might be due to genetic
isolation because of a
topographically and climatically heterogeneous landscape,
availability of many pollinators, a
long flowering season, as well as a regular fire regime (Goldblatt,
1978; Linder & Ferguson,
1985; Linder, 1995; Bakker et al., 2005). Though all of these
factors are likely to have played
a role, climate continues to be considered the main trigger for
this radiation (Levyns, 1964;
Linder, 2003). Levyns (1964) was the first to suggest that the
remarkable plant species
diversity of the western Cape was the result of elevated speciation
following the onset of arid
climates in the area, which started around the end of the Miocene.
This may have led to
widespread extinction, opening a variety of empty niches into which
lineages which were
pre-adapted to survive summer aridity were able to diversify.
However, more recent studies
have estimated the start of the origin and radiation of several
Cape lineages to be well before
the late Miocene (Linder and Hardy, 2004; Bakker et al., 2005;
Linder, 2005), when climates
were presumably moister than they are at present. It is possible
that much radiation may
have happened in high-altitude environments which support the
greatest fynbos plant
species richness as well as the highest concentrations of local
endemics, a pattern that may
partly be a result of reduced extinction in the past (Cowling &
Lombard, 2002). It is also in
these environments that most of the region’s palaeoendemic taxa
occur (Linder et al., 1992).
Past and present biodiversity loss Both speciation and extinction
are heavily affected by climate change (Erwin, 2001; Linder,
2003; Midgley et al., 2005; Barnosky, 2008; Erwin, 2009).
All of the five mass extinction events have been related to large
scale climatic changes, such
as sea level fluctuations, which resulted from extensive global
warming in the first mass
extinction and global cooling after bolide impacts in the second
mass extinction (Erwin,
2001; Erwin, 2009). During the Late Permian, a combination of drop
in atmospheric oxygen
and climate warming (supposedly caused by another bolide impact and
subsequent volcanic
13
activity) is thought to have induced hypoxic stress and compressed
altitudinal ranges to near
sea level with consequent habitat fragmentation and population
isolation effects (Huey &
Ward, 2005).
A period of climatic oscillations that began about 1 Mya, during
the Pleistocene, was
characterized by glaciations alternating with episodes of glacial
melting (Barnosky, 2008).
The current episode of global warming can be considered as an
extreme and extended
interglacial period; however, most geologists treat this period as
a separate epoch, the
Holocene, which began ~11,000 years ago at the end of the last
glaciation. The Holocene
extinctions were greater than occurred in the Pleistocene,
especially with respect to large
terrestrial vertebrates. These are also the only major extinctions
that took place when
humans were on the planet and occurred during a global warming
episode at a time when
human populations were rapidly expanding (Fig. 1). Around 20,000
years ago megafauna
biomass collapsed at the same time human biomass started increasing
exponentially, reached
a new lower plateau ~10,000 years ago and has not recovered (Fig.
2). Recent studies suggest
that human impacts such as hunting and habitat alteration
contributed in many places to
extinction events, and that climate change exacerbated them
(Barnosky, 2008).
Figure 1: Number of non-human magafauna species that went extinct
through time plotted against estimated population growth of humans
(from Barnosky, 2008).
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Figure 2: Estimated biomass of humans plotted against the estimated
biomass of non- human megafauna (from Barnosky, 2008).
The Holocene extinctions take on special significance in
understanding the potential
outcomes of similar kinds of pressures on biodiversity today: the
exponential growth of
human populations at the same time as the Earth is warming at
unprecedented rates.
The possibility that a new mass extinction spasm is upon us has
received much attention.
Many scientists argue that we are either entering or in the midst
of the sixth great mass
extinction and that it may be largely triggered by human activities
(Wilson, 1988; Leakey &
Lewin, 1995).
Causes of current biodiversity loss The latest update of the IUCN
Red List of Threatened Species shows that 17,291 species out
of the 47,677 assessed species are threatened with extinction and
that 875 are already extinct
or extinct in the wild (IUCN, 2009).
The well known causes of present biodiversity loss are multiple,
but almost all inextricably
linked to poverty and human population growth in developing
countries, as well as
disproportionately high per capita resource consumption and
inadequate technological
advancement in wealthier countries. Together with the realisation
that local actions anywhere
in the world have global repercussions for biodiversity and human
survival, climate change
15
has become more and more prevalent in the popular and scientific
literature (Biello; Lewis,
2006; Ott et al., 2008; Levi, 2009; Pettorelli et al., 2009; Veron
et al., 2009).
Climate change is a major cause of biodiversity loss in southern
Africa, partly because it
exacerbates the effects of land use change and introductions of
exotic species. Temperatures
have risen in this region by approximately 1 degree over the past
100 years, which is 0.3
degrees higher than the world average (IPCC, 2007). There is now
evidence that many
species are disappearing from the northern parts of their ranges.
In addition, there is
experimental evidence that the recorded expansion of woody
invasions into grasslands and
savannas may be driven by rising global CO2 concentrations
(Millennium Ecosystem
Assessment, 2005). The ability of native species to disperse and
survive these pressures will
be hampered by a severely fragmented landscape (Bomhard et al.,
2005; Midgley et al., 2006).
Major losses in many southern African mammal species are predicted
in the next 40 to 70
years as a result of climate change, as well as an eastward shift
of mammal diversity (Thuiller
et al., 2006). These results suggested that the effects of climate
change on wildlife
communities may be most noticeable not only as substantial loss of
species from their
current ranges, but also as a fundamental change in community
structure, as species
associations shift with influxes of new colonisers (Thuiller et
al., 2006). The Cape Floristic
Region and the Succulent Karoo are also predicted to lose more than
41% of endemic plant
species richness and undergo 39% range reduction by 2050
(Broennimann et al., 2006).
The effects of a warming climate are magnified by human landuse.
Forests and woodlands
are converted to croplands and pastures at a very fast rate. Half
of the southern African
region consists of drylands, where overgrazing is the main cause of
desertification
(Millennium Ecosystem Assessment, 2005). The spread of oil palm in
the upper limits of
southern Africa as well as South-East Asia is another example of
landuse with strong effects
on local climates. African oil palm, Elaeis guineensis, is grown
across more than 13.5 million ha
of tropical, low-lying areas, a zone naturally occupied by moist
tropical forest, one of the
most biologically diverse terrestrial ecosystems on Earth (Corley
& Tinker, 2003;
MillenniumEcosystemAssessment, 2005). Vegetable oils are among the
most rapidly
16
expanding agricultural sectors (EC, 2006), and more palm oil is
produced than any other
vegetable oil (Corley & Tinker, 2003). Global palm oil
production increased by 55% between
2001 and 2006 (http://faostat.fao.org), prompted largely by
expanding biofuel markets in
the European Union (MillenniumEcosystemAssessment, 2005) and by
food demand globally
(EC, 2006). Some of the largest multinationals worldwide, including
Nestlé, Unilever and
Dove, make abundant use of palm oil in their processed food and
beauty products, as it is
far cheaper than any other oil on the market (Fitzherbert et al.,
2008). In palm oil
plantations, 85% of the pre-existing vertebrate and invertebrate
communities are unable to
persist and go locally extinct (Fitzherbert et al., 2008). The
species lost include species with
the most specialised diets, those reliant on habitat features not
found in plantations, those
with the smallest range sizes and those of highest conservation
concern (Chung et al., 2000;
Corley & Tinker, 2003; Aratrakorn S. et al., 2006). Plantation
assemblages are typically
dominated by a few abundant generalists, non-forest species
(including alien invasives) and
pests (Chung et al., 2000; Corley & Tinker, 2003; Aratrakorn S.
et al., 2006).
Niche modeling meets phylogenetics Niche or species distribution
modeling has traditionally been one of the most powerful
tools
in conservation science (Vaughan et al., 2003; Rushton et al.,
2004; Guisan & Thuiller, 2005).
These empirical models relate field observations to environmental
predictor variables to
identify current and future species distributions (Guisan &
Zimmermann, 2000; Guisan &
Thuiller, 2005). At the core of species distribution models is the
concept of the “ecological
niche”, the theoretical framework to the quantification of the
relationship between species
and their environment (Austin et al., 1990; Araujo & Guisan,
2006). The concept of niche as
used in niche models was formalized by Hutchinson (1957) as the
ensemble of
environmental conditions under which populations of a species can
maintain a positive
growth rate. At this time an important distinction between
“fundamental” and “realized”
niches was made. In the “fundamental” niche abiotic factors only
(such as climate and
topography) are taken into account whilst both biotic (such as
competition and facilitation)
and abiotic factors make up the “realized” niche (Hutchinson,
1957). Since they are
calibrated from field observations of species that include the
effects of biotic interactions,
niche models capture an approximate realized niche
(Jimenez-Valverde et al., 2008).
17
Some of the major applications of niche models (Guisan &
Thuiller, 2005; Franklin, 2010)
include improving the likelihood of identifying the location of
rare species (Engler et al.,
2004; Guisan et al., 2006; Le Lay et al., in review), predicting
the susceptibility of a particular
area to invasive species (Thuiller et al., 2005c; Broennimann et
al., 2007) and predicting how
species will shift their distributions as a result of climate
change (Thuiller et al., 2005b;
Randin et al., 2009).
There have been substantial improvements to niche models in terms
of accounting for
dispersal (Engler & Guisan, 2009; Engler et al., 2009) and
increasingly for species
interactions (Araujo & Luoto, 2007). A major drawback of using
niche models to predict
future distributions is that they generally assume either no
dispersal at all or unlimited
dispersal (i.e. the species occupies all potentially suitable
habitat; e.g. (Thomas et al., 2004;
Engler et al., 2009). Inevitably these two options provide
unrealistic scenarios of plant
dispersal. Recently, models have started to account for a large
number of parameters such as
seed dispersal, evolution of a population’s reproductive potential
over time, stochastic long
distance dispersal events, barriers to dispersal, random population
extinctions, vegetative and
seed-bank resilience to environmental change and differential
dispersal along rivers or roads
(Engler & Guisan, 2009; Thuiller et al., 2009b).
Phylogenetics, traditionally used by systematists, is the science
of species’ evolutionary
relationships and their reconstruction into phylogenetic trees. The
explosion of molecular
phylogenetics in the last couple of decades has been triggered by
the emergence of new
molecular methods and statistical techniques and has been used to
address a wide array of
important evolutionary and ecological questions. Phylogenetics has
been used to identify the
presence of cryptic species (Bode et al., 2010; Schonhofer &
Martens, 2010), to test
speciation and biogeographic hypotheses (Moritz et al., 2005;
Oliveros & Moyle, 2010;
Thinh et al., 2010) and to understand why some species may be
better biological invaders
than others (Strauss et al., 2006). It has also been useful to
retrace switches in evolutionary
history, the rise of key adaptations and whether these made single
or parallel appearances
(Christin et al., 2007; Christin et al., 2008).
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The overlap between the science of species distributions and that
of species evolutionary
relationships has taken several interesting directions. Firstly,
niche models and phylogenies
are increasingly coupled to study speciation patterns (Hugall et
al., 2002; Savolainen et al.,
2006; Carnaval et al., 2009; Malay & Paulay, 2009).
Secondly, species distribution data and phylogenies have been used
to study the relationship
between biodiversity measures. Traditionally, the units in
conservation biology have been
species, which provide an intuitive measure to compare biodiversity
at different sites.
However, species are not equivalent in the amount of evolutionary
history they contribute to
a community and it has been argued by many authors that they should
not be considered as
equal conservation units. Phylogenetics made one of its first
contributions to conservation
biology with the introduction of phylogenetic diversity (PD)
(Faith, 1992a), a measure of
diversity which takes evolutionary relationships into account. The
important question of
how species richness and phylogenetic diversity patterns compare
(and thus whether most of
the past conservation efforts based on species richness have
intrinsically incorporated
evolutionary history or not) has been examined by several authors
with different methods.
Some studies found a tight relationship between patterns of SR and
PD (Rodrigues &
Gaston, 2002), while others found significant discrepancies
(Rissler et al., 2006; Forest et al.,
2007), but in general little attention has been paid to how these
two measures overlap
spatially.
Thirdly, species distributions and phylogenies have been used to
study niche evolution. A
large body of literature still disagrees as to whether closely
related species strive to partition
resources by differentiating their ecological niches or whether
they tend to conserve more
similar niches (Peterson et al., 1999; Losos et al., 2003; Graham
et al., 2004; Knouft et al.,
2006; Losos, 2008a; Pearman et al., 2008). Because characters are
assumed to evolve
following a neutral model, most studies expect close relatives to
occupy similar niches
(Losos, 2008a). However, theory and practice do not always match
and the evidence for this
pattern in nature is limited and controversial (Peterson et al.,
1999; Losos & Glor, 2003; Rice
et al., 2003; Knouft et al., 2006).
19
Together with life history traits, phylogenies have been related to
the level of threat
experienced by many species (Purvis et al., 2000; Purvis et al.,
2005; Davies et al., 2008; Fritz
et al., 2009). Considerable attention has been devoted to
investigate rarity patterns and
whether extinctions within a particular clade or taxon are
generally random or
phylogenetically clumped (Purvis et al., 2000; Sakai et al., 2002;
Pilgrim et al., 2004; Sjostrom
& Gross, 2006; Vamosi & Vamosi, 2007; Vamosi & Wilson,
2008). If extinction risk were
indeed mostly phylogenetically clumped as argued for some bird,
mammal and plant groups
(Purvis et al., 2000; Vamosi & Wilson, 2008) this could have
very dramatic consequences on
evolutionary history loss, especially within hotspots of diversity.
So far estimates have been
made for the present, but very little attention has been paid to
what consequences climate
change may have on future loss of evolutionary histories. Only
through spatially explicit
niche modeling will this be possible.
Finally, patterns of phylogenetic relatedness within communities
have been widely used to
infer the importance of different ecological and evolutionary
processes during community
assembly (Kembel, 2009) and are increasingly used in combination
with niche modeling to
make powerful predictions in community ecology.
Main aims and thesis structure The general aim of this thesis is to
answer several questions relating to diversity patterns and
evolutionary history of southern African animal and plant taxa.
More specifically, my aims
are to provide the first spatial comparison of species richness and
phylogenetic diversity
predictions, to assess how much phylogenetic diversity may be lost
in the future, to ask
whether there is a clear link between evolutionary history and
morphology and to investigate
the structure and stability of climatic niches. The thesis
structure is as follows:
Chapter 1: Spatial predictions of phylogenetic diversity challenge
conservation decision
making
I quantify spatial overlap of species richness and phylogenetic
diversity predictions in an
extremely diversified plant family found in the Cape region: the
Proteaceae.
20
Chapter 2: Climate change effects on phylogenetic diversity
I compare present and future predictions for several animal and
plant taxa to assess how
species extinctions will affect evolutionary history loss.
Chapter 3: Exploring the relationship between phylogenetic
diversity and
morphology
I compare phylogenetic diversity measures to morphological
disparity in a diverse bat
community to evaluate whether phylogenetic and morphological
distances can be thought of
as interchangeable.
Chapter 4: Are climatic niches conserved?
I present the first species level phylogeny for southern African
bats and employ it to
determine the extent to which spatial and climatic partitioning is
influenced by evolutionary
relationships.
Conclusions
In this section, I recapitulate the main findings of each chapter
and discuss some of the
limitations, as well as how an understanding of evolutionary
history may best contribute to
conservation in the future.
Annexes
I include two studies to which I contributed during my
doctorate.
21
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27
decision making Dorothea V. Pio1,2, Olivier Broennimann1, Timothy
G. Barraclough3, , Gail Reeves4,5,
Anthony G. Rebelo5, Wilfried Thuiller6, Antoine Guisan1*, and
Nicolas Salamin1,2
1Department of Ecology and Evolution, University of Lausanne, 1015
Lausanne,
Switzerland
2Swiss Institute of Bioinformatics, University of Lausanne, 1015
Lausanne, Switzerland
3Division of Biology and NERC Centre for Population Biology,
Imperial College London,
Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK
4Jodrell Laboratory, Royal Botanic Gardens, Kew, TW9 3DS, UK
5Protea Atlas Project, South African National Biodiversity
Institute, P/Bag X7, Claremont
7735, Cape Town, South Africa
6Laboratoire d'Ecologie Alpine, CNRS, Université Joseph Fourier, BP
53, 38041 Grenoble
Cedex 9, France
28
Abstract The inclusion of a measure of evolutionary history and
relatedness (phylogenetic diversity) in
conservation has long been argued as an important step towards
preserving biodiversity in a
more meaningful and comprehensive way. Some of the studies that
have addressed this issue
find that phylogenetic diversity patterns do not differ enough from
those of species richness
to justify their inclusion in conservation planning. This
conclusion, however, is often
reached by correlating these two measures across a series of sites
without paying much
attention to their spatial patterns. Here, we compared fine-scale
species richness and
phylogenetic diversity predictions of a diverse plant family, the
Cape Proteaceae, obtained
through individual species distribution models and ten different
phylogenetic diversity
indices. We examined their correlations, spatial patterns of
overlap and performance in a
complementarity algorithm. Overlap was found to vary enormously
among phylogenetic
indices, but discrepancies existed for most measures when
considering realistic amounts of
land set aside for conservation. Climate explained in part the
segregation of particularly
species rich versus phylogenetically rich areas. In view of our
results, the gradual breakdown
in the species concept and an increased availability of molecular
data, we encourage
conservation prioritization to take advantage of the additional
information provided by
phylogenetic diversity.
Keywords: phylogenetic diversity, species richness, Proteaceae,
spatial overlap, South Africa, conservation planning, predictive
modeling, Angiosperms.
Contribution to the project: I carried out the analyses in
collaboration with O.B. and N.S., produced figures and wrote the
paper. This paper is currently in review
29
Introduction
Allocation of funds for nature conservation relies heavily on
prioritisation exercises. The
budgets in most environmental organizations are very limited,
making the use of the most
meaningful criteria a number one priority in the design of
protected area networks (Bottrill
et al., 2008). In an effort to provide more realistic and
comprehensive examples for
conservation practice, several authors have argued for the
inclusion of costs, ecosystem
services, potential human-wildlife conflicts and other
socio-economic factors (Moore et al.,
2004; Eigenbrod et al., 2009). However, the primary purpose of
these prioritization exercises
is the identification of the most biologically rich and unique
areas still existing today. In this
regard, the inclusion of evolutionary history in conservation,
through the calculation of
phylogenetic diversity has long been argued as an important step
towards preserving
biodiversity in a more meaningful and comprehensive way (Faith,
1992a). Species are not
equal in the amount of evolutionary history they bring to their
community and should not, as
such, be considered equivalent conservation units. Biodiversity
hotspots (Myers et al., 2000)
for example contain a higher proportion of species characterized by
exceptionally long and
unique evolutionary histories (Sechrest et al., 2002). If
phylogenetic diversity patterns were
found to match those of species richness, there would be no reason
to use phylogenetic
diversity measures in conservation prioritization, as species
richness will always be easier,
cheaper and quicker to measure. Some studies have found a tight
relationship between
patterns of species richness and phylogenetic diversity (Rodrigues
& Gaston, 2002; Schipper
et al., 2008), while others have found significant discrepancies
(Rissler et al., 2006; Forest et
al., 2007). However, as a general rule it seems that species
richness is a bad surrogate for
phylogenetic diversity only when species restricted to species poor
areas correspond to the
ancient branches of an “unbalanced” tree (i.e. containing long
ancient branches which
account for a disproportionate amount of phylogenetic diversity;
Rodrigues & Gaston,
2002).
Phylogenetic methods have mostly been applied across a limited
number of systems and
spatial scales, and often at the genus rather than species levels
(Rodrigues & Gaston, 2002;
Forest et al., 2007; Proches et al., 2009) with notable exceptions
(Winter et al., 2009). Many
30
global studies use incomplete and coarse data to identify areas for
conservation. While these
exercises may be useful for resource allocation at a country level,
the conclusions they reach
and their use at a finer geographical scale are limited. If we are
to incorporate phylogenetic
diversity information into practical conservation prioritization
efforts, it is of prime
importance that we test whether phylogenetic diversity measures are
congruent with species
richness, using appropriate spatially-explicit species level data.
The most recent and one of
the most thorough studies examining the relationship between
species richness and
phylogenetic diversity found a decoupling of taxon richness and
phylogenetic diversity for
plant genera in the Cape Floristic Region (Forest et al. 2007).
Furthermore, by means of a
complementarity algorithm, this study illustrated that within a
conservation planning context,
gains in phylogenetic diversity are poorly matched by gains in
taxon richness (Forest et al.
2007).
In this study, our aim was to assess the relationship between
spatial predictions of species
richness and several phylogenetic diversity indices by
investigating how correlated they are
and by examining their spatial patterns of overlap. Since there is
considerable variation in the
way evolutionary history is measured and we wanted this analysis to
be as comprehensive as
possible, we employed all ten phylogenetic diversity measures
recently listed by Schweiger et
al (2008). Our aim was also to conduct the first species level
analysis (as conservation still
mostly operates on this scale) and to relate potential
discrepancies between species richness
and phylogenetic diversity patterns to environmental gradients
present in the study area. As a
model group, we use the Proteaceae, an ancient Gondwanan plant
family with fossils
attributed to extant genera from the mid-Cretaceous (Drinnan et
al., 1994; Dettmann &
Jarzen, 1996). This group is found in South-Africa’s Cape Floristic
Region, a biodiversity
hotspot containing one of the highest levels of species richness
and endemism of any known
tropical or temperate area (Myers et al., 2000; Linder, 2003).
These extremely diverse, low-
growing shrubs and trees include over 330 species (Cowling &
Lamont, 1998), and present a
wide variety of pollination and fire survival strategies (Rebelo,
2001). Of the 13 genera
occurring in mainland Africa, ten are almost entirely endemic to
the fynbos vegetation of the
south-western Cape (Barker, 2002). The Cape Floristic Region
contrasts with other high-
diversity areas, such as tropical forests as it is made up of
dissimilar local communities, in
31
which most species are relatively abundant and very few are rare
(Latimer et al., 2005). This
pattern can be explained by examining migration rates in the
fynbos, which are two orders of
magnitude lower than in tropical forests, and speciation rates of
this vegetation type, which
are higher than in any previously studied plant system (Latimer et
al., 2005). The interesting
evolutionary history, high diversity and excellent quality of both
genetic and occurrence data
available for Proteaceae in South Africa make this group an ideal
model for the study of
spatial patterns of overlap between phylogenetic diversity measures
and species richness.
Materials and Methods
Predicting species distributions
Niche modeling was employed to obtain all-inclusive and
wide-ranging predictions of likely
species distributions at a fine scale in the Cape Floristic Region.
Though the occurrence data
for this group is extensive and of excellent quality, its coverage
does not include 100% of the
regions of the Cape Floristic Region. Niche modeling was therefore
necessary to provide a
probability distribution of the occurrence of each Proteaceae
species over the whole Cape
Floristic Region. We built species distribution models at a
resolution of 1’ × 1’ (~1.6 × 1.6
km at this latitude) for 168 endemic or near endemic Proteaceae
species (the availability of
both occurrence and genetic data was necessary in order to include
species in the study) and
occurring in more than 20 mapping cells. Species distribution data
were taken from the
Protea Atlas Project (PAP) database, comprising field-determined
species presence and
absence observations at more than 40,000 geo-referenced locations.
Generalised Additive
Models (Hastie & Tibshirani, 1990) were calibrated in the
Splus-based BIOMOD application
(Thuiller, 2003) using seven bioclimatic variables. These variables
were derived from the
Worldclim database for the Cape region and included annual
evapo-transpiration,
evapotranspiration of the wettest quarter, annual precipitation,
precipitation of the wettest
quarter (May to August), precipitation of the driest quarter
(November to February), annual
temperature and temperature of the coldest quarter (May to August).
A random sample of
the initial data (70%) and a stepwise selection methodology
(forwards and backwards) were
employed to identify the best model using the Akaike information
criterion (AIC) as a
selection criterion. The predictive power of each model was
evaluated on the remaining 30%
32
of the initial dataset using the values obtained for the area under
the curve (AUC) of a
receiver operating characteristic (ROC) plot (Fielding & Bell,
1997).
The probabilities of occurrence were filtered with a measure of
anthropogenic disturbance,
the “human footprint”, considered as a regionally consistent way to
represent land
transformation on a global scale (Sanderson et al., 2002).
Predictions for individual species distribution models were summed
at each site to obtain
species richness predictions, which were in turn used to calculate
corresponding values for
various phylogenetic diversity indices. Modeled distributions were
therefore the basis for
both species richness and phylogenetic diversity predictions used
throughout this study.
Calculation of phylogenetic diversity indices:
A calibrated phylogenetic tree for the Proteaceae family based on
23 genes was assembled
from pre-existing data (ITS, Reeves, Barraclough et al, unpublished
data) and all other
available sequences for the South African (and some Australian)
Proteaceae in GenBank
(McMahon & Sanderson, 2006). The tree comprising 284 species
was built using MrBayes
3.1.2 (Huelsenbeck et al., 2001). Two runs of four Markov chain
Monte Carlo chains were
run for 10 mio generations using the GTR+Gamma model of DNA
evolution (as
determined by likelihood ratio tests) and default priors. The
convergence of the two runs
was assessed using Tracer (Drummond & Rambaut, 2007). The tree
with the highest
posterior probability was then dated with a penalized likelihood
method (Sanderson, 2002)
as implemented in the ape package (Paradis et al., 2004) in R using
previously described
fossils (Sauquet et al., 2009). To check the consistency of the
date estimates, we also ran
penalized likelihood on 100 randomly sampled trees from the
posterior distribution given by
MrBayes.
Phylogenetic diversity values for each of the grid cells on the map
of the study area were
calculated using each of the measures listed in Schweiger et al.
(2008). Calculations were
carried out with scripts in R based on the ape package (Paradis et
al., 2004). These measures
include topology indices, which are based on node information only
(W and Q) and pairwise-
33
distance (J, F, AvTD, TTD and Dd) as well as minimum-spanning-path
indices (PDroot,
PDnode, AvPD), which are based on both branch length and node
information. Moreover,
indices used in this study, can be subdivided into total indices
(Q, W, PDnode, PDroot, F,
TTD, Dd), which add the evolutionary history of all species present
in an area and averaged
measures (AvTD, J, AvTD), where total evolutionary history is
divided by the number of
species present. Details on the mathematical properties of each of
these measures can be
found in a summary table in Schweiger et al (2008).
Discrepancy values
Species richness and phylogenetic diversity indices were first
normalized. This consisted for
each of the two measures in subtracting the mean value and then
dividing it by the standard
deviation calculated from each grid cell. Species richness was then
subtracted from
phylogenetic diversity to obtain discrepancy values. Where these
values were above zero,
phylogenetic diversity was greater than species richness and where
they were below zero,
phylogenetic diversity was smaller than species richness.
Comparison between species richness and phylogenetic diversity by
correlation, spatial overlap and
complementarity algorithm:
In order to describe the relationship between species richness and
phylogenetic diversity in
the study area, a Spearman correlation was used for each
phylogenetic diversity measure. In
addition, we ran the complementarity algorithm developed by Forest
et al. (2007), a
traditional approach in reserve selection. This algorithm chooses
the most diverse grid cell
first and sequentially adds grid cells with the highest
complementary diversity (gain) until all
diversity is represented. This analysis investigates how gains in
phylogenetic diversity or
species richness may change as a function of which measure is
maximized and whether sites
selected by maximizing phylogenetic diversity or species richness
overlap spatially. Finally,
we quantified the spatial overlap between phylogenetic diversity
and species richness
measures when considering increasing amounts of land set aside for
conservation. For
increasing percentages of land considered, the richest grid cells
as measured by species
richness and phylogenetic diversity indices were identified. The
spatial overlap was then
calculated as the percent of common grid cells among those
identified by both measures,
34
paying particular attention to the values obtained for average
amounts of land set aside for
conservation (UNEP-WCMC, 2008).
PCA of environmental variables and quadratic regression
A PCA of the environmental variables used to predict individual
species distributions was
calculated in the R package ade4 (Franquet et al., 1995). The
scores corresponding to higher
normalized phylogenetic diversity or species richness values were
highlighted in different
colors to identify possible spatial segregation between the two
groups of scores. Following
this analysis we conducted a polynomial quadratic regression to
describe the relationship
between altitude and discrepancy values.
Results Correlations, complementarity analysis and discrepancy
values
Species distribution model accuracy was consistently excellent with
an average AUC of 0.98
over all species (range: 0.88-0.99). Spearman rho coefficients of
correlations between species
richness and phylogenetic diversity varied greatly between
phylogenetic diversity indices.
Topology measures scored high in their correlation to species
richness (0.98 to 0.99
Spearman rho for W and Q respectively), while methods using both
node and branch length
information showed considerable differences and ranged from -0.75
to 0.94 for minimum-
spanning-path methods (0.92, 0.94 and -0.72 for PDnode, PDroot and
AvPD respectively) and
from -0.06 to 0.99 for pairwise distance methods (-0.06, -0.03,
0.99, 0.98, and 0.7 for AvTD,
J, F, TTD and Dd respectively). Graphical checks (data not shown)
of these relationships
indicated that two of these correlations were linear (W and TTD).
Others were upward
sloping asymptotically (PDnode, PDroot and Dd), downward sloping
asymptotically (W and F)
and some showed no correlation to species richness (AvPD, AvTD and
J).
The complementarity algorithm showed that gains in different
phylogenetic diversity
measures did not match each other closely when complementarity in
pixels added was
maximized for species richness (Fig. 1).
35
Figure 1 – Complementarity analysis. Gains in species richness and
several phylogenetic diversity indices when a complementarity
algorithm is run maximizing species richness. The gains for the
sites selected are normalized for each measure.
This was also not the case when complementarity was maximized for
phylogenetic diversity
indices (data not shown). Moreover, when maximizing gains for each
measure separately and
plotting the sites selected for each measure on a map of the Cape
Floristic Region, no
overlap existed between species richness and phylogenetic diversity
sites (data not shown).
Gains in Q (a topology measure) were the only ones to match species
richness gains closely.
Those for a minimum-spanning-path (PDroot) were poorly predicted by
species richness.
One of the pairwise-distance indices (Dd) followed an even more
unpredictable trend, with a
decrease in values when the second and third cells were added.
Finally, the two averaged
methods showed completely conflicting patterns with species
richness with their values
decreasing as the number of cells were added (Fig. 1). Normalizing
and subtracting species
richness from phylogenetic diversity revealed areas of discrepancy.
The spatial patterns of
discrepancy between species richness and phylogenetic diversity
varied considerably
depending on the index used (Fig. 2).
36
Figure 2 - Discrepancy maps between species richeness and different
phylogenetic diversity indices. In red are all the areas where
phylogenetic diversity is greater than species richness, whilst
areas in blue are areas where species richness is greater than
phylogenetic diversity (both are normalized). The Spearman
correlation (Rho) between species richness and phylogenetic
diversity and the percent of grid cells where phylogenetic
diversity is greater than species richness are indicated.
37
Both of the topology indices (W and Q) and the pairwise distance
measure F showed the
areas harboring unexpectedly high phylogenetic diversity values to
be congruent with the
most significant species richness hotspot (c in Fig.3). On the
other hand, most of the
minimum spanning path and pairwise distance indices (e.g. PDroot,
AvPD, J, AvTD, and Dd)
identified more peripheral areas to the main species richness
hotspot as having higher than
expected levels of evolutionary history. These peripheral areas
included the Koebeeberge
mountains in the northern portion of the Cederberg range (Fig. 3a)
and the more low-lying
areas between Knysna and Port-Elizabeth (Fig. 3d) in particular, as
well as the area
comprising parts of the Cederberg, KoueBokkeveld and Groot
Winterhoek mountains (Fig.
3b.
Figure 3 - Predicted species richness for the Proteaceae of the
Cape Floristic Region. Areas of special interest which are
identified and discussed throughout this study are: the Koebeeberge
mountains, in the northern portion of the Cederberg range (a),
parts of the Cederberg, KoueBokkeveld and Groot Winterhoek
mountains (b), the Hawekwas, Hottentots Holland and Kogelberg
Mountains, the Cape Peninsula and the Agulhas plain (c), the areas
between Knysna and Port Elizabeth (d).
38
The overlap between species richness and various measures of
evolutionary history varied
with the amount of surface area considered, but it did not increase
linearly with it (Fig. 4).
The highest level of overlap when selecting the average amount of
land set aside for
conservation, for example, was experienced with the use of the two
topology methods, Q
and W (93% and 89% respectively) and two of the pairwise distance
methods, F and TTD
(95% and 93% respectively). Intermediate levels of overlap with
species richness patterns
were experienced in one of the pairwise distance methods, Dd (53%)
and in two of the
minimum spanning distance methods, PDroot and PDnode (79% and 78%
respectively), and
no overlap at all was experienced with two pairwise distance
methods and the remaining
minimum spanning method (J, AvTD and AvPD respectively).
Figure 4 - Percentage of overlap between species richness and
different evolutionary history patterns against amount of land
cover considered. The change in overlap between predicted species
richness and phylogenetic diversity indices when different
percentages of the landscape are set aside for conservation. The
overlap values obtained for realistic percentages of the “richest”
land cover to be set aside for conservation are highlighted within
the black box.
PCA of environmental variables and quadratic regression
39
The PCA-based gradient analysis of the six environmental variables
employed to predict
species distributions (and consequently species richness and
evolutionary history) was used
to investigate the differences in climatic features between areas
where phylogenetic diversity
was higher than species richness and vice-versa (Fig. 5). The
scores corresponding to these
grid cells are climatically separated along the axes of the PCA
(Fig. 5a,b,c). Segregation
between these points was more evident in phylogenetic diversity
measures which correlate
poorly with species richness (e.g. AvTD, AvPD and J, Fig. 5c), and
less so in phylogenetic
diversity indices which correlate highly with species richness
(e.g. TTD, F, W and Q, Fig 5a).
In general however scores corresponding to grid cells with higher
than expected
evolutionary history were associated with higher temperatures and
evapo-transpiration and
lower rates of precipitation (Fig 5). Altitude explained 31%, 11%
and 9% of the variation in
the discrepancies between TTD (t(27,243)=-76.82, p<0.001,
Fig.6a), PDroot (t(27,243)=-
40.32, p<0.001, Fig.6b) and AvTD (t(27,243)=-28.12, p<0.001,
Fig.6c) respectively.
40
Figure 5 - Principal component analysis of the environmental
variables used to predict patterns of species richness and
phylogenetic diversity. The seven environmental variables in the
analysis are: average annual evapo-transpiration (Evtr0112),
average evapo-transpiration between May and August (Evtr0508),
average annual temperature (Temp0112), average temperature between
May and August (Temp0508), average annual precipitation (Prec0112),
average precipitation between June and August (Prec0508) and
average precipitation between November and February (Prec1102)
(5d). Principal components 1, 2 and 3 explain respectively 74, 17
and 8% of the variability. Scores corresponding to grid cells where
phylogenetic diversity is greater than species richness are
highlighted in red, whilst those where species richness is greater
than phylogenetic diversity are in blue.
41
Figure 6 – The relationship between altitude and discrepancy values
– A quadratic regression using altitude as the explanatory variable
and the discrepancy between species richness and three phylogenetic
diversity indices (TTD in a, PDroot in b and AvTD in c) as response
variables.
Discussion We found considerable variation in how different
phylogenetic diversity indices correlated
with species richness. The investigation of spatial patterns, both
from a complementarity
algorithm perspective and a spatial overlap approach revealed that
only topology-based
phylogenetic diversity indices can be truly considered
interchangeable with species richness.
Moreover, we find that regions highlighted preferentially by
species richness or phylogenetic
diversity are, to some extent, segregated climatically and
spatially. Given that the Proteaeceae
are part of a unique system with low migration rates and high
beta-diversity, our results are
of particular relevance to this and other similar biodiversity
hotspots.
Correlations
Highly significant correlations between species richness and
phylogenetic diversity were
often but not always matched by an equally significant level of
overlap in spatial patterns.
An important distinction exists amongst functions which calculate
the evolutionary history
of species living in a particular area. Non-averaged phylogenetic
indices sum the evolutionary
history across all species, whilst averaged phylogenetic diversity
measures divide the sum of
evolutionary paths by a function of the number of species present,
ultimately representing
the mean evolutionary history brought by each species. This
property removes the
relationship between species richness and these phylogenetic
diversity indices (Schweiger et
al. 2008). Because species richness is somehow included in
non-averaged phylogenetic
42
diversity measures, a positive relationship is to be expected, but
the exact nature of this
relationship is likely to vary between different classes of
non-averaged indices. Although
Rodrigues and Gaston (2002) found a linear relationship between
genera richness and
phylogenetic diversity (using PDroot), our results showed that this
may not always be the
case. Tree shape, a very influential feature in phylogenetic
analyses (Mooers & Heard, 2002)
has also been shown to have an effect on the correlation between
taxon richness and
phylogenetic diversity (Rodrigues et al., 2005) and the addition of
a single species from a
heavily imbalanced tree, for example, will have a disproportionate
effect on phylogenetic
diversity. It is also likely that diversification rates among
lineages will have a large influence
on the correlation with species richness. For example, areas
including clades which
originated from recent diversification bursts (and thus
characterized by many closely related
taxa with shorter branches) should show lower expected phylogenetic
diversity compared to
areas inhabited by a relatively higher proportion of older
monotypic clades.
Discrepancies – the roles of climate and space
Soil characteristics, seasonal fire and climate regimes and
pollinator specificity are amongst
some of the favorite candidates used to explain the unusual
diversity found in the Cape
Floristic Region (Goldblatt & Manning, 2002; Linder, 2003).
Colonization and climatic
history are regarded as some of the major factors explaining
current distribution of diversity
within this exceptional region (Engler, 1904; Hedberg, 1965;
Hedberg, 1970; Bergh &
Linder, 2009). In this study found that areas harboring higher than
expected species richness
were characterized by higher precipitation and lower rates of
evapotranspiration (Fig. 5).
These results were strengthened by the observation that higher
elevations (normally
associated with higher precipitation and lower evapotranspiration
regimes) supported
unusually high numbers of species (Fig.6).
Mountains in the Cape region have long been described as hotspots
of diversity (Myers et al.,
2000; Linder, 2003; Linder & Hardy, 2004; Linder, 2005).
Cowling and Lombard (2002) for
example attributed the high levels of species richness and endemism
to reduced summer
aridity of these environments (Cowling & Lombard, 2002).
Several authors attributed their
high diversity to a variety of repeated dispersal and colonization
events from regions such as
43
the Mediterranean, the Northern and Southern Hemispheric temperate
regions and the Cape
Floristic Region itself (Engler, 1904; Hedberg, 1965; Hedberg,
1970). It may be possible to
explain these unexpectedly high levels of species richness in
mountain areas with the fact
that they are characterized by allopatric speciation processes,
which took place in particularly
stable climates and which underwent very low extinction rates
compared to lower lying
regions (Dynesius & Jansson, 2000; Lawes et al., 2000).
Moreover, mountains provide a
sharp environmental gradient, where species are able to take
advantage of the shorter
migration distances to re-colonize suitable habitats and survive
climatic fluctuations (Loarie
et al., 2009). Therefore, relatively stable mountain climates (yet
diverse from a micro-climate
perspective) may have allowed species to persist and evolve through
time undisturbed,
displaying today most of the original members in each clade. Lower
lying regions on the
other hand may have been subject to much higher extinction rates,
and may contain today
only the surviving members of once much more diverse clades, thus
accounting for higher
amounts of unique evolutionary history.
Spatial patterns of overlap
When considering portions of the landscape which may realistically
be set aside for
conservation there was considerable difference in levels of spatial
overlap, with topology
indices always scoring high and averaged indices always scoring
low. Non-averaged
(minimum-spanning-path and pairwise-distance) indices mostly
displayed intermediate levels
of overlap. The kind of index used can therefore influence greatly
both the areas considered
to represent large amounts of evolutionary history and our decision
to include such measures
in conservation planning altogether or to simply resort to species
richness.
How to select the right index?
We agree with Schweiger et al. (2008) that there is no overall best
phylogenetic diversity
index, but simply different situations to which certain indices may
be better than others.
What kinds of factors should we take into account when selecting a
phylogenetic diversity
index? And what may the advantages and disadvantages be of a high
correlation with species
richness? Averaged indices were consistently found to have the
lowest correlations with
species richness and lowest levels of spatial overlap. This was not
particularly surprising as
44
their very aim is to eliminate the effect of taxon richness
(Schweiger et al. 2008). In theory, a
low correlation with species richness is a very desirable property
for a phylogenetic diversity
measure, as it will maximize the effect of phylogeny and therefore
provide an unbiased
measure of evolutionary history. From a practical point of view
however, if phylogenetic
diversity information is to be included in a complementarity
algorithm we discourage
conservation practitioners from using averaged measures. Our
results showed (Fig. 1) that
the increase in phylogenetic diversity gained by adding a new site
is heavily counter-weighted
by the number of new species that will be added ultimately causing
a reduction in averaged
phylogenetic diversity estimated. The mathematical properties of
averaged methods are such
that maximising diversity and representing all parts of the
phylogenetic tree is impossible
through basic complementarity algorithms.
Ultimately, the choice of index and whether to include a
phylogenetic diversity index at all
depends on what conservation efforts are trying to prioritise.
Presently, a large complement
of inter-specific genetic diversity as option value for the future
is both still a desirable and
sensible feature to preserve (Mooers et al., 2005b; Forest et al.,
2007; Cadotte et al., 2009).
Though feature diversity of very old monotypic lineages is always
likely to be higher, it may
very well also be more susceptible to going extinct (Purvis et al.,
2000) and depending on
what is driving extinction, impossible to retain in the longterm.
On the other hand, we have
evidence that evolutionary rich communities have higher levels of
productivity in terms of
biomass (Cadotte et al., 2009) suggesting that high phylogenetic
diversity not only preserves