Late-Quaternary Landscape
Dynamics in the Iberian Peninsula
and Balearic Islands
José Pedro Rodrigues Tarroso Gomes
Porto

Late-Quaternary Landscape
Dynamics in the Iberian Peninsula
and Balearic Islands
a proposal submitted
in partial fulfillment of the degree of Master of Sciences
to Faculdade de Ciências da Universidade do Porto
José Pedro Rodrigues Tarroso Gomes
Master in Biodiversity and Genetic Resources
Porto

ii |
To the memory of my mother.
| iii
iv |
Acknowledgements
Since the beginning of this masters project, many people have shown their interest and were fundamental to its completion.
In the first place I have to mention my supervisor Professor Paulo Célio who suggested the
thesis theme and who believed I would be able to execute it. It was a long walk to achieve the final
result and I hope that this work can, at least, be at the level of his expectations. One person without
whom this thesis would be entirely (or even more) impossible was José Carlos Brito. I am immensely
thankful for his guidance, suggestions and reviews from the last glacial maximum until the present
time!
The support of my family was extremely important during the development of this manuscript. My father, who was always present with great sense of humour, gave me all the support I
needed. My brother, sister and grandparents were permanently present as well. It is strange to be
surrounded day after day by biologists, geneticists and all source of statistical analyses aiming at the
most basic logic thinking of the scientific method and then to feel the peculiar genetic bound like I
feel with them. I also would like to mention Helena and her children for the kind support they gave.
I am indebted to my friends Pedro and Sofia: you are just like a family. You have the ability to
make me think that I am capable to achieve good results and our conversations are always so prolific
in both fields of science and art. Without all the knowledge I have stolen from you, this work would
never be possible. I also would not understand anything about 14C without Elin’s help. I am pleased
to be her friend and grateful to all her endless questions! Some of the best conversations I ever had
were with Sara and all her lunacy: thank you for being present and I hope biology didn’t make any
harm to you! Adriano, you are a good friend. You patiently listened all I said in the best and worst
moments throughout this work and you always had something to say that was, at least, unexpected,
however bright! I am especially thankful to Fátima for all her support and concern with me and with
my work.
It was gratifying to receive the support of some colleagues at work. The support of my “lab”
friends Fernando Lima, Nuno Queiroz and Pedro Ribeiro was completely indispensable. You are
great scientists and friends and I appreciated all the belief in me and the help you gave me since the
beginning. And also all the laughs! Furthermore, I am grateful for the opportunities you gave me to
work with you. I could not forget Raquel Xavier and her superior skills in friendship! I am also
thankful to my friends Joana Abrantes, who lent me some distracting literature, and Diana. Miguel
|v
Carretero became a good friend after all this time I worked at CIBIO, and I have learnt a lot with
him. I am also thankful to Catarina Rato and to the newest international friends, Anna and Jay, who
provided good moments during the obscure period of writing a thesis!
One of the everlasting friends I made even before I began working at CIBIO was Catarina
Ferreira. Thank you for all your support and the friendship revealed every time we meet. I have
enjoyed working with Joana and Claudia and I am thankful for their great friendship. Francisco
Álvares was extremely helpful with all the bibliography he lent me just because he saw a probable
link to my work. He always provided good moments and laughs in the workplace. I am especially
thankful to him and also to Neftalí Sillero due to the trust he deposited in me all this time, the
friendship and those hard to find papers he dug up in Salamanca. He was also the first person with
whom I shared a office in Vairão and, since then, a good mood was the tone with all the colleagues
there, to whom I am indebted, especially to Silvia. I am also thankful to João Torres, with whom I
had productive dialogues about GIS (among other more interesting subjects), and Hélder Freitas.
Both have been good friends since the beginning of this masters. I also have to mention my gratitude
to Paulo Alves for his friendship and some tips about vegetation in the Iberian Peninsula and to
Professor João Honrado for revealing confidence in my work, interest in the subject and the provided
literature. Finally, all my colleagues at CIBIO were responsible for the fact that this thesis reached its
end, with the manifested interest and support given.
I bootstrapped these people a huge number of times along with some other else I am forgetting, as you were so many to mention here. I reached one hundred percent confidence that this work
would never be possible without all of you. Thank you all.
vi |
Table of Contents
Resumo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Biogeography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Climatic Oscillations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Reconstruction of past landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 GIS in past vegetation reconstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1. 5 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
2.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Biomization procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Data visualization and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
3.1 Distribution of plant genera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Distribution of Plant Functional Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3 Distribution of Biomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4 Relationships between Biomes and environmental conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.5 Distribution of persistence areas of plant genera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39
4.1 Correlations with climate data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Comparing independent past landscape reconstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Congruence with phylogenetic reconstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.4 Parallelism between fauna and flora refugia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53
6 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
Appendix I - Biome affinity scores by sampled site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Appendix II – Script for interpolating affinity surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Appendix III – Script for classify Biomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
| vii
Appendix IV – Script for calculating correlations between maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Appendix V – Script for smoothing rasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Appendix VI – Script for converting ascii files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Appendix VII – Script for masking rasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Appendix VIII – Script for classifying rasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Appendix IX – Script for exporting maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
viii |
| ix
x|
Resumo
O clima no planeta ao longo do fim do Pleistocénico e durante todo o Holocénico foi instável, variando entre o frio extremo do último máximo glaciar (LGM) até períodos de aquecimento intenso.
Estas ocilações no clima suscitaram ciclos de contracção e expensão na distribuição da vida na Terra.
As temperaturas adversas do LGM concentraram a diversidade biológica em áreas de refúgio, localizadas em latitudes inferiores e com um clima mais ameno, possibilitando eventos de especiação. No
sul da Europa, as penínsulas Ibérica, Itálica e Balcânica, são reconhecidas como áreas de refúgio para
várias espécies. Deste modo, a Península Ibérica configura-se como um local de extrema importância
para a análise de padrões paleogeográficos na distribuição da fauna e flora.
No presente trabalho é estudada a dinâmica da vegetação na Península Ibérica e ilhas
Baleares durante o período temporal entre 15 até 1 ky BP. Como estiveram distribuídos alguns
géneros vegetais e Tipos Funcionais de Plantas (PFT) durante o período em estudo? Qual era a área
ocupada pelos vários Biomas e a sua relação com as oscilações na temperatura? Para responder a
estas questões foram analisadas as presenças de fósseis de pólen dos géneros Alnus, Betula, Castanea,
Fagus, Olea, Pistacia, Quercus de folha perene e Quercus de folha caduca. Estes dados polínicos foram
adquiridos em bases de dados públicas disponíveis na Internet ou digitalizados a partir de diagramas
polínicos publicados, formando uma rede de amostragem distribuída por toda a área da Península
Ibérica e ilhas Baleares. Após a devida calibração do método de datação, as percentagens de presença
de pólen e as afinidades a Biomas nos vários locais de amostragem foram interpoladas pelo algoritmo
kriging e representadas espacialmente num Sistema de Informação Geográfica (GIS). Obteve-se
assim uma sequência temporal de mapas de distribuição, reveladores de padrões de migração e persistência (refúgio) dentro da área de estudo. As áreas de persistência foram quantificadas por sobreposição espacial sendo analisada a relação entre a sua área e as oscilações climáticas através de correlações espaciais. Adicionalmente, desenvolveram-se scripts específicos por forma a automatizar a
produção de uma grande quantidade de mapas de distribuição potencial e a quantificar os processos
de reconstrução da vegetação do passado.
O presente estudo demonstra a resposta dinâmica da vegetação às alterações do clima ao
longo do fim do Quaternário dentro do refúgio Ibérico, que se reflecte através da expansão e contracção das áreas de distribuição dos géneros, PFTs e Biomas estudados. A relação da temperatura
com a distribuição dos vários biomas revelou tendências de aumento das área ocupadas por “biomas
quentes” e de decréscimo da área dos “biomas frios” com o aumento da temperatura. As zonas bio| xi
climáticas da Península Ibéria, Temperada no norte e Mediterrânica no sul, estão correlacionadas espacialmente com as zonas de persistência dos vários géneros, como por exemplo a Betula e a Pistacia,
respectivamente. Os padrões de variação espacial na distribuição e persistência dos géneros, corroboram outras reconstruções de carácter não-espacial assim como estudos filogenéticos baseados em
marcadores moleculares. A utilização de GIS provou ser essencial na reconstituição histórica da distribuição de géneros, PFTs e Biomas. As projecções históricas constituem modelos adequados para
aferir estudos filogeográficos, permitindo deste modo uma análise multidisciplinar do passado.
xii |
Abstract
The climate of the planet during the late Pleistocene and Holocene was unstable, ranging from
extreme cold during the last glacial maximum (LGM) to warming periods with hot temperatures.
Shifting trends of climatic events had repercussions in the distribution of species in the planet,
forcing cycles of contraction and expansion. At the LGM, most life diversity was constricted to
several refuge areas in lower latitudes, where the climate was mild, allowing the occurrence of speciation events. In Southern Europe, the Iberian, Italic and Balkan peninsulas are known refugia for
several species. Therefore, the Iberian Peninsula is an important area to develop studies on paleogeographic patterns of the distribution of fauna and flora.
In the present work it is explored the vegetation dynamics in the Iberian Peninsula and
Balearic Islands during the period of 15 to 1 ky BP. What was the distribution of some plant taxa
and Plant Functional Types (PFT) during the studied time span? What was the area occupied by
each Biome and its relationship with temperature shifts? To answer these questions, fossil pollen
presence was determined for the genera Alnus, Betula, Castanea, Fagus, Olea, Pistacia, evergreen
Quercus and deciduous Quercus. These pollen data were acquired in public databases available in
the Internet or in digitized published pollen diagrams, configuring a network of sampled sites
throughout all the Iberian Peninsula and Balearic Islands. After proper calibration of the dating
method, the pollen presence percentages and the affinities to Biomes in the sampled sites were interpolated using kriging algorithm and represented spatially in a Geographical Information System
(GIS). It was obtained a time series of distribution maps, allowing discerning migration patterns
and persistence areas (refugia) inside the study area. These persistence areas were quantified by overlaying spatially the distribution maps and analyzed the relation between their area and climatic oscillations through spatial correlations. Additionally, specific scripts were developed to automate the
production of a large dataset of potential distribution maps and to quantify the processes of reconstruction of past vegetation.
The present work illustrates the dynamic response of vegetation to climatic shifts inside the
Iberian refugia during the late-Quaternary, observed in the expansion and contraction of the distribution of studied genera, PFTs and Biomes. The relationship between temperatures with the range
of several Biomes suggested trends for an increase in the area occupied by warm Biomes and decrease of the area occupied by cold Biomes with temperature increase. The bioclimatic zones of
Iberian Peninsula, Temperate in the north and Mediterranean in the south, are spatially correlated
| xiii
with the persistence areas identified for several genera, such as, Betula and Pistacia, respectively. The
patterns of shifting distributions and persistence of genera support other non-spatially explicit reconstructions as well as phylogenetic studies based in molecular markers. The use of GIS proved to
be essential for the reconstruction of past genera, PFTs and Biomes. These historical reconstructions
are adequate benchmarks to evaluate phylogeographic studies, rendering a multidisciplinary approach of the past.
xiv |
 Introduction
The reconstruction of past landscapes and environments has been an active research
field in recent years as shown by the number of published works in the last decade.
Due to its multidisciplinary nature, this recent interest is explained by major advances in different scientific and technologic areas as biology, chemistry, and informatics.
These recalls of the past rely on several proxies (Roberts 1998, Trenberth & OttoBliesner 2003), as there is no direct approach to past ages and generates different
types of reconstructions, such as climatic, vegetation, among others. What is the
nature of information which enclosures evidence from the past?
Direct past evidence of paleopalynology was used by Williams (2004) to reconstruct the vegetation and biomes of North America during late-Quaternary. A
similar method has been applied by Elenga et al. (2000) to reconstruct biomes of
Western Europe and North Africa. The procedures published by Prentice (1996)
and the exceptional effort of the project BIOME 6000 (1998) to attain the reconstruction of past biomes for 6000 years BP of almost all land surface are the basis of
reconstruction procedures nowadays. Recently, Benito Garzón (2007) recreated the
vegetation of Iberian Peninsula for the LGM and mid-Holocene (6000 years BP) by
predictive modelling with two atmospheric general circulation models. This modelling approach does not rely on direct evidence as pollen data, though it has increased
the interest of comparison with other reconstruction methods. As Prentice (1996,
1998) stated before, the data-model comparisons must be made with a global data
set uniformly compiled using biomes as an objective method to assess pollen and
other plant remains. Results of these comparisons are extremely valuable as they may
confirm each other hypotheses, contributing for an increasing confidence in reconstructions as they are the best-guesses of past processes. Vegetation and climatic data
comparisons are also valuable due to their intrinsic relation and is a useful tool to
assess the complex pattern of biotic response to late-Quaternary. Pollen and macrofossil records constitute a direct evidence of vegetation composition of specific spatial
and temporal location but can be indirect sources of paleoenvironmental conditions
(Huntley 2001). Thus, analysis between models and direct evidence are informative
Introduction | 
of discrepancies and the state-of-the-art of past environmental model making
(Alfano et al. 2003). Jost (2005) studied the comparison between data and high resolution models, finding dissimilarities that could reach 10ºC in Western Europe.
Why the interest on these reconstructions intensified recently? The increasing
of computational power strengthened the interest on past reconstructions. Huntley
& Webb (1989) anticipated the importance of palaecological data to study the dynamics of ecological processes leading late-Quaternary migrations, “especially when
displayed cartographically at the appropriate spatial scale”. The models with more complexity to use in reconstruction itself or for spatial interpolations of data need less
time to compute and improvements in software facilitate the production of a vast
number of maps with a large data-set and are easily accessible. Other methodological
advances, as precision increment and the increased diversity of dating analysis,
brought more accuracy to this research field.
The complexity of past reconstructions is divided by its multidisciplinary
nature and the need to analyse a large temporal and spatial intervals. Understanding
the role of vegetation in the Earth system is therefore possible (Williams et al. 2004)
and, furthermore, the climate rhythm as a controlling mechanism of distribution
patterns of life (Cox & Moore 2005, Hewitt 2004b, Trenberth & Otto-Bliesner
2003). This spanned timescale knowledge of past environments combined with
recent expertise in actual species distributions and behaviour produces an integrated
description of the past and the ability to consistently predict the future (Anderson et
al. 2006, Davis 1994).
1.1 Biogeography
Biogeography is the study of all living organisms in space and time (Brown et al.
2005). The present distribution of organisms hide important clues about their
history and this knowledge allows a better prediction of future changes. This field of
research is strongly tied to the concept of Biodiversity, which is a term that encompasses the whole living organisms in the planet, including all described species and
those that remain undiscovered (Cox & Moore 2005). Biodiversity is not uniformly
distributed throughout Earth’s surface. There is a latitudinal effect that concentrates
the highest number of species along the equator. In the tropics, the number of
mammal species is very high mainly due to a larger number of fruit-eaters and insectivores. Therefore, prey availability is an important factor contributing for such high
levels. Plant species in the tropics have also a great variety due to higher photosynthetic production (Cox & Moore 2005).
How can priority areas be delimited with species diversity? Areas where the
highest diversity converges along with high rate of habitat lost are designed as biodi | Introduction
versity hotspots. The species diversity is assessed by the number of all species, rare
species or threatened species, among others biodiversity indicators (Myers et al.
2000, Reid 1998). One of the world’s most important hotspot is the Mediterranean
Basin (Cincotta et al. 2000, Cox & Moore 2005, Myers et al. 2000), where the
Iberian Peninsula is located. This peninsula clusters high levels of diversity since it
has an exceptional concentration of species, enclosures important information about
the past behaviour of local biodiversity, and has an important role in the future conservation of biodiversity (Weiss & Ferrand 2006).
The pattern of distribution of species richness nowadays is closely tied to historical factors and was partially driven by climatic shifts that caused migrations to
southern latitudes where higher range of life-supporting environments were available
(Hewitt 2000). The glacial epochs caused an increase in the extent of the ice sheet,
mostly in northern latitudes, inducing adverse conditions with the decrease of life
sustainability (Davis & Shaw 2001, Hewitt 2000, 2004a). How did life react to climatic oscillations? Whereas some species responded to past climatic shifts with
southward migrations, others remained at the same latitude with altitudinal shifts in
their distribution (Davis & Shaw 2001, Hewitt 2004b). The changing climate and
the need to colonize new areas challenges species to survive in refugia and adapt to
face new climate conditions (Davis & Shaw 2001, Hewitt 2000, Taberlet &
Cheddadi 2002). The periodicity of climate shifts makes these events to occur repeatedly during life history on Earth and left a legacy in the genetic structure of the
organisms (Hewitt 2000). The Iberian Peninsula, with other southern peninsulas,
was a refugia for multiple animal and plant species in Europe during the LGM and
consequently has increased the genetic diversity amongst several species in those
areas as seen in several genetical studies (Hewitt 2004a, 2004b, Taberlet et al. 1998).
With warming climate, there is a trend for northward migrations, where there
was previous unsuitable habitat due to presence of ice. While some species persisted
in the south, others followed several routes of expansion in Europe from southern
refugia (Hewitt 2004a, 2004b, Taberlet & Cheddadi 2002) into recent suitable areas
according to intrinsic dispersal capabilities and ecological requirements (Taberlet &
Cheddadi 2002). Although there is a broad pattern of northern migration routes,
different species had different migrations paths. Some topographic features as the
Pyreenes and Alps acted as barriers of dispersion for some species, whereas others
crossed them easily (Hewitt 2000). This expansion is accomplished with the loss of
genetic diversity and it is noticeable in the present spatial pattern of genetic structuring with a south-north gradient of decreasing diversity (Davis & Shaw 2001, Hewitt
2004b, Taberlet & Cheddadi 2002). These characteristics render the Iberian
Peninsula as a special place to undergo biodiversity studies and to apprehend the
patterns of late-Quaternary climate influence in key species within glacial refuge.
Introduction | 
1.2 Climatic Oscillations
The Earth is constantly suffering from periodical phenomenon that creates a
dynamic climate, by the shifting nature of incident solar energy (Zachos et al. 2001).
Some occur at a human temporal scale and with direct biological consequences as
the daily light cycle or the sequence of seasons caused by rotation and inclination of
Earth axis. These factors have an immediate influence in living organisms, easily perceived in annual activity and circadian rhythms.
Several other factors occur at longer scales, contributing to a climatic history
of periodical oscillations. As consequence, life on the planet underwent distribution
changes, extinction and speciation events until achieving its present diversity. The
study of the genetic consequences of these events allows the disclosure of the evolutionary process (Hewitt 2004b). The climatic history is characterized by colder and
warmer stages, drifting from massive expansion of polar ice-sheet and decrease of
sea level to free polar ice caps and sea level rising. Distinct Earth orbital parameters
described by the Croll-Milankovitch are responsible for the climatic pace: (1) eccentricity refers to the shape of Earth orbit around the Sun, shifting from quasi circular
to elliptic and with a 400 and 100 ky cycle; (2) obliquity of Earth axis in relation to
orbital plan, tilting from 22.1º to 24.5º and responsible for a 41 ky pace; (3) axial
precession or the wobble of axis of rotation every 23 and 19 ky (Hewitt 2000,
A - Eccentricity
400 and 100 ky
Fig. 1.2 – Earth orbital
parameters
The pace of climate change is
mainly controlled by three orbital perturbations described
by the Croll-Milankovitch
theory. Eccentricity (A) refers
to the changing shape of the
Earth’s orbit around the sun
from a near circular to an elliptic. The cycle of this orbital
propriety has a period of 400
and 100 ky. The axis of rotation (B) modifies its position
with amplitude of 2.4º every
41 ky. The precession (C)
is a characteristic of objects
in rotational movement: the
axis has not a fixed position
and wobbles describing a
circular movement in space.
Modulated by eccentricity,
this perturbation has a period
of 23 and 19 ky. Data are
adapted from Zachos et al.
(2001).
 | Introduction
0.06
0
200
400
600
800
1000 ky
C - Axial Precession
B - Axial Obliquity
2.4º
41 ky
23 and 19 ky
-0.08
25
0
23
21
0.08
0
200
400
600
800
1000 ky
0
200
400
600
800
1000 ky
Zachos et al. 2001). Isolated or combined together, these orbital perturbations shape
the distribution of solar radiation in Earth’s surface. Whereas intensity and season
contrast are balanced by eccentricity and axial precession, the most exposed hemisphere is determined by obliquity. Other intrinsic factors of the Earth had a huge influence in the planet’s history and climatic oscillations. Topographic, bathymetric
and atmospheric features, conditioned mainly by plate tectonics, had effects at a
million year time scales and increased climate complexity and diversity (Zachos et al.
2001).
Evidence of Milankovitch cycles were found in several reconstructions and it
was observed a prevalence of orbital parameters over each other: 8 My ago the 100
ky the glacial/interglacial cycle was weak whereas in the last 2 My dominated the
climate change (Augustin et al. 2004, Cox & Moore 2005), i.e., during the
Quaternary. Warming and cooling phases do not occurred with stable increments or
declines of temperature. During the last 150 ky, a succession of warm/cold cycles
took place, building a dynamic climate (Alley & Clark 1999, COHMAP 1988,
Folland et al. 2001, Grafenstein et al. 1999, Petit et al. 1999, Zachos et al. 2001).
The Quaternary (Fig. 1.2), especially the late-Pleistocene and Holocene are characterized by sudden events in the warming phase until present time. The Last Glacial
Maximum (LGM) extended from 25 to 18 ky BP followed by warming trend, or the
beginning of deglaciation known as the Bölling-Alleröd event. This warming phase
was abruptly discontinued with the Younger Dryas (YD), a cold event about ~12.7
to 11.5 ky BP. When compared to modern climate, the YD is characterized by cold,
dry and windy conditions (Alley & Clark 1999). The Holocene is a warming period,
starting at 10 ky BP until present, and at its earliest phase was generally warmer then
the 20th century (Folland et al. 2001). As other periods, it was not free from oscillations. It had a maximum warming at ~4.5 to 6 ky BP across Europe and a fast cold
event about ~8.2 ky BP (Alley et al. 1997, Folland et al. 2001, Rohling & Palike
2005). This latter event had a similar pattern to YD event (Alley & Clark 1999)
with a worldwide decrease of 2ºC of annual mean temperatures (Folland et al. 2001,
Rohling & Palike 2005, Wick & Tinner 1997). Strong climatic oscillations during
the last 20 ky due to orbital perturbations had shaped the distribution of life in
Earth’s surface and left behind clues that can be analysed to reconstruct past
environments.
Fig. 1.2 – Main climatic
events during the late-Quaternary
The late-Quaternary comprises the end of Pleistocene
and the Holocene. The first
begun at ~150 ky BP with
the Last Glacial from ~74
to 14 ky BP and reaching
the LGM at ~25 to ~18 ky
BP, when the deglaciation
have started. The Holocene
begun at ~10 ky BP until
the present. The warming of
climate is an unstable process:
it had pronounced events that
oppose to or emphasize the
general trend. The cold event
1 is the Oldest Dryas (~18 to
14.5 ky BP) that took place at
the beginning of deglatiation,
which extends until the beginning of Holocene at ~10
ky BP. A noticeable warmer
event (2), the Böling-Alleröd,
occurred from 14.5 to 13 ky
BP in Europe, followed by
the Younger Dryas (3), a well
studied cold event. The event
4 happened at 8.2 ky BP and
it was a sudden reversal to
cold with a brisk appearance.
The Holocene Maximum
Warming (5) or the warmest
phase of the Holocene took
place from 6 to 4.5 ky BP.
Introduction | 
1.3 Reconstruction of past landscapes
The reconstruction of past conditions is based on proxies that reflect past climate or
vegetation composition. There is an ample variety of data sources and choosing
between them depends on the kind of reconstruction needed. Due to high correlation with temperature, proxies like oxygen or carbon isotopes (δ 18O and δ 13C, respectively) are used since several decades to build climatic reconstructing to obtain
high-resolution data (Zachos et al. 2001) and information about abrupt climate
changes (Crowley & North 1988). Fossil remains of pollen, other vegetation structures and animals are precise evidence of past biological composition and may also
serve as indirect climate proxies. The reconstruction of past vegetation assumes that
there is a plant feedback to climate shifts with immediate results on its distribution
and composition (Hewitt 2004a, Huntley & Webb 1989, Williams et al. 2004), configuring a dynamic and complex system.
A requisite for paleo-reconstructions is precise dating, which can be obtained
by a careful selection of sample sites and using the most recent dating techniques
(Vandenberghe et al. 1998). There are several dating methods which may be clustered into four major groups: (1) historical, based on known date events and detectable in data; (2) biological, based on increment quantity related to time as the tree
rings; (3) paleomagnetism with secular variation and (4) radiometric, based on radioactive decay propriety of elements (Roberts 1998). The latter are the methods most
used since there is a vast availability of data and results are effortless, when compared
to other methods. The radiometric most frequently used is the 14C that allows the
datation of organic matter from ages comprehended between 200 and 40 ky, with an
error of 20 - 1.000 years (Roberts 1998). This interval includes all Holocene and a
portion of Pleistocene. It is based on the rate of radioactive decay of elements as a
geological clock. The half-life is a measure of radioactive decay and in the case of 14C
is 5730±40 years, which is the time its radioactivity decreases by half (Roberts
1998). When the organism dies, their 14C content ceases to be replaced and the
clock begins. The major disadvantage of this method is the impossibility to use in
more recent ages due to phenomena with human origin. The fossil fuel combustion
since industrial revolution as well more recent nuclear experiments has introduced
on the atmosphere “older” carbon, misleading the dating method to yield farther
dates (Roberts 1998). To increase the precision of this method, Stuiver et al. (1998)
proceed to a calibration with parallel data as tree rings and marine data (Reimer et
al. 2004, Stuiver et al. 1998).
One method for the reconstruction of past landscapes and detect past species’
presence is the analysis of organism’s remains, a process globally known as paleoecol | Introduction
ogy. Palynology is one of its most important branches and concerns to the study of
fossil pollen that represents a vegetation structure of the study area for a specific time
(Roberts 1998). Plant reproduction produces pollen spores and these are preserved
in lake muds, peat bogs and other sediments, allowing posterior analysis of the
remains (Roberts 1998). The information extracted from pollen cores has a multivariate nature offering multiple aspects of past environmental conditions, being one
of the major advantages to other physical or chemical proxies (Huntley 2001). The
analysis of a pollen site requires a core drilling and posterior analysis in laboratory of
the remainings, often reaching low taxonomic levels as families, sub-families, genuses
or even species (Cox & Moore 2005, Huntley 2001). The outputs of this method
include raw pollen counts and a pollen diagram representing percentages of pollen
presence by time or depth, and by taxa. However, it is a time consuming method and
there is a low number of high temporal resolution studies (Huntley 2001), despite
the large amount of paleovegetation data available worldwide since LGM (Prentice
et al. 1998).
Direct evidence from the past vegetation is given by fossil pollen, whereas
other data is obtained indirectly with proxies. However, assumptions have to be
made to achieve a conclusive use of pollen data: the morphology of present plant
pollen and the response range to environmental conditions did not suffered significant changes from past species and there is a dynamic equilibrium of distribution
patterns with climate change until present equilibrium (Huntley 2001). One major
drawback of pollen analysis is the non linear equivalence between pollen abundance
and abundance of mother plants (Odgaard 1999). Nevertheless, it is necessary to
deal with this complexity and biased input because it provides a quantitative evidence that can be subjected to statistical analysis (Williams et al. 1998).
A possible way to use this information is converting pollen percentages to
biomes through plant functional types (Prentice et al. 1996). PFTs are assemblages
of plant taxa that occur in similar environmental conditions, despite its phylogeny
(Prentice et al. 1996, Rusch et al. 2003). Signatures from plant species (e.g. leaf form,
phenology, climatic thresholds and others) are used as functional traits that inhibit
or promote growth under certain conditions, grouping similar taxa together (Grime
et al. 1997, Prentice et al. 1996, Rusch et al. 2003). Therefore, the usage of PFTs
throughout a time span discloses patterns of flora migrations in presence of stressful
conditions (Rusch et al. 2003). Setting up biomes from PFTs is the following step to
produce useful information from pollen percentages. Biomes stress the link between
plant presence and environmental conditions as they are combinations of PFTs. The
major advantage of biomes is that they can predict global distributions and may be
compared spatially, temporal and with biomes resulting from other climatic reconstructions, such as the global circulation models of climate where climatic parameters
are derived from mathematical simulations (Prentice et al. 1996, Williams et al.
Introduction | 
1998). Biomes reconstructions based on paleopalynology are supported by direct evidence of the past, therefore, they may serve as benchmark to other indirect models
(Prentice et al. 1996, Prentice et al. 1998). Successful comparisons between past
biomes and modern pollen data exhibited a congruence between past and present
biomes and detected anthropogenic influence in its distribution (Prentice et al.
1996). Biomes also served as benchmarks for other climatic simulations and discrepancies were found due to inaccuracies in climate simulations, in biomes derived by
simulations and in methods for biomization; although the latter was not the major
source of error (Williams et al. 1998). The behaviour of both reconstructions (based
in direct and indirect data) was also assessed by others authors ( Jost et al. 2005)
using high resolutions simulations and it was found a general correspondence
between models, with minor temperature discrepancies in Western Europe.
The reconstruction of past landscapes may follow several methodological
pathways. Nevertheless, those based in direct past evidence provide accurate dating,
yield good benchmarks to all others studies, and carry important information about
past vegetation processes, especially when studied from a spatial point of view.
1.4 GIS in past vegetation reconstructions
The number of palynological sites being studied is increasing due to great interest in
past reconstructions. Dealing with a high amount of data from each site and to assess
the larger scale patterns of all analysed sites is arduous and requires a large database
linked to a Geographical Information System (GIS). This tool assigns a spatial
context to acquired data, making the visualization and quantitative analysis easier.
The need for spatially explicit reconstructions grants the continuous growing of
paleobiogeographical research in the future with the increasing usage of this tool
(Stigall & Lieberman 2006).
The most notorious advantage of GIS is the map construction, where is possible to assemble all data and discern areas of greater or lesser uncertainty in concern
to samples distribution (Prentice et al. 1998). The reconstruction of species past distributions has been an important research field and this tool allows to work with
every scale needed, ranging from local to continental, enhancing the visualization of
species migration patterns with precision (Stigall & Lieberman 2006).
Paleobiogeography expands the normal temporal scale in ecologic studies to geologic
timescale, thus making possible the examination of distribution patterns throughout
time (Rode & Lieberman 2004).
What is the multivariate nature of the palynology data that GIS have to deal
with? These type of data carries evidences from the past revealing several processes
acting simultaneously which shaped the vegetation composition at each site (Huntley
 | Introduction
2001). GIS have a great power to deal with this complex information, as it provides
tools to analyse qualitatively (by discerning patterns) and quantitatively with the use
of traditional and spatial statistics (Stigall & Lieberman 2006). Several past reconstructions have been made with the aid of GIS. Prentice et al. (1996), in the scope of
BIOME 6000 project, mapped biomes at sampled sites for the LGM and midHolocene (Fig. 1.3). This study separated biomes geographically and gave insights of
distribution changes during late-Quaternary. Williams et al. (2004) made an extensive study of biomes in North America, encompassing United States of America and
Canada, with a time lag of 1.000 years between mapped distributions. Paez (2001)
used in Argentina spatial interpolations techniques to improve the mapping features
of modern pollen vegetation. These studies suggested that GIS can produce a large
quantity of results easily interpreted and the increasing processing power along with
the development of new GIS tools are indispensable in past reconstructions.
Fig. 1.3 – Biomes reconstructed for present time,
6ky BP and 18 ky BP
The most widely used
method to reconstruct past
vegetation is the classification
of assemblages in Biomes.
The BIOME 6000 group
has mapped the worldwide
distribution of biomes based
on the available pollen sites.
Data used to produce these
maps is freely available to the
scientific community from
the website: http://www.
bridge.bris.ac.uk/resources/
Databases/BIOMES_data
[Prentice et al. (2000), Harrison et al. (2001), Bigelow
et al. (2003), Pickett et al.
(2004)].
Introduction | 
1. 5 Objectives
The effects of climatic shifts during the late-Quaternary on the distribution of plants
throughout time have been reported at world and continental scales. However, there
is a lack of information for regional scales especially inside refuge areas. Therefore,
the main purpose of this study is to reconstruct the past vegetation cover in the
Iberian Peninsual and the Balearic Islands. This was subdivided in:
1) Reconstruct the distribution of plant genera, PFTs and biomes in the
Iberian Peninsula and the Balearic Islands for late-Quaternary with a 1.000 years
time interval. Past distribution maps are reconstructed with interpolation algorithms
from pollen counts in a GIS environment.
2) Relate the distributions of biomes and climate data. Biomes are linked to
climate oscillations and warmer and colder areas defined by biomes are depicted
throughout space and time with support of quantitative analysis with independent
temperature.
3) Identify probable areas for the persistence of plant genera throughout time.
With the results from objectives 1) and 2) it will be analysed the possible migration
routes and delimited the probable refuge areas for tree genera in the Iberian
Peninsula and the Balearic Islands.
Additionally, several tools to automate processes of biomization and creation
of large datasets of distribution maps were produced. Scripts developed in Python
and Visual Basic for Applications programming languages inside a GIS environment
will generate an output with quantitative comparisons between the resulting maps.
The application of these scripts are explained in the methods section and presented
in Appendices II to IX.
 | Introduction
 Data and Methods
2.1 Study Area
The study area covers the Iberian Peninsula and the Balearic Islands. The Iberian
Peninsula is located in the western-most mainland Europe and includes Portugal
and Spain with an area of ~580.000 km2 (Fig. 2.1). The northern and western continental shelves are bathed by the Atlantic Ocean, whereas at the south and eastern it
is bordered by the Mediterranean Sea. It is isolated from the rest of Europe except
by the Pyrenees mountain chain. The Strait of Gibraltar is the most southern part of
the peninsula and separates it from the African continent.
The high plateaus prevail in the Iberian Peninsula, divided by the Central
Mountain System, into Northern and Southern Plateaus. The plateaus are isolated
from the sea by the Cantabrean Mountains in the north and the Baetic Mountains
in the south. The north-eastern area is covered by the Iberian Mountain System,
Fig. 2.1 – Study area
The study area comprises the
Iberian Peninsula and the
Balearic Islands. The main
topographic futures of this
peninsula are the mountain
systems with a west-east orientation in the north (Cantabrean mountains), centre
(Central Mountains) and
south (Baetic Mountains).
There are also the Pyreenes
and the Iberian Mountains,
located in eastern Iberia.
Main rivers include the Ebro,
Tejo, Douro, Guadalquivir,
Guadiana and Minho.
Data and Methods | 
parallel to the Ebro River which flows to Mediterranean Sea. All remaining main
rivers flow to the Atlantic Ocean.
The Balearic Islands are an archipelago in the Mediterranean Sea, located at
approximately 200 km from the eastern coast of Iberian Peninsula (Fig. 2.1). It is
composed by four islands: Majorca, Minorca, Ibiza and Formentera with a total area
of 5.000 km2.
The Iberian Peninsula is divided in two macrobioclimatic areas: the Temperate
zone mainly in the north and the Mediterranean zone, occupying a large area of the
centre and south of the peninsula (Rivas-Martínez et al. 2004) (Fig. 2.2). The latter
is characterized by less then two consecutive arid months during the warmest period
of the year. Therefore, the average precipitation (in mm) of the two warmest months
in summer is lesser than the double of the average temperature (in ºC) of the same
two months (Rivas-Martínez 2005). The Temperate bioclimate expands through
places where there less than (or it is balanced) two or more consecutive arid months
in the summer, i. e., when the average precipitation value (in mm) of the period of
the two warmest months of the summer is higher then the average temperature (in
ºC) of the same period (Rivas-Martínez 2005).
2.2 Dataset
A total of 77 palynological sample sites were analysed (table 2.1). This dataset is
composed by 53 digitized sites collected from published pollen diagrams and 24
samples from the European Pollen Database (http://wdc.obs-mip.fr/epd/epd_
Fig. 2.2 – Iberian Peninsula
Bioclimatic zones
The Iberian Peninsula has a
pronounced differentiation
between bioclimatic zones.
In the north dominates the
Temperate bioclimate, with
colder temperatures and
higher precipitation than the
Mediterranean. This bioclimate occurs mainly in the
south and central peninsula.
 | Data and Methods
No.
Src.
Author
Year
Latitude
Longitude
Site name
1
2
D
Desprat, S.
2003
42.2345
-8.7895
Ria de Vigo
D
Carrión, J. S.
2002
36.9
-2.91667
3
Sierra de Gádor
D
Múgica, F. F.
1998
40.8
-3.93
Rascafria (Sierra de Guadarrama)
4
D
Santos, L.
2000
42.64
-7.01
Laguna Lucenza (Sierra de Courel)
5
D
Santos, L.
2000
42.18
-7.29
Fraga (Sierra de Queixa)
6
D
Múgica, F. F.
2001
41.95667
-3.935
Espinosa_Cerrato
7
D
Carrión, J. S.
2001
38.8
-2.36667
Villaverde
8
D
Goñi, M. F. S.
1999
42.03333
3.033333
Las Pardillas
9
D
Leira, M.
2002
42.6
-3.4
Laguna Lucenza
10
D
Valero-Garcés, B.
2000
41.50278
-0.73333
Salada Mediana (Ebro Basin)
11
D
Sobrino, C. M.
2004
42.11667
-6.71667
Lleguna (Lago de Sanabria)
12
D
Sobrino, C. M.
2004
42.13333
-6.7
Laguna de las Sanguijuelas (Lago de Sanabria)
13
D
García, M. J. G.
2002
42.02389
-2.75
Hoyos de Iregua (Sierra de Cebollera)
14
D
Santos, L.
2003
38.08333
-8.78333
Santo André
15
D
Ramil-Rego, P.
1998
42.04
-8.87
Lagoa de Marinho
16
D
Ramil-Rego, P.
1998
43.6
-7.8
Mougás
17
D
Ramil-Rego, P.
1998
43.5
-7.69
Pena Vella
18
D
Ramil-Rego, P.
1998
42.71
-7.21
Chan do Lamoso
19
D
Ramil-Rego, P.
1998
41.91
-8.19
Pozo do Carballal
20
D
Ramil-Rego, P.
1998
42.77
-3.6
La Piedra
21
D
Sobrino, C. M.
2001
42.58333
-7.11667
Laguna de Lucenza
22
D
Carrión, J. S.
2002
38.4
-2.5
Siles
23
D
Carrión, J. S.
2001
38.06667
-2.7
Cañada de la Cruz
24
D
van der Knaap, W.O.
1995
40.34167
-7.57639
Charco da Candieira A
Table 2.1 – Dataset origin
The data of sampled sites
have two possible origins:
they were digitized (D) from
published pollen diagrams
(53 sites) or they were raw
pollen counts from the EPD
(24 sites). While the digital
raw counts offer more resolution, the digitized data were
important to fill out the gaps
of the sampled network in the
study area. (Continues in the
next page)
main.html, last accessed in May 2007). The sample network covers all Iberian
Peninsula and Balearic Islands but it is not uniformly distributed. The south-western
portion of the Iberian Peninsula is less sampled with only 15% of the sampled sites
(Fig. 2.3). Sampled sites do not share the same sampled ages (Fig. 2.4), as a conse-
Fig. 2.3 – Sampled points
distribution
The symbol l represents the
digitized dataset, whereas the
symbol p represents the dataset provided by EPD. The
southwestern area has less
coverage, nevertheless there is
a reasonable distribution of
sampled sites.
Data and Methods | 
Table 2.1 – Dataset origin
Continued.
 | Data and Methods
No.
Src.
Author
Year
Latitude
Longitude
Site name
25
D
van der Knaap, W.O.
1995
40.34167
-7.57639
Charco da CandieiraB
26
D
van der Knaap, W.O.
1995
40.34167
-7.57639
Charco da CandieiraC
27
D
van der Knaap, W.O.
1995
40.34167
-7.57639
Charco da CandieiraD
28
D
van der Knaap, W.O.
1995
40.34167
-7.57639
Charco da CandieiraE
29
D
Zapata, M. B. R.
2002
42.02
-3.04
Quintanar de la sierra
30
D
Valiño, M. D.
1999
39.07
-3.86
La Cuenca alta
31
D
Múgica, F. F.
2001
41.18
-3.11
Turbera de pelagallinas
32
D
Sobrino, C. M.
2005
43.53
-7.57
Chan do Lamoso
33
D
Sobrino, C. M.
2005
43.55
-7.5
Penido Vello
34
D
Sobrino, C. M.
2005
43.07
-3.67
Puerto de los Tornos
35
D
Sobrino, C. M.
1997
42.70556
-7.11111
Suárbol
36
D
Sobrino, C. M.
1997
42.86389
-6.85278
A Golada
37
D
Sobrino, C. M.
1997
42.76806
-6.85
Brañas de Lamela
38
D
Sobrino, C. M.
1997
42.70556
-7
Pozo do Carballal
39
D
Sobrino, C. M.
1997
42.87778
-6.99722
A Cespedosa
40
D
Sobrino, C. M.
1997
42.87778
-7
Porto Ancares
41
D
Valiño, M. D.
2002
39.08333
-3.86667
La Mancha plain
42
D
Taylor, D. M.
1998
38.82
-2.32
El Jardin
43
D
Taylor, D. M.
1998
38.66667
-2.42
Alcaraz
44
D
González-Sampériz, P.
2004
42.8
-0.39778
Portalet
45
D
van der Knaap, W. O.
1997
40.3375
-7.57972
Charco_da_Candieira
46
D
van der Knaap, W. O.
1997
40.36333
-7.64167
Lagoa Comprida 1
47
D
van der Knaap, W. O.
1997
40.33917
-7.61111
Charca_dos_Cões
48
D
van der Knaap, W. O.
1997
40.33556
-7.605
Lagoa Clareza
49
D
Stevenson, A. C.
1985
37.16
-6.84
Laguna de las madres 2
50
D
Stevenson, A. C.
1988
37.11667
-6.5
El Acebron (Huelva)
51
D
Múgica, F.
2001
41.26667
-3.11667
Pelagallinas
52
D
Julià, R.
1998
39.98889
-1.87361
La Cruz
53
D
Múgica, F.
2005
41.32003
-4.14697
El Carrizal
54
EPD
Burjachs, F.
1994
39.79278
3.119167
Albufera Alcudia (Balearic Islands)
55
EPD
Yll, E-I.
1997
39.94056
3.958611
Algendar (Balearic Islands)
56
EPD
Mariscal, B.
1993
43.11778
-4.01667
Alsa
57
EPD
Pantaleon Cano, J.
1997
37.20833
-1.82361
Antas
58
EPD
Penalba, C.
1989
43.25
-1.55
Atxuri01
59
EPD
Perez-Obiol, R.
1994
42.13333
2.75
Banyoles
60
EPD
Penalba, C.
1989
43.03333
-2.05
Puerto de Belate
61
EPD
Yll, E-I.
1997
39.93694
3.965
Cala Galdana (Balearic Islands)
62
EPD
Yll, E-I.
1997
39.87056
4.131389
Cala’n Porter (Balearic Islands)
63
EPD
Mariscal, B.
1983
43.11667
-4.36417
Cueto de Avellanosa
64
EPD
Yll, E-I.
1997
39.875
4.126389
Hort Timoner (Balearic Islands)
65
EPD
McKeever, M.H.
1984
43.05
-6.15
Lago de Ajo
66
EPD
Allen, J.R.M.
1996
42.21667
-6.76667
Laguna de la Roya
67
EPD
Carrion, J.S.
1996
39.1
-0.68333
Navarres (core 1)
68
EPD
Carrion, J.S.
1996
39.1
-0.69
Navarres (core 2)
69
EPD
Mariscal, B.
1986
43.21556
-4.43611
Pico del Sertal
70
EPD
Mariscal, B.
1989
43.12139
-3.70056
Puerto de las Estaces de Trueba
71
EPD
Penalba, C.
1989
43.15
-3.43333
Puerto de Los Tornos
72
EPD
Penalba, C.
1989
42.03333
-3.01667
Quintanar de la Sierra
73
EPD
Pantaleon Cano, J.
1997
36.79444
-2.58889
Roquetas de Mar
74
EPD
Penalba, C.
1989
43.05
-2.71667
Saldropo
75
EPD
Pantaleon Cano, J.
1997
36.77361
-2.60139
San Rafael
76
EPD
Hannon, G.E.
1985
42.1
-6.73333
Sanabria Marsh
77
EPD
Yll, E-I.
1997
39.92472
4.027222
Sou Bou (Balearic Islands)
Number of sampled sites
quence of differences in the methodology applied to palynological analysis. There are
sampled sites with higher temporal resolution, such as the Banyoles site (no. 59 in
table 2.1) that extends the palynological analysis for 30 ky, ranging from 6 to 35 ky
BP, covering almost all studied timescale. There is a peak at 3 ky BP with 61 sampled
sites and the lower value is at 15 ky BP with 15 sampled sites. Although there is an
evident reduction of the available data towards the past, the study area is reasonably
covered with sampled sites available from 15 to 1 ky BP (Fig. 2.5). To accept as a
valid age to reconstruct past distributions, it was chosen a threshold of 19 sample
sites available as the minimum. This threshold is a trade-off between the number of
palynological sites and their geographic distribution with statistical significance for
the interpolation algorithm (see below, 2.4). The interval to collect pollen percentages was 1.000 years, since 15.000 until 1.000 BP.
The EPD supports a free database with a friendly user interface where data
can be obtained as raw pollen counts. All pertinent data was extracted with the respective 14C age samples. Raw counts were converted to pollen percentages and data
of needed ages were obtained by linear interpolation of calibrated 14C controls
(Elenga et al. 2000, Williams et al. 2004). Digitized pollen diagrams do not provide
the same accuracy as digital data as they present data in pollen percentages instead
of raw pollen counts and taxa with lower presence are often misjudge. Nevertheless,
digitized data was required to extend the sample network. Control ages in 14C dates
obtained at different depths of each sampled point are shown in the published pollen
data diagrams. They were calibrated to real BP dates and the percentages of pollen
of sampling ages were extracted directly from the diagram.
The calibration of 14C dates to real calendar was executed with the OxCal 4ß
software (Ramsey 1995, 2001). This process insures that all ages have passed
through the same calibration process, representing with higher accuracy the pretended age without discrepancies between different methods. INTCAL04 (Reimer et al.
2004) was the chosen curve in the software to interpolate real dates from the uncalibrated 14C samples.
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9
10
11
12
Sampled Ages (ky BP)
13
14
15
16
17
18
19
20
21
Fig. 2.4 – Number of sampled sites by sampled age
The number of available sites
by age is not constant. It
increases until the maximum
at 3 ky BP with 61 sampled
points and decreases until 15
ky BP with a minimum accepted of 19 sampled points.
This value excludes the
present and all years before
15 ky BP.
Data and Methods | 
2.3 Biomization procedure
The biomization method was described in detail by Prentice et al. (1996) and
Prentice & Webb (1998). In general, this method attributes several taxa to PFTs and
afterwards it classifies these PFTs into biomes at each sampled point. An affinity
index is calculated and it weights the biome at each point. Although all biomes have
an affinity to a sample point, the one with the maximum value is assumed to prevail
(Prentice et al. 1996).
The PFTs are groups of taxa assigned by bioclimatic affinity and plants phenology traits and therefore this classification retains much of bioclimatic information
(Prentice et al. 1996, Prentice et al. 1998). Despite the unavailability of standard
methods to classify PFTs and biomes (Prentice et al. 1998), the comparison between
results is needed. Therefore, the current study adopted a nearly universal classification scheme for Quarternary biomes already used by other researchers (Elenga et al.
Fig. 2.5 – Location of sampled sites between 15 and
1 ky BP in Iberian Penisula
and the Balearic Islands
The time resolution differs
between sample points. Some
extended throughout all lateQuaternary while others did
not. This configures a slightly
different network of sampled
point for each age, but the
coverage of the study area
remains the same.
 | Data and Methods
Code
aa
PFT
arctic/alpine dwarf shrub
bec
bs
ctc
ctc
df
ec
g
boreal evergreen conifer
boreal summergreen
cool-temperate conifer
intermediate-temperate conifer
desert forb/shrub
eurythermic conifer
grass
h
sf
heath
steppe forb/shrub
ts
ts
ts
wte
wte
wte
temperate summergreen
cool-temperate summergreen
warm-temperate summergreen
warm-temperate broadleaved evergreen
cool-temperate broadleaved evergreen
warm-temperate sclerophyll shrub
Pollen taxa
Alnus, Betula, Empetrum, Dryas, Rhododendron, Salix, Saxifraga,
Vaccinium
Abies, Picea
Betula, Alnus, Salix
Abies
Cedrus
Ephedra
Juniperus, Pinus subgen. Diploxylon
Poaceae
Table 2.2 – Taxa by PFT
The assignment of pollen
taxa to plant functional types
(PFTs) used as an intermediate process to produce the
Biomes.
Ericaceae, Calluna
Artemisia, Apiaceae, Armeria, Asteraceae, Brassicaceae,
Campanulaceae, Caryophyllaceae, Centaurea, Chenopodiaceae, Dipsacaceae, Ephedra fragilis, Fabaceae, Helianthemum,
Hippophae, Plantago, Polygonum, Rosaceae, Rubiaceae,
Rumex, Sanguisorba, Thalictrum
Alnus, Fraxinus excelsior, Populus, Quercus (deciduous), Salix
Carpinus, Corylus, Fagus, Tilia, Ulmus
Ostrya
Quercus (evergreen)
Buxus, Hedera, Ilex
Olea, Phillyrea, Pistacea
2000, Peyron et al. 1998, Prentice et al. 1996). These biomes were used in several reconstructions and using them in the present study allows a comparison with other
works. A matrix of taxa vs. PFTs (table 2.2) is built with pollen percentages and
crossed with a matrix of PFTs vs. Biomes (table 2.3) to yield a final matrix with
biomes and the allowed taxa with binary values of presence/absence. The affinity
index is given by equation (1):
(1)
where Aik is the affinity index of a sample point k for the biome i. ∑j is the sum
of all taxa and δij is the presence/absence value of taxon j in biome i. The pollen percentage is represented by p subtracted by a threshold pollen percentage (θ). For the
latter it was adopted the universal threshold of 0.5% (Prentice et al. 1996, Prentice
et al. 1998). The incidence of misassignment among relatively species-poor assemblages is reduced, although there may be a nearly identical affinity for several biomes
(Prentice et al. 1998). This occurs because single pollen from different taxa could
have a major effect in biome affinity: a point site with two taxa with 10% yields half
affinity score to a biome than eight taxa with 2.5% (Prentice et al. 1998). These low
pollen counts may derive from long-distance transport during polinization or sample
contamination. This 0.5% threshold does not assure that the long-distance transport
is eliminated, because some taxa can produce large amount of pollen that could
result in high percentages at other local. However, setting a higher value could eliminate positive information from other taxa with low pollen expression. Therefore, a
low but non-zero value is acceptable (Prentice et al. 1998).
Data and Methods | 
Table 2.3 – PFT by Biome
Plant functional types used
to generate affinity scores to
each Biome. The PFTs are
abbreviated with the scheme
shown in table 2.2.
Code
Biome
Plant functional types
CLDE
TAIG
CLMX
COCO
TEDE
COMX
WAMX
TUND
XERO
STEP
DESE
cold deciduous forest
taiga
cold mixed forest
cool conifer forest
temperate deciduous forest
cool mixed forest
broadleaved evergreen/warm mixed forest
tundra
xerophytic woods/scrub
steppe
desert
bs, h
bec, bs, ec, h
bs, ctc, ctc, ec, h, ts1
bec, bs, ctc, ec, h, ts1
bs, ctc, ctc, ec, h, ts, ts1, ts, wte1
bec, bs, ctc, ec, h, ts, ts
ec, h, ts, ts, ts, wte, wte1
aa, g, h
ec, wte, wte2
g, sf
df
2.4 Data visualization and analysis
 | Data and Methods
The genera and PFTs were mapped by interpolating individual or average percentages, respectively, at sample points by ordinary kriging technique at each sample age.
This method estimates weighted linear combinations of data and differs from other
interpolations methods by attempting to maintain a zero mean residual error, i. e.,
unbiased results, and minimizing the variance of the errors (Isaaks & Srivastava
1989). This is achieved by creating a model of the data to calculate the bias and error
variance, otherwise they are unattainable (Isaaks & Srivastava 1989). Kriging
method is also able to incorporate in the model the effects of spatial autocorrelation,
that is intrinsic to biological data (Edwards & Fortin 2001).
Maps obtained by interpolations techniques are equivalent to isopol maps
(Bernabo & Webb 1977, Williams et al. 2004) and it is assumed that pollen percentages reflect the plant densities at sampled age (Bradshaw & III 1985, Williams et al.
2004). In this study, pollen percentages values were divided in four classes through a
geometric interval classification scheme. This process algorithm generates class
breaks based on class intervals with geometrical series. The inverse of geometric coefficient, or the difference between the distances of two classes, can change only once
to ensure that each class range has approximately the same number of values, thus
the change between intervals is consistent (ESRI 2006). This yields maps with distinguishable patterns of distribution despite the variation in pollen percentages peculiar to each genus or PFT.
The biome maps are representations of maximum affinities of the interpolated
affinity surfaces. To produce these surfaces, affinity for every biome was interpolated
by kriging technique from each sample point. The final outcome is a map of biomes
in which each cell represents the biome with the maximum affinity in the interpolated surface. Although there are procedures to resolve tied biome affinities by establishing a priority order of biomes, this study opted to represent areas of biomes co-
dominance. The core areas for the presence of genera was calculated by averaging all
distribution maps for the time span analysed. The resulting map provides the location of the areas where each genus persisted through the late-Quaternary.
In order to reduce the effort of producing a large number of maps, this process
was automated with several scripts. These scripts were developed in Python free programming language (http://www.python.org) and implemented as toolboxes in
ArcGIS 9.2. These include toolboxes for create all affinity surfaces for every biome
using a single interpolation model configured in GeoStatistical Analyst Extension
(appendix II), a builder of classification biome surfaces (appendix III) and analyst of
geographic correlations between rasters (appendix IV). The remaining scripts made
a routine task of smoothing all available rasters files (appendix V), automated the
conversion between raster file types (appendix VI) and the extraction of masked
areas from the original rasters (appendix VII). The capability to use ArcGIS programming objects inside the software was also employed to develop two automating
scripts in Visual Basic for Applications language. These two scripts were an aid to set
up identical classifications and graphical displays to all ages for each distribution (appendix VIII) and export to images with a defined size and resolution (appendix IX).
All raster maps were processed with a 0.045 degree resolution (~5 Km) and
the coordinate system used was the geographic WGS84. All genus and PFTs distribution maps as well the interpolated affinity surfaces were smoothed by an 8 cell
square moving window that collects the surrounding average to each pixel. This
process assured that the spatial autocorrelation of data is not represented as sharp
transitions, hence reducing some artefacts of the interpolation method, and also provides an enhancement of visualization. When pollen percentages data was missing it
was not possible to reconstruct a potential distribution map, which was left blank.
This occurred only at farther ages with single genus distributions, which are the most
sensible due to lack of other taxa to compensate the distribution as it happens in
PFTs assemblages or biomes.
The quantitative analyses were geographic correlations between raster pixels.
To correlate distributions of pollen percentages by Pearson correlation, it was used
the average presence maps from each mapped taxa and PFT. These core presence
sites throughout time were compared to achieve a correlation value of its geographic
distribution. The biomes areas for each year were compared to the average value of
oxygen isotope for the correspondent time interval and extracted the linear trend.
This provides a quantitative relationship between temperature sensitive biomes and
the independent temperature proxy to assess the co-evolution of both. The evolving
biomes areas were also correlated to the evolution of the temperature proxy to quantify a possible positive or negative correlation.
Data and Methods | 
 | Data and Methods
 Results
The pattern of vegetation distribution in the past is considerably different from the
present, notwithstanding the persistence of a stable core of each genus, PFTs or
biome throughout time in specific areas (Fig. 3.1). For genus like Alnus, Betula,
Castanea, Quercus deciduous and, to a lesser extent, Fagus the stable core is located in
north and west of Iberian Peninsula (Fig. 3.1). The southern and eastern area of the
peninsula holds the distribution of Olea, Pistacia and Quercus evergreen throughout
the late-Quaternary (Fig. 3.1). This duality of the global pattern of distribution is
also detectable in the mapped PFTs (Fig. 3.2). The boreal summergreen (bf) and the
temperate summergreen (ts) PFTs follow a north-western gradient, contrasting to
the south-eastern distribution of the cool-temperate broadleaved evergreen (wte)
and steppe forb / shrub (sf) assemblages.
The biomes present the same pattern of distribution (Fig. 3.2). The
maximum affinity scores of Broadleaved evergreen / Warm mixed forest and
Xerophytic woods/scrub biomes occurs at south portion of the Iberian Peninsula.
On the other hand, the Cool mixed forest occurs mainly in the north and the
Temperate deciduous forest extends for a large area of the Iberian Peninsula, with a
similar pattern to the north-western distribution of several genera and PFTs. The
Tundra biome is not present constantly throughout the studied time interval and
appears mainly in the extreme areas of western and eastern Iberia. The distribution
of interpolated affinity surfaces for each biome reveals also this persistent pattern of
distribution (Fig. 3.3). The southern distribution pattern exhibited in some genera,
PFTs and biomes often extends to the Balearic Islands.
3.1 Distribution of plant genera
There is a marked difference between the eight mapped plant genus distribution,
with some showing a general northern distribution and others occurring in the
southern part of Iberia (Fig. 3.1). This difference is supported by a geographic correlation value of the average distribution for the studied time-scale (table 3.1). Alnus,
Results | 
15 ky BP
14 ky BP
13 ky BP
Alnus (%)
0.00 - 0.59
0.59 - 1.48
Betula (%)
0.00 - 3.42
3.42 - 3.97
Castanea (%)
0.0000 - 0.0793
Fagus (%)
0.00 - 0.09
0.09 - 0.45
0.45 - 1.99
1.99 - 100
Olea (%)
0.00 - 0.45
0.45 - 0.47
0.47 - 0.89
0.89 - 100
Pistacea (%)
0.00 - 0.08
0.08 - 0.29
0.29 - 0.78
0.78 - 100
Quercus (deciduous) (%) 0.00 - 8.65
8.65 - 10.84
10.84 - 18.14
18.14 - 100
Quercus (evergreen) (%)
2.38 - 4.53
4.53 - 6.95
6.95 - 100
0.00 - 2.38
1.48 - 3.52
3.97 - 7.12
0.0793 - 0.0812
12 ky BP
3.52 - 100
7.12 - 100
0.081 - 0.1605
0.1605 - 100
Fig 3.1 – Reconstruction of Genera distribution in Iberian Peninsula and Balearic Islands between 15 and 1 ky BP
The distribution of Alnus, Betula, Castanea, Fagus, Olea, Pistacia, deciduous and evergreen Quercus was achieved by direct
interpolation of pollen percentages between 15 and 1 ky BP. These maps can not be compared between taxa as the production
 | Results
11 ky BP
10 ky BP
9 ky BP
Alnus (%)
0.00 - 0.59
0.59 - 1.48
Betula (%)
0.00 - 3.42
3.42 - 3.97
Castanea (%)
0.0000 - 0.0793
Fagus (%)
0.00 - 0.09
0.09 - 0.45
0.45 - 1.99
1.99 - 100
Olea (%)
0.00 - 0.45
0.45 - 0.47
0.47 - 0.89
0.89 - 100
Pistacea (%)
0.00 - 0.08
0.08 - 0.29
0.29 - 0.78
0.78 - 100
Quercus (deciduous) (%) 0.00 - 8.65
8.65 - 10.84
10.84 - 18.14
18.14 - 100
Quercus (evergreen) (%)
2.38 - 4.53
4.53 - 6.95
6.95 - 100
0.00 - 2.38
1.48 - 3.52
3.97 - 7.12
0.0793 - 0.0812
8 ky BP
3.52 - 100
7.12 - 100
0.081 - 0.1605
0.1605 - 100
of pollen differs. Nevertheless, they may be analysed through time inside each taxa to achieve the maximum and minimum
presence and the distributional shifts. The white maps indicate absence of points to model the distributions. Distributions at
time 0 of Alnus, Betula, Fagus, deciduous and evergreen Quercus were adapted from Tenorio et al. (2001) and of Castanea, Olea
Results | 
7 ky BP
6 ky BP
5 ky BP
Alnus (%)
0.00 - 0.59
0.59 - 1.48
Betula (%)
0.00 - 3.42
3.42 - 3.97
Castanea (%)
0.0000 - 0.0793
Fagus (%)
0.00 - 0.09
0.09 - 0.45
0.45 - 1.99
1.99 - 100
Olea (%)
0.00 - 0.45
0.45 - 0.47
0.47 - 0.89
0.89 - 100
Pistacea (%)
0.00 - 0.08
0.08 - 0.29
0.29 - 0.78
0.78 - 100
Quercus (deciduous) (%) 0.00 - 8.65
8.65 - 10.84
10.84 - 18.14
18.14 - 100
Quercus (evergreen) (%)
2.38 - 4.53
4.53 - 6.95
6.95 - 100
0.00 - 2.38
1.48 - 3.52
3.97 - 7.12
0.0793 - 0.0812
4 ky BP
3.52 - 100
7.12 - 100
0.081 - 0.1605
0.1605 - 100
Fig. 3.1 - Continued.
and Pistacia from Inventário Florestal Nacional (http://www.dgrf.min-agricultura.pt/ifn/mapas.htm) and Proyecto Anthos
(http://www.anthos.es/intro_v2.html)
 | Results
3 ky BP
2 ky BP
1 ky BP
Alnus (%)
0.00 - 0.59
0.59 - 1.48
Betula (%)
0.00 - 3.42
3.42 - 3.97
Castanea (%)
0.0000 - 0.0793
Fagus (%)
0.00 - 0.09
0.09 - 0.45
0.45 - 1.99
1.99 - 100
Olea (%)
0.00 - 0.45
0.45 - 0.47
0.47 - 0.89
0.89 - 100
Pistacia (%)
0.00 - 0.08
0.08 - 0.29
0.29 - 0.78
0.78 - 100
Quercus (deciduous) (%)
0.00 - 8.65
8.65 - 10.84
10.84 - 18.14
18.14 - 100
Quercus (evergreen) (%)
0.00 - 2.38
2.38 - 4.53
4.53 - 6.95
6.95 - 100
1.48 - 3.52
3.97 - 7.12
0.0793 - 0.0812
0
3.52 - 100
7.12 - 100
0.081 - 0.1605
0.1605 - 100
Results | 
Betula, Castanea and deciduous Quercus have a markedly north and western distribution and maintain high negative correlation with Olea, Pistacia and evergreen Quercus
which are distributed in the south. Fagus distribution is not related to other genera,
therefore low correlation levels explain the distinct north-eastern core throughout
time.
Alnus species found in palynological analysis had it first strong appearance in
the north of the Iberian Peninsula at 14 ky BP. Its distribution extended to southwest, reaching southern areas of the peninsula, in the following 1 ky, achieving high
values of pollen percentages, never reaching the Balearic Islands. The presence of this
genus decreased during 12 ky BP before a new extension of its distribution area from
the western Iberia. This expansion reaches the Atlantic zone and reasonably maintained its distribution until present time, with a core of high pollen percentage values
in the northwest. This genus has a strong geographic correlation with Quercus deciduous and Betula (Table 3.1). A strong correlation with Quercus evergreen and
Pistacia is found, although negative for both genera.
Betula species preserved a high pollen production throughout time, with
highest values in the half nortwestern part of the Iberian Peninsula. However, it is
noticeable the expansion of the core in the north from 15 to 12 ky BP and its slow
retraction from 10 to 1 ky BP. There is some evidence of this genus presence at 13
and 12 ky BP in the Balearic Islands. An evident negative correlation with Pistacia
genus reveals the different geographic distribution shape.
The evolution of Castanea is peculiar when comparing to the other distribution maps. It has a northern presence and rarely expands further from the nucleus
with high values of pollen percentages. It is near absent from 10 to 6 ky BP but has a
noteworthy recover until 2 ky BP. Its presence in the Balearic Islands was never depicted. It shows a robust correlation of the average presence with deciduous Quercus.
Decidous Quercus is near absent from Iberian Peninsula from 15 to 13 ky BP,
when it is found a distribution at the western most area of the peninsula. It suffered
a retraction at 12 ky BP, with the persistence of a nucleus in the northwest. This core
expanded in direction of the southeast, however with low expression further than
half of the peninsula. From 10 to 4 ky BP it shows a second nucleus at the northeastern portion of the peninsula. The dispersion of these genuses never arrives to the
Balearic Islands. Its distribution correlates positively with Alnus and Castanea and it
is the genus that achieves higher values of pollen percentages.
Olea has a markedly south and east distribution. Its pollen percentages values
decreases until 12 ky BP without loosing a core in the extreme south. From 11 to 6
ky BP, its distribution is unstable, with contraction and expansion phases. At 5 ky
BP it has an energetic expansion, reaching almost all area of the peninsula with high
values of pollen percentages. The presence of Olea in the Balearic Islands is consistent throughout time, only with low pollen values from 13 to 11 ky BP. Although all
 | Results
Alnus
Betula
Castanea
Fagus
Olea
Pistacea
Quercus
(deciduous)
Alnus
1.00
Betula
0.87*
1.00
Castanea
0.76*
0.66
1.00
Fagus
0.33
0.25
-0.04
1.00
Olea
-0.73
-0.82*
-0.55
-0.38
1.00
Pistacea
-0.86*
-0.91*
-0.61
-0.49
0.79*
1.00
Quercus
(deciduous)
0.90*
0.85*
0.90*
0.13
-0.71
-0.83*
1.00
Quercus
(evergreen)
-0.86*
-0.69
-0.59
-0.50
0.50
0.81*
-0.78*
Quercus
(evergreen)
1.00
correlations with other species are distant from zero, the highest correlation is with
Betula.
It was not possible to produce distribution maps for genus Pistacia for the farthest age. Although the remaining distribution points to a south nucleus, at 13 ky
BP it weakly occupied a western area, without visible expression in the east. The ages
of 13 ky BP, 10 ky BP and 1 ky BP had lower values of pollen percentages. From 7
to 2 ky BP it had a constant distribution, with a main area in the south, and expanding to cover most of Iberian Penisula. In the Balearic Islands, Pistacia was present
throughout time with the exception of a lower presence period from 13 to 10 ky BP.
This genus presents a very strong negative correlation with Betula.
The evergreen Quercus genus has a low expression during the farthest years,
from 15 to 11 ky BP, with presence restricted to the south of the Iberian peninsula.
This low expression reached its extreme at 10 ky BP, when its presence is indistinguishable. From then onwards, its presence is constant with a nucleus of high pollen
percentage values moving slightly to east. At 3 ky BP is found a weak presence in the
extreme northwest. In the Balearic Islands, evergreen Quercus presence is very low
until 6 ky BP, when it increases. The correlation values with other genera are higher
or equal to 0.50 (in their symmetric positive values, if negative), being the highest
with Pistacia.
The genus Fagus has a general low pollen percentage values and interpolation
maps for older ages (between 15 and 11 ky BP) were unachievable. From 10 to 1 ky
BP it has a conspicuous expansion from its northern presence. This genus presents
the most different pattern of distribution through time when compared to all other
genus. It has a north core of high pollen percentages, slight shifted to east. Genus
Fagus had throughout time a indistinguishable presence in the Balearic Islands. This
genus has a near zero correlation values with all other genus, with the highest value
with Quercus evergreen.
Table 3.1 – Pearson’s
coefficiente of correlations
between mapped taxa
The correlations are made
between raster cells of the
average distribution through
time of each pair of taxa. This
correlation has a geographic
meaning: positive values indicate persistence of taxa in the
same location, and negative
values persistence at different
locations. Significant correlations (r>0.750) marked
with *.
Results | 
15 ky BP
oxygene isotope
anomaly
2
A
% biome area
13 ky BP
bs (%)
0.00 - 2.39
2.39 - 3.29
3.29 - 5.49
5.49 - 100
ts (%)
0.00 - 2.87
2.87 - 3.51
3.51 - 5.89
5.89 - 100
wte2 (%)
0.00 - 0.32
0.32 - 0.35
0.35 - 0.64
0.64 - 100
sf (%)
0.00- 1.22
1.22 - 1.52
1.52 - 2.16
2.16 - 100
biomes
clmx
comx
tede
12 ky BP
tund
wamx
A
1
0
0
-2
-1
-4
-2
-6
-3
100
B
14 ky BP
B
80
60
40
20
0
16 ky BP
 | Results
15 ky BP
14 ky BP
13 ky BP
Fig 3.2 – Reconstruction of PFTs and Biomes distribution in Iberian Peninsula and Balearic Islands from 15 to 1 ky BP
The distribution of four PFTs and biomes were reconstructed from 15 to 1 ky BP. The chosen PFTs were bs and ts colder assemblages and the warmer wte and sf and their distribution was achieved by the interpolation of average pollen percentage
of member taxa. The actual distribution was not possible to achieve due to low number of sampled sites of paleopollen. The
Biomes maps were accomplished by finding the biome that achieve the maximum value of the interpolated affinity scores by
raster cell. The combined biomes are tied affinity scores between two biomes. Each colour represents a biome or a co-dominance of two biomes (described in the upper legend). The biome map for time 0 was adapted from Prentice (1996). Distributions at time 0 of PFTs bs and ts were adapted from Tenorio et al. (2001) and wte from Proyecto Anthos (http://www.
anthos.es/intro_v2.html). The PFT sf, at time 0, is distrubted throughout all the Iberian Peninsula. Graphic A depicts the
anomalies of temperature related to last 1000 yeast average throughout time. Blue line represents the GISP2 oxygen isotope
record anomaly (Grootes et al. 1997, 1993, Meese et al. 1994, Steig et al. 1994, Stuiver et al. 1995), whereas the red line is
temperature anomaly adapted from Heiri (2003). Graphic B depicts the shifts in area of each biome throughout time.
12 ky BP
10 ky BP
9 ky BP
8 ky BP
bs (%)
0.00 - 2.39
2.39 - 3.29
3.29 - 5.49
5.49 - 100
ts (%)
0.00 - 2.87
2.87 - 3.51
3.51 - 5.89
5.89 - 100
wte2 (%)
0.00 - 0.32
0.32 - 0.35
0.35 - 0.64
0.64 - 100
sf (%)
0.00- 1.22
1.22 - 1.52
1.52 - 2.16
2.16 - 100
biomes
xero
clmx + tund
comx + tede
dese
step
2
1
0
0
-2
-1
-4
-2
-6
-3
Temperature anomaly
11 ky BP
100
80
60
40
20
0
12 ky BP
11 ky BP
10 ky BP
9 ky BP
8 ky BP
Results | 
7 ky BP
6 ky BP
5 ky BP
4 ky BP
bs (%)
0.00 - 2.39
2.39 - 3.29
3.29 - 5.49
5.49 - 100
ts (%)
0.00 - 2.87
2.87 - 3.51
3.51 - 5.89
5.89 - 100
wte2 (%)
0.00 - 0.32
0.32 - 0.35
0.35 - 0.64
0.64 - 100
sf (%)
0.00- 1.22
1.22 - 1.52
1.52 - 2.16
2.16 - 100
biomes
clmx
tede
comx
tund
wamx
A
1
oxygene isotope
anomaly
2
0
0
-2
-1
-4
-2
-6
-3
% biome area
100
B
80
60
40
20
0
8 ky BP
7 ky BP
Fig 3.2
Continued.
 | Results
6ky BP
5 ky BP
4 ky BP
2 ky BP
1 ky BP
bs (%)
0.00 - 2.39
2.39 - 3.29
3.29 - 5.49
5.49 - 100
ts (%)
0.00 - 2.87
2.87 - 3.51
3.51 - 5.89
5.89 - 100
wte2 (%)
0.00 - 0.32
0.32 - 0.35
0.35 - 0.64
0.64 - 100
sf (%)
0.00- 1.22
1.22 - 1.52
1.52 - 2.16
2.16 - 100
biomes
xero
clmx + tund
0
comx + tede
dese
step
2
1
0
0
-2
-1
-4
-2
-6
-3
Temperature anomaly
3 ky BP
100
80
60
40
20
0
4 ky BP
3 ky BP
2 ky BP
1 ky BP
0
Results | 
Table 3.2 – Pearson’s correlation coefficient between
mapped PFTs
The correlations are made
between raster cells of the
average distribution through
time of each pair of PFT.
The persistence of the pair
of PFT throughout time in
the same area are depicted
by positive values, whereas
the negative values indicate
that they do not occur in the
same place.
 | Results
bs
ts
sf
bs
1
ts
0.67
1
sf
-0.66
-0.58
1
wte
-0.73
-0.44
0.46
wte
1
3.2 Distribution of Plant Functional Types
It was mapped the past distribution of four PFTs (Fig. 3.2). These plant associations
have different climatic preferences which are revealed by the shifting distribution
patterns throughout time. The correlations values among PFTs are indicative of geographic position of the core throughout time. The overall cline of distribution is
similar to genera distribution: a northwest to southeast gradient or reverse, depending on plant association characteristics.
The PFT bs exhibits a northern distribution during the end of the Pleistocene.
At 14 ky BP the core strengthens and expands towards the south and east with
strong presence in the west and the northeast from 13 to 10 ky BP. From 10 to 3 ky
BP, this assemblage has a solid presence in the north and in the western portion of
Iberian Peninsula. At 1 ky BP there are no evidences of intense presence in the
eastern half of the peninsula. Although there are no strong correlations among PFTs,
functional type bs shows a trend for a negative geographic correlation with PFT sf
(Table 3.2). This PFT presence in the Balearic Island is just noticeable from 13 to 12
ky BP.
During older ages, evidence of PFT ts was absent from Iberian Peninsula. It
emerged in the western area at 13 ky BP and retracted at 12 ky BP. Its presence was
constant in the west and north of the Iberian Peninsula, expanding into almost all
areas. The easternmost area was never permanently occupied, except at 6 and 3 ky
BP. During the latter period, the PFT distribution assumes two cores: one in the
northwest and other in the southeast. This south eastern core extends to the Balearic
Islands and it’s the exclusive presence of this PFT in the archipelago. The average ts
distribution presents a positive correlation with bs and a negative with sf and wte.
The PFT sf dwelt in all area of Iberian Peninsula through 15 to 12 ky BP,
with a retraction in the westernmost area. The distribution of pollen percentages at
11 ky BP suffered its largest contraction and it was confined to the south of Iberia,
disappearing also from the Balearic Islands, where this PFT presence was constant
throughout time. From then onwards, it expanded and covered the southern and
eastern half with a notable exception at 7 ky BP, when it had a decrease of pollen
percentage.
tede
comx
wamx
tund
xero
n
15
11
15
12
4
r
0.60
0.48
0.73
-0.83
-0.23
The presence of wte functional type pollen in the Iberian Peninsula is constant through time in the south and east. It had several rapid expansions phases. At
11 ky BP its distribution increased to all areas aside from the north and at 9 ky BP it
had the same spreading behaviour. Since 8 ky BP its pollen presence grew from the
south, covering all Iberia until 1 ky BP with no considerable discrepancies in its presence. The presence in the Balearic Islands of this PFT fluctuates throughout time. It
is absent from 13 to 11 ky BP and again at 9 ky BP. Its average geographic presence
is not correlated with other PFTs (Table 3.2).
Table 3.3 – Pearson’s correlations coefficient between
Biomes area and δ18O
temperature proxy
The values of temperature
proxy were averaged for each
studied age and correlated
with biomes areas from 15 to
1 ky BP. The values of n refer
to the number of available
ages when the biome was
present.
3.3 Distribution of Biomes
The biomes past distribution exhibit an evolving pattern similar to individual genera
and PFTs distributions (Fig. 3.3). The presence of tund at 15 ky BP is evident and
occupies almost 36% of the available area with two main and geographically distinct
cores (Fig. 3.2). Until 13 ky BP its area decreased to 22% but at 12 ky BP it suddenly
increased to 55%. From then onwards, its presence was never expressed with values
upper than 8 % and it was not present from 7 to 4 ky BP. During the three latter
studied ages there was an increase of tund occupancy with a peak value at 2 ky BP
(14% of total area).
The tede biome had the most constant area throughout time, always with
high occupancy, ranging from 43 to 78% of total available area. The lowest value
occured at 15 ky BP, coinciding with an increase of tund, and the highest at 10 ky
BP. From 15 to 10 ky BP, and also at 3 ky BP, there was mixtures with comx biome
due to locations with the same affinity values. The comx was present from 15 to 6
ky BP with occupancy areas ranging from 3 to 25% (12 ky BP and 8 ky BP, respectively). Its presence was visible again at 3 ky BP.
Like biome tede, the wamx was present throughout all studied timescale.
The most variable era is from 15 to 10 ky BP where its presence is revealed in occupancy values from six to 21%. It suffered a decrease in area from 15 to 12 ky BP, increasing during the following age. At 10 ky BP its presence was reduced and then it
stabilised around 25% of total area occupied. On the other side, the biome xero had
a scarce appearance with highest occupied area of 3% of available area at 9 and 1 ky
BP. Although this biome was present at 2 and 8 ky BP, its presence was lower then
Results | 
15 ky BP
14 ky BP
13 ky BP
clde
0.26 - 5.07
clmx
1.96 - 6.87
coco
12 ky BP
5.07 - 7.88
11 ky BP
10 ky BP
9 ky BP
7.88 - 9.51
9.51 - 12.32
6.87 - 9.26
9.26 - 11.65
11.65 - 16.56
1.96 - 6.87
6.87 - 9.26
9.26 - 11.65
11.65 - 16.56
comx
2.98- 10.45
10.45 - 13.31
13.31 - 16.17
16.17 - 23.64
dese
0 - 0.001
0.001 - 0.045
0.045 - 0.312
0.312 - 1.922
step
7.50 - 11.73
11.73 - 14.35
14.35 - 18.58
18.58 - 25.41
taig
1.87- 6.42
6.42 - 8.92
8.92 - 10.29
10.29 - 12.79
tede
3.10 - 10.90
10.90 - 13.72
13.72 - 16.53
16.53 - 24.33
tund
2.46 - 8.16
8.16 - 11.68
11.68 - 13.86
13.86 - 17.39
wamx
5.00 - 10.26
10.26 - 11.85
11.85 - 13.44
13.44 - 18.71
xero
0.56- 2.22
2.22 - 3.02
3.02 - 4.68
4.68 - 8.08
8 ky BP
Fig. 3.3 – Interpolated affinity surfaces of individual Biomes in the Iberian Peninsula and the Balearic Islands between
15 and 1 ky BP.
Individual biomes were mapped with the interpolation of affinity scores. Each site has an affinity value to each biome which is
spatially interpolated using kriging method to all cells in the Iberian Peninsula and Balearic Islands, creating a surface of affinity values. The distribution at time 0 of interpolated affinity surfaces was not possible to achieve due to a lack of sample sites.
 | Results
7 ky BP
6 ky BP
5 ky BP
clde
0.26 - 5.07
clmx
1.96 - 6.87
coco
4 ky BP
5.07 - 7.88
3 ky BP
2 ky BP
1 ky BP
7.88 - 9.51
9.51 - 12.32
6.87 - 9.26
9.26 - 11.65
11.65 - 16.56
1.96 - 6.87
6.87 - 9.26
9.26 - 11.65
11.65 - 16.56
comx
2.98- 10.45
10.45 - 13.31
13.31 - 16.17
16.17 - 23.64
dese
0 - 0.001
0.001 - 0.045
0.045 - 0.312
0.312 - 1.922
step
7.50 - 11.73
11.73 - 14.35
14.35 - 18.58
18.58 - 25.41
taig
1.87- 6.42
6.42 - 8.92
8.92 - 10.29
10.29 - 12.79
tede
3.10 - 10.90
10.90 - 13.72
13.72 - 16.53
16.53 - 24.33
tund
2.46 - 8.16
8.16 - 11.68
11.68 - 13.86
13.86 - 17.39
wamx
5.00 - 10.26
10.26 - 11.85
11.85 - 13.44
13.44 - 18.71
xero
0.56- 2.22
2.22 - 3.02
3.02 - 4.68
4.68 - 8.08
Results | 
1% of the total area.
Each biome for every sample point has a calculated affinity index and it is
assumed the presence of the biome with higher affinity (Fig. 3.3). The interpolated
affinity surfaces created reveal, for each biome, the same northwest – southeast gradient observed in mapped genera and PFTs (Fig. 3.2). There are biomes that exhibits
higher affinity values to the northwest (clde, clmx, coco, comx, taig, tede, tund
and wamx) and others in the southeast (dese, step and xero). The coco and
clmx have a very similar distribution due to homogeneous composition in PFTs
and, therefore, similar taxa. Affinity surfaces also disclose the dimension of each
biome through Iberian Peninsula. Despite expansion to other areas, biomes clde,
clmx, coco, comx, taig, tede, tund and wamx are restricted to a small area at
the north while dese, step and xero have a more broad distribution. During recent
ages (mainly after 5 ky BP), all biomes expanded, occupying a specific area in the
Iberian Peninsula.
3.4 Relationships between Biomes and environmental conditions
 | Results
25000
20000
Area (km2)
Fig. 3.4 – Linear relation of
biome area with oxygen isotope as a temperature proxy
The area of biomes is
temperature dependent. This
graph depicts the relation of
each biome with the increasing of temperature proxy
value. The tede, comx and
wamx biomes have, generally,
their area increased with
the increasing temperature,
whereas the tund biome
has a decreasing area. Biome
xero reveals a trend for a
negative relationship with
temperature but it is only
present in warmer periods.
Apparently there is a relationship between the spreading of biomes and temperature: higher temperatures or proxy’s values are favourable to warmer biomes,
whereas colder biomes expand with lower values (Fig 3.4). There is a linear relationship between the area occupied by each biome and δ18O temperature proxy. Tede,
wamx and comx have their area increased with temperature analogue augment,
with a high positive correlation throughout time for the first two (Table 3.3). Comx
has a lower correlation value, although it is possible to infer a positive relation with
temperature. Tund has a negative linear relation with temperature, decreasing with
higher values of δ18O, and the high correlation evidences severe contraction of its
distribution with increasing temperature. Although is possible to depict a general
tendency of biome xero to decrease with the increasing temperature, this biome is
only present at the most warm temperatures. Therefore, it is a biome dependent on
heat conditions.
tede
comx
wamx
tund
xero
15000
10000
5000
0
-41
-40
-39
-38
-37
δ 18O (‰)
-36
-35
-34
3.5 Distribution of persistence areas of plant genera
The core presence of each genus occurred in distinct locations of the Iberian
Peninsula and the Balearic Islands (Fig. 3.5). Nevertheless, there is a broad pattern
that clusters genus Alnus, Betula, Castanea and evergreen Quercus in the northeastern portion of Iberia, while Olea, Pistacia and deciduous Quercus cores dwell in
the south-eastern area. Genus Fagus had the most different location, in the northeast
of Iberian, near the Pyrenees Mountains. In the core of the genus Olea has a noticeable presence in the eastern and southern Iberia, although its presence extends
between those areas. In the Balearic Islands is noticeable the presence of one core of
Olea and, at a lesser extent, Pistacia and deciduous Quercus.
Fig. 3.5 – Stable core areas
of each mapped genus.
The pollen percentage maps
of each age were combined
together through their average values for each genus. The
resulting map indicates the
constant presence locations of
genera from 15 to 1 ky BP.
(continues on the next page)
Results | 
Fig. 3.5 – Stable core areas
of each mapped genus.
continued.
 | Results
 Discussion
Deglaciation was not a monotonic event (Alley & Clark 1999, Folland et al. 2001).
Therefore, the shiftable patterns throughout time of vegetation distribution in the
Iberian Peninsula were expected and confirmed with the present work. In the
Balearic Islands, the distribution of vegetation and range shifts follow the same patterns as south-eastern Iberia. These shifting patterns of vegetation persistence and
migrations during the late-Quaternary exposed in this study are coherent with other
world and European scale reconstructions (Prentice et al. 1996, Elenga et al. 2000),
climate change (Grootes et al. 1997, Heiri 2003), predictions of vegetation presence
(Garzón et al. 2007), historical facts (Conedera at al. 2004) and molecular phylogenies (Belaj et al. 2007, Besnard et al. 2002, 2007, Dumolin-Lapègue et al. 1997,
Fineschi et al. 2000, King & Ferris 1998, Kropf et al. 2006, Lumaret et al. 2004,
Magri et al. 2006, Maliouchenko et al. 2007, Olalde et al. 2002, Palme et al. 2003,
Petit et al. 2002, 2005, Taberlet et al. 1998).
During the late-Quaternary in the Iberian Peninsula, Steppe was a dominant
biome, as previously reported to Mediterranean areas with a probable savannah type
structure like those that can be found nowadays in arid areas of Spain (Elenga et al.
2000). This dominance is due to presence of high number of families with low pollen
percentages. As Prentice (1996, 1998) asserted, the square-root included in equation
used to calculate biome affinity maximize the detection of low abundance taxa.
Therefore, a high value of affinity to Steppe was expected, as minor indicator taxa is
valuable to the biomization method. This comprises families as Chenopodiacea,
Poaceae and genus like Artemisia that have a high number of species and a widely
distribution in sample points, thus, despite the low pollen percentage values, a high
affinity is achieved as a consequence of the assignment of numerous vegetation types
to biome Steppe (Elenga et al. 2000). Furthermore, the affinity scores originated
from digitized data could be biasing the scores for this biome due to lower resolution
of minor taxa pollen. Raw pollen counts should be used to better assess the affinity
score.
The second best affinity scores reveal that the temperate deciduous and cool
mixed forests were dominants in Iberian Peninsula since 15 ky BP. Extreme winters
Results | 
along with arid summers may have reduced the distribution of Mediterreanean evergreen trees and shrubs, resulting in Steppe arising at high altitude lakes (Elenga et al.
2000, Prentice et al. 1992). For biome mapping, Steppe was excluded to enhance
perception of evolving biomes distribution and area occupied in each time frame.
Elenga et al. (2000) highlighted the important information provided from these
runner-up biomes for the analysis of vegetation dynamics in Western Europe and
suggested a further division or different classification of treeless biomes.
A modern biome map was adapted from literature due to limitations with the
available number of sites with data for present. Moreover, anthropogenic factors have
a strong effect in biomes classification, misleading it. Heatlands in southern Europe
are usually misidentified as Tundra due to presence of Ericales, Poaceae and
Cyperacea pollen, which are indistinguishable at a biome scale (Prentice et al. 1996).
The number of available data points is a major constrain in this type of reconstructions. In the present study, this was counterbalanced by a reasonable geographic distribution of pollen sites. This non uniform distribution left a low sampled
area in southwestern Iberian Peninsula. This area comprises the southern plateau
and it is topographically homogenous, thus it is not expected great variation in its
vegetation composition at each age, and the available data should allow good predictions. Areas with more heterogeneous terrains have normally a good composition of
sampled points. Thus, small variations due to the presence of more possible natural
barriers to climate and dispersion should be predicted. Another constrain of this
strategy to reconstruct past environments is the non-stability of available points
throughout time. Some data have a higher temporal resolution and extended the
provided information throughout almost all timescale, while others represent just a
small portion. These latter data were used to balance missing data in some locations
and at some ages, otherwise it was used to increase the resolution of the analysis.
Therefore, it was chosen for analysis a timescale between 1 and 15 ky BP derived
from the limitation of available points, while taking into account their geographic
distribution.
The data used to yield distribution maps, the fossil pollen record, has some inherent difficult issues to deal, such as the unknown relationships between pollen
data and the density of mother plants (Odgaard 1999). Therefore it is an extremely
complex task to extract densities from pollen records. Nevertheless, fluctuations in
the amount of pollen found in sampled locations for the same taxa are indicative of
higher or lower presence throughout time. Furthermore, finding pollen of one taxon
or assemblage is an incontestable evidence of its presence at some age. The rapid
change of tree presence and migrations is perceived within the thousand year interval
as response to climate. Those changes were not constant at spatial and temporal dimensions (Clark 1998). Using the available paleovegetation data it is possible to validate of the information internally due to the spatial autocorrelation of biological data
 | Results
throughout time: geographic closer points at certain age have similar pollen composition. This is reflected in the consistent patterns of paleopollen distribution. Despite
all pollen associated issues, such as accurate calibration and sufficient density of sites,
this is a valuable source of information for past vegetation composition and historical factors (Petit et al. 2004).
Ecological information is lost in the transformation of pollen data to biome
maps (Williams et al. 2004) but this method presents several advantages that are
useful to compare between different zones of the world, timescales and methods.
The synthetic information provided enlightened areas of congruent climatic and vegetation features. Also they not necessarily rely on large data sets of data to yield
results (Prentice et al. 1996, Prentice et al. 1998), like is often the case of paleovegetation reconstruction. The intermediate stage between the assignments of taxa to
biomes is the classification in PFTs. This classification is sensible to changes in vegetation and biodiversity as consequence of environmental shifts at different scales
(Rusch et al. 2003) and, taken together, the classified taxa represent a very characteristic climate range.
4.1 Correlations with climate data
The late-Quaternary, as already seen, was a period of intense climatic oscillations,
ranging from the LGM to modern temperatures, with abrupt changes and fluctuations. The analysis of the relationship between temperature abnormalities and the
occupied areas by each biome reveals the major events of the Holocene (Fig. 3.2 and
3.3). These events had an extreme importance in the evolving patterns of vegetation
distribution, producing expansion and retraction phases and the consequent sudden
emergence and extinction of biomes. Imprints of climate shifts are evident and assessable through the distribution maps.
Oldest dryas and Bölling-Alleröd – From LGM to the first warming phase
The peak of last glacial maximum was at ~18 ky BP in the Pleistocene, and
the transition to full-interglacial period begun since then to 10 ky BP, which is
known as the Bölling-Alleröd warm period (Folland et al. 2001, Roberts 1998). The
contraction of tundra distribution was noticeable during this warming period along
with an expansion of temperate forests (Fig. 3.2). The cold mixed forests remained
in the north, with slight shifts in their distributions, mainly with eastern orientated
movement. The area occupied by warm mixed forest decreased towards southeast,
being occupied by the temperate forest. During this period, the biome steppe migrated to the southwest (for individual biome surfaces, see Fig. 3.3), indicating a conDiscussion | 
traction of treeless biomes. Plant associations point towards a migration of warmer
assemblages from eastern to southern Iberian Peninsula and a fast reduction of treeless PFTs (Fig. 3.2). The PFT bs has the most obvious shift: at the maximum age
studied, it had its presence confined to northern Iberian Peninsula and at 13 ky BP
expanded throughout almost all peninsula. This warming phase was also characterized by movements of individual genera (Fig. 3.1). Genus as Betula and Castanea expanded their distribution in the Iberian Peninsula, the latter with a pattern similar
to those achieved with predictive modelling with climatic data for the last glacial
maximum (Garzón et al. 2007). It was not possible to model Fagus at this period due
to limitation of evidences from pollen. Huntley (1989) suggested a restricted distribution of beech during the Holocene, possible in refugia in the Balkanic and the
Italic Peninsulas, expanding and reaching Iberia in recent times (~4 ky BP).
Although it was not possible to achieve a distribution of deciduous Quercus species
at latter times, its expansion is perceptible at 13 ky BP, occupying most of the western
belt of Iberia. The comparison of pollen percentages between different genus is
almost impossible due to different pollen production (Odgaard 1999). However, it is
possible to conclude, based on the time-series analysis of the same genus, that evergreen Quercus had a weak appearance in this period and a slight retraction at 14 ky
BP.
The end of Pleistocene marks the beginning of a warmer, although unstable
climate. During the LGM, species found their refugia mainly in the north and south
of the Iberian Peninsula and the warming gave birth to a continuous shift of occupancy by each genus, assemblages and biomes. This conspicuous relation of patterns
of migration and climate is assessed through the distribution maps and correlations
between areas and temperature proxies.
Younger Dryas – Return to cold
The following event is characterized by a reversal in temperature trend, dropping to very low values and originating cold, dry and windy conditions when compared to the present (Alley & Clark 1999). This period known as Younger Dryas extended from ~12 to 11.5 ky BP (Folland et al. 2001, Isarin 1997), and the main
evidence in fossil pollen is the restoration of Tundra predominance in Iberian
Peninsula while this event occurred (Fig. 3.2). The temperate forest was pushed to
northern Iberia and there was a small southern refuge for the warm mixed forest.
Betula species had a minor increase of their distribution area during this period,
being the only genus to expand southwards (Fig. 3.1). Pistacia had a longitudinal
shift, with increased presence of pollen in the south, in the same area of warm mixed
forest. Both evergreen and decidous Quercus have their area decreased to a minimum
 | Discussion
expression of pollen, the former in the south and the latter in the northwest. There is
no great impact on bs PFT suggesting a good response to colder climates, as opposite to ts and wte PFT that have a reduction of their areas with a similar pattern
of deciduous and evergreen Quercus, respectively. The Castanea species had a large
reduction, occupying during this period a small refugee in northern Iberia.
The Younger Dryas is a paradigm of late-Quaternary trend reversal with
abrupt transitions. As seen in the mapped distributions here, it has a major impact
on plant dispersal despite their ephemeral nature when compared to most persistent
climatic trends.
Holocene - Warmer beginning
A warmer period followed the Younger Dryas, characterized by an exceptional retraction of Tundra and expansion of warmer biomes (Fig. 3.2). During this
period occurred the first appearance of the hottest biome, Xerophyte, in the south of
Peninsula. It ranged in areas previously occupied by Warm mixed forests and it was
surrounded by this biome. The persistence of both these biomes is a clear sign of a
permanently warm region. The Cool mixed forest and its co-dominance with
Temperate deciduous forests have the most mutable distribution during this event:
it shifted from a northwestern distribution to a north and eastern cores at 10 ky BP
and with posterior dominance in the east. Between 10.7 and 10.5 ky BP occurred a
fast cooling event, detected in the Swiss alps in inferred July air temperatures (Heiri
et al. 2003), with a perceived effect in Iberian Peninsula due to expansion of Tundra
near Serra da Estrela (Portugal) and Valencia (Spain) and decreased area of Warm
mixed forests.
At 9 ky BP there was a evolution of Xerophytic and Tundra biomes with an
improbable proximity. This could be due to the nature of interpolations and pollen
analysis: the collection sites are often lakes or peat bogs that collect pollen from a
wide area and may confound regional an local effects (Williams et al. 1998).
Therefore, Tundra composition is possible restricted to high altitude lakes at colder
time and its presence could be extended to other areas by pollen dispersion and interpolations results. Nevertheless, it was notorious the dominance of treeless biomes
in the southeastern area of Iberia. The PFT wte is very sensible to temperature
changes: during this cold event it is noticeable a narrowing of its distribution to the
warmer areas of southern Iberia, while during warmer periods it extends throughout
all Iberia. The PFTs bs and ts have a distribution pattern similar to the temperate
bioclimatic zone, and the warmer sf and wte PFTs are similar to Mediterranean
bioclimatic zone (Fig. 2.2). The main warmer trend of this period caused a retaction
of Betula species from the southeast and an abrupt change in Olea species distribution during the small cold event, when it spreaded along a narrow area in the
Discussion | 
Mediterranean coast (Fig. 3.1). The deciduous Quercus species had a noticeable presence in the northwest, with high percentage of pollen when compared to the
maximum achieved throughout the studied timescale and spreaded southwards
during this period. Evergreen Quercus persisted in the south with low values and
even with undetectable cores during the cold event but has an expansion to almost
all Iberia area at 9 ky BP. With a similar pattern, Castanea had a strong retraction
that resulted in undetectable cores at 10 ky BP. The first evidence of genus Fagus
suggests a core in the north and further expansion, always with low levels of pollen
percentage. The warmer climate of the Holocene should have boosted the dispersion
of these warmer species.
Cold event at 8.2 ky BP - Sudden reversal
As already seen, even fast climatic reversals may have a strong impact in vegetation dispersal. At ~8 ky BP a shift in temperature was triggered, producing a
cooling event even faster than the Younger Dryas (Baldini et al. 2002, Rohling &
Palike 2005). This event affected the distribution of biomes, and Tundra expanded
again along with Cool mixed forests (Fig. 3.2). Warm mixed forest remained with
unnoticeable changes, probably due to the brisk appearance of this event. The cooling
climate had a strong influence in the treeless biomes as seen in the expansion of
Tundra and in Steppe which has discontinued its reduction drift during this event.
As in previous cooler climates, the wte presence was reduced to the southern
Iberia, while other PFTs did not had considerable shifts in their distribution (Fig.
3.2). Therefore, the impact of cooler climates shifts, especially in warmer trend ages,
is stronger in the mostly thermophilic species. This seems to be the case of Olea,
since it proved to be very sensible to climatic fluctuations (Fig 3.1). For instance, in
the presence of fast cooling events, its distribution was systematically reduced to
small areas in the Mediterranean coast. Both mapped Quercus were more resilient to
this sudden reversal in climate, maintaining their distribution with slight changes.
Holocene Maximum Warming - The current warming phase
The Holocene maximum warming phase in Europe extended from 6 to 4.5
ky BP and Temperate deciduous and Warm mixed forests have prevailed during this
time (Fig. 3.2). From 4 ky BP until now, there were temperature shifts that caused
the emergence of cold and arid biomes. At 6 ky BP there was a subtle decrease in
temperature which left imprints in the most sensible distributions: the PFT wte
and the genus Olea distributions had a sudden decrease as already seen in other
cooling events (Fig. 3.1).
Genus Castanea had a drastic decrease from its constant presence in the north | Discussion
west to a subtle core. Due to extreme low values of pollen percentage and narrow
presence, the uncertainty in the presence of this core does not differ from the presence of the genus during this millennium. Although, it reappears at 5 ky BP in the
same location with high values, suggesting a remaining founder core. The strong
presence in the northwest contrast with the broad distribution of Castanea at 2 ky
BP. This age marks a new period in the history of Iberian Peninsula: the arrival of
Romans in the southeast coast at 220 BC and successful journey during the second
Punic Wars which led to the control of all Iberia even before 200 BC. The spread of
Roman civilization throughout Iberia consequently implied the spread of their agricultural habits as the cultivation of Castanea sativa for medicine, wood and food
(Conedera et al. 2004). The knowledge about Castanea sativa fruit effects have been
described in the ancient Greek. Athenaeus (translated by Yonge, 1854), citing previous authors addresses the subject in a humoristic way: “...Mnesitheus the Athenian, in
his book on Comestibles, says, ‘ The digestion of Eubuean nuts or chestnuts (for they are
called by both names) is very difficult for the stomach, and is attended with a great deal of
flatulence, and they are apt to thicken the juice, and to make people fat, unless their constitution is strong enough to neutralise them’”.The long history of human induced distribution of Castanea sativa was confirmed by historical and genetic analysis (Fineschi
et al. 2000). Therefore, the vast dispersal of this genus was mostly due to human
factors. Furthermore, the same impact could be expected in other species; for instance, the more subtle changes in species Olea during 1 ky BP.
The emergence of Tundra biome at 1 ky BP has a dual interpretation: lower
temperatures and/or anthropogenic factors. Temperature alone does not completely
justify the presence of Tundra in a great area since there was a weak lower temperature anomaly in a more general warm tendency and Tundra did not aroused as
widely as in other colder periods. Prentice (1996) advanced a possible human factor
in the emergence of biome Tundra during modern times: the presence of Ericales,
Poaceae and Cyperacea pollen is characteristic of anthropogenic heatlands and, although with different compositions, heatlands produce indistinguishable pollen from
Tundra. Heatlands may thus counfound pollen counts and be responsible for an artificial increase of Tundra.
The presence of Xerophytic biome at 1 ky BP is indicative of a warmer climate
in the Mediterranean area of the Iberian Peninsula, with the nowadays characteristic
arid zones.
4.2 Comparing independent past landscape reconstructions
One of the main advantages of mapping biomes is the possibility to compare between
several other worldwide or regional biomes from different sources. The mapped disDiscussion | 
tribution of pollen percentage is also an excellent tool to compare with other probable distributions of species for the late-Quaternary. This advantage steams from patterns of biomes, plant associations and individual genus being perceived in the time
series of mapped distributions. A similar outcome was found in North America by
Williams (2004) during a similar time span analysis with emergence and disappearance of biomes. Plant associations and individual genus also responded to climatic
shifts with a constant variation of distribution in North America during late
Quaternary oscillations. The vegetation patterns have a dynamic equilibrium with
climate, which is an assumption for modelling pollen distribution. With the finer
scales normally used in plant ecology it is difficult to assume this equilibrium due to
non equilibrium processes as succession in plants. However, with larger spatial and
temporal scales the main patterns of distribution reflect the climate driven shifts
(Prentice et al. 1991).
In a biome reconstruction for 6 ky BP in Europe (Prentice et al. 1996) it was
suggested colder winters then present together with wetter conditions at growing
season, accompanied by the replacement of today’s Xerophytic vegetation by temperate forests in the Mediterranean region. These past conditions are supported by the
present study in the Iberian Peninsula. At 6 ky BP, Iberia was characterized by the
dominance of Temperate forests along with Warm mixed forests at a lesser extent,
and with a small appearance of cool forests in the north. This combination of biomes
was stable for this warming phase.
At the LGM, it was predicted a dominance of Steppe in Mediterranean region,
including Iberia in a pollen analysis of past biomes for Europe and North Africa
(Elenga et al. 2000). This is also supported in this study. As discussed before, the
dominance of Steppe is ambiguous and the biomes with the second highest affinity
Fig. 4.1 – Geographic persistence of taxa
The reconstruction of taxa
distributions allowed the illustration of areas where they
have always occurred. Those
areas of persistence, shown
in the map with different
colours and line styles, are
divided into two main areas:
one at the Temperate region
and other at the Mediterranean region.
 | Discussion
±
Alnus
Betula
Castanea
Fagus
Olea
Pistacea
Quercus (deciduous)
Quercus (evergreen)
0
100
200
Km
400
scores usually provide better information about vegetation changes in Iberia. Tundra
and cool biomes were widespread in central Europe. Nevertheless, in lower latitudes,
there was an increasing supremacy of Temperate forests. Furthermore, in Turkey, at
the same latitude of southern Iberia, there was a clear preponderance of this type of
forests during the LGM at 18 ky BP. Therefore, at 15 ky BP it were expected in the
Iberian Peninsula the presence of Tundra as in central Europe, Italy and the
Temperate forests of European lower latitudes.
The comparison of different data sources is worthy to detect flaws in past reconstructions. The paleopalynology data is a good benchmark to evaluate simulations (Huntley 2001). The relationship between past biomes and vegetation mapping
with independent data of temperature proxies is compared along the present study
with success. This comparison allowed the detection of the main patterns of climate
shifts in plant migrations. The next step is to compare the present reconstruction
with others built with different climatic modelling approaches. Jost (2005) found
some discrepancies for Western Europe between climate simulations and temperatures obtained by pollen models. Those differences, although reduced, were found
even with higher resolution simulations. Some reconstructions of past landscapes
use climate simulations to predict distribution of species in the past (Williams et al.
1998). Although the pollen distribution assumes a constant equilibrium with
climate, despite other small scale processes, the predictive approach uses present distribution to simulate past ranges. This implies that the actual spread of species are
human independent. However, some economic valuable species have distributions
extended to their physiologic limits while others have shrunken their potential distribution. The Castanea sativa have an unambiguous human influence in its distribution since 2 ky BP. This factor imposes a great effort to model correctly the past distribution. In a predictive modelling approach to the past landscape in Iberian
Peninsula this problem was partially avoided by correlating with the current Iberian
natural forests (Garzón et al. 2007). The results found are compatible with the
current work at near the LGM and mid-Holocene.
Garzón et al. (2007) found possible refugia in the northwest and the foothills
of the Pyrenees for trees species. These locations were, in the present study, inditified
also as areas of constant presence throughout time for several tree species (Fig. 4.1),
where the mountain topography is most likely to create a favourable environmental
situation for the maintenance and differentiation of genetic structure (Magri et al.
2006). But the present study identified also persistence areas in southern Iberia for
Mediterranean species (Olea and Pistacia) not predicted by Garzón et al. (2007). The
higher accuracy of the present study is probable a consequence of the analysis not
being limited to the spatial and temporal resolution of climate simulations.
Biomization procedure and interpolated pollen percentages surfaces have a temporal
sequence that allows discerning migrations.
Discussion | 
4.3 Congruence with phylogenetic reconstructions
The comparison of pollen based reconstructions with other type of reconstructions
yield details that can not be assessed individually. These include the genetic analysis
of species or group of organisms that aim to assess their evolutionary history in a
spatially explicit context. This multidisciplinary approach yields a detailed investigation of the evolutionary process behind the actual distribution of species, and provides a supported delimitation of refuge areas (Hugall et al. 2002, King & Ferris
1998, Taberlet et al. 1998).
The main pattern at an European scale and throughout the late Quaternary is
the individual response of all species with expansions/contractions in their distribution due to climatic oscillations and the persistence in southern peninsulas (Iberian,
Italy and Balkan), acting as refugia during the most aggressive conditions of the
LGM (Taberlet et al. 1998). The major conclusion of the present study is that the
Iberian Peninsula did not serve as a stable refuge and the same patterns of migration
and contraction are found inside Iberia throughout the late Pleistocene and
Holocene. This observed patterns of northern persistence of colder-tolerant species
and the most thermophilic at the south (Fig. 4.1) suggests a refuge for Temperate
species and another to Mediterranean ones inside Iberia, in the north and south, respectively. The pattern of “refugia within refugia” (Gómez & Lunt 2006) is coincidental with the genetic substructuring patterns observed today for these groups of
species (Belaj et al. 2007, Besnard et al. 2002, 2007, Dumolin-Lapègue et al. 1997,
Fineschi et al. 2000, King & Ferris 1998, Kropf et al. 2006, Lumaret et al. 2004,
Magri et al. 2006, Maliouchenko et al. 2007, Olalde et al. 2002, Palme et al. 2003,
Petit et al. 2002, 2005, Taberlet et al. 1998). Furthermore, Kropf et al. (2006) described a vicariant area in southern Spain (Sierra Nevada) that supports a high
genetic diversity in some tree species and found evidence of long-term isolation,
which can support this southern refugia. The persistence in the Iberian Peninsula is
common to all studied plant taxa is, although not all genera colonized central and
northern Europe afterwards, during migration phases (Magri et al. 2006,
Maliouchenko et al. 2007, Palme et al. 2003).
Alnus species are present in the Iberian Peninsula with great evidence in pollen
data since 14 ky BP. This supports the suggestion of King et al. (1998) to add Iberia
as a possible refugia for Alnus glutinosa, where, along with Turkey, a high level of
chloroplast DNA diversity was found. With approximately the same latitude and
the similar biomes (Elenga et al. 2000), the Iberian Peninsula and Turkey were very
probable refuges for Alnus. The Castanea sativa had the same pattern of origin, surviving in Turkey and Iberian Peninsula at similar latitudes. Although with a huge
 | Discussion
human influence on its distribution, the historical origin of Castanea sativa is attributed to Turkey along with other possible refugia in northwestern Iberia, as suggested
by phylogenetic analysis (Fineschi et al. 2000). This study provides additional supporting evidence of this northwestern Iberian refuge, very sensible to climate
oscillations.
The genus Betula is clearly a case of a cold-tolerant species surviving at lower
latitudes during the LGM, although its main refugia core were at higher latitudes
(Maliouchenko et al. 2007, Palme et al. 2003). The proposed refugia for those species
are northern Alps, southern Sweden and areas close to the Ural Mountains (Palme
et al. 2003). There is a strong pollen evidence for a northwestern Iberia persistence
of this genus with posterior expansion during the late Quaternary, even though they
are unlikely to be the source for colonization of central and northern Europe after
the LGM (Maliouchenko et al.).
Molecular studies of Olea species point to a dual origin in the west and east of
the Mediterranean Basin with some degree of separation in the western populations
(Besnard et al. 2002, 2007). The complex biogeography history of olive populations
in the Mediterranean, due to environmental factors and human dispersion, contributed to populations differentiation (Besnard et al. 2002). In the present work, it is
possible to notice the constant presence of Olea species in southern Iberia and its
thermophilic characteristic with extensive responses to climate change: expansion
during warmer phases and contraction in colder periods. Besnars et al. (2007) suggested that during the LGM, which was a favourable period for olive in North Africa,
there may have been gene flow between these populations and the ones from the
Mediterranean basin.
In an extensive study of Fagus distribution in North America and Europe,
Huntley et al. (1989) suggested a restricted distribution to Italy and Balkans at the
LGM. The following expansion from those refuge areas reached Iberian Peninsula
through the Pyrenees Mountains at 4 ky BP. At this age there is an obvious increase
in pollen percentage of Fagus genus near the Pyrenees, indicating a more stable presence in that area, probably due to expansion from described refuge. Nevertheless,
this genus is constantly present in North Iberia throughout the time span of the
present study. A haplotype found in Cantrabrean Mountains support the evidence
of persistence of Fagus, and there is isozyme evidence of three populations in this
area during the Holocene, although they did not contribute for the colonizing of the
rest of Europe (Magri et al. 2006).
The fossil pollen analysis does not often reach higher taxonomic levels for
Quercus than the classification in deciduous or evergreen. As seen, these different assemblages have different persistence in Iberian Peninsula since the LGM: the evergreen residing at the south, whereas the deciduous dwell at the northwest. The
Iberian refugia is obvious in molecular analysis of white oaks in Europe, due to the
Discussion | 
presence of high chloroplast DNA diversity (Dumolin-Lapègue et al. 1997, Olalde
et al. 2002, Petit et al. 2002). Inside the Îberian Peninsula, however, molecular data
reveals a complex pattern of migrations during the Holocene with different smaller
refugia (Olalde et al. 2002, Petit et al. 2002). This is compatible with the present
results, where migrations inside the peninsula have visible patterns in the fossil
pollen presence. Moreover, the suggested routes for lineages migration inside the peninsula and the migration inwards (Olalde et al. 2002, Petit et al. 2002) are congruent with the pollen evidence: 1) deciduous Quercus have first appearance in central/
south Portugal at 13 ky BP, followed by a migration to north, prevailing in the northwest with an extension towards the Pyrenees; 2) the evergreen Quercus have a southern dominance, expanding to central Iberia and a slight change of their main core
eastwards, along the Mediterranean coast.
Olalde (2002) suggested that the latitudinal effect in the Iberian Peninsula is
overridden by its topography, creating possible refugia both in northern and southern areas.
4.4 Parallelism between fauna and flora refugia
The glacial refugia areas for fauna and flora are obviously related due to a common
vicariant history (Gómez & Lunt 2006). Although there is a lack of geographic resolution when defining refugia areas in most studies (Gómez & Lunt 2006), the
Mediterreanean and Temperate refuge described in this study exhibit a parallelism
with the fauna refuge described by several genetic studies (Fig. 4.2). In Northern
Iberia there is evidence for several refugia for fauna species along with the Alnus,
Betula, Castanea, Fagus and deciduous Quercus refugia proposed in this study. In
Picos de Europa Mountains there is some genetic evidence for herpetofauna refugia,
including Zootoca vivipara (Guillaume et al. 2000, Surget-Groba et al. 2001) and
Salamandra salamandra (Garcia-Paris et al. 2003, Steinfartz et al. 2000); and
mammals, Lepus castroviejoi (Pérez-Suárez et al. 1994).
The Central Mountain System is proposed as a refugia for Lacerta schreiberi
(Paulo et al. 2001, 2002), Chioglossa lusitanica (Alexandrino et al. 2000, 2002), Alytes
obstetricans boscai (Arntzen & Garcia-Paris 1995, Fonseca et al. 2003) and Microtus
agrestis ( Jaarola & Searle 2002). In the present study, this area does not have a continuous strong presence of pollen data when confronted to both persistence cores in
the north and south (Fig. 4.1). Nevertheless, Betula and Alnus at a lesser extent, are
present it the Central System throughout the late-Quaternary, indicating a possible
continuous presence there. Although their persistence areas have an evident core in
the northern Iberian Peninsula, those are the genus with persistence areas extending
further south, reaching the Central System (Fig. 3.5).
 | Discussion
In southern Iberian Peninsula putative refugia for Mediterranean vegetation
was demonstrated in the present work by the continuous presence of evergreen
Quercus, Pistacia and Olea taxa. The Baetic Mountains constitute also putative
refugia for several fauna species: Alytes dickilleni (Arntzen & Garcia-Paris 1995),
Discoglossus jeanneae (Garcia-Paris & Jockusch 1999), Salamandra salamandra
(Garcia-Paris et al. 2003, Steinfartz et al. 2000) and Oryctolagus cuniculus (Branco et
al. 2000, 2002). The latter mammal species has also a possible glacial refugia in the
Ebro basin (Branco et al. 2000, 2002) along with Brachionus plicatilis (Gómez et al.
2000), which is an area with strong presence of Olea species during the
late-Quaternary.
In the Pyreenes, the occurrence of some tree species, as suggested by the distribution of Olea and Fagus since the LGM, is congruent to other recognized refugia
as indicated for Zootoca vivipara (Guillaume et al. 2000, Surget-Groba et al. 2001).
There is a strong correlation of glacial refugia for fauna described by several
molecular studies and the mapped distribution of vegetation and persistence areas
described in the present study. As Goméz & Lunt (2006) pointed, further studies
are important to delimit and increase the possible refugia areas. These studies may
opt for a molecular approach of several key species in the Iberian Peninsula, or may
be reconstructions based in other evidence as this study demonstrated.
-05º
00º
05º
Pyrenees
Picos de Europa
Zootoca vivipara
Salamandra salamandra
Lepus castroviejoi
Zootoca vivipara
45º
Fagus
Olea
40º
Alnus
n
Al
Ca
Ebro basin
stan
ea
/ Quercus (dec)
Ol
ea
/ Pi
stac
ia / Q
Ole
a
Alnus / Betula
us
/
uercus
Brachionus plicatilis
Oryctolagus cuniculus
(evr)
40º
35º
Central System
Lacerta schreiberi
Chioglossa lusitanica
Alytes obstetricans boscai
Microtus agrestis
-10º
Baetic System
Alytes dickilleni
Discoglossus jeanneae
Oryctolagus cuniculus
Salamandra salamandra
-05º
00º
Fig. 3.2 – Glacial refugia
for fauna and flora in the
Iberian Peninsula
The climate oscillations force
species migrations inside
the Iberian Peninsula. The
arrows indicate possible
patterns of migrations for
the vegetation, accordingly to
pollen data presented in this
study. The inset boxes indicate the putative refugia for
several faunal species based
on Goméz & Lunt (2006).
Discussion | 
 | Discussion
 Conclusions
Iberian Peninsula is usually assumed as one of the southern refugia in Europe for
the adverse conditions of the last glacial epoch. Its low latitude location, when compared to remaining Europe, allowed a more tolerant climate although was affected by
the same instability processes, resulting in a dynamic environment throughout the
late Quaternary. This makes the Iberian Peninsula a privileged area for analysing the
oscilation processes during the warming since LGM and their influences in organisms. As Huntley (2001) suggested, the targets of these analyses should be regions
and times where there is supposed to exist sensible responses to climate shifts. The
biomization is a robust method to envisage past vegetation processes through the
fossil pollen data due to high correlation to climate. Moreover, biomization allows
the mapping of distributions of typical taxa and plant assemblages. It was also
stressed the ability of this type of reconstruction to serve as benchmarks for other
methods. Pollen reconstructions and biomization were successfully compared to
direct climate reconstructions as well as with molecular studies identifying probable
refugia. The combination of these distinct methods provides a solid understanding
of past processes, mainly when the reconstructions provide a time sequence that
allows visualizing the migrations patterns of taxa.
One of the limitations of the present study is the availability of pollen information in digital format. The EPD provides a vast database for all Europe with raw
pollen counts which are the best data to reconstruct biomes. Nevertheless, there are
numerous local scale studies that present important pollen data. The process of digitizing these data from old and newer pollen cores into general purposes databases
should be encouraged as they constitute important resources providing extremely
useful information to several multidisciplinary studies.
The present study also consolidates the usefulness of GIS for the reconstructions of past environments, as they intrinsically have a spatial content and the display
and analysis in a spatially explicit context provides more detailed information. An
advantage of GIS software is the possibility of developing scripts to automate processes. The effort of producing a large number of maps for the present work was
reduced by the development of several scripts that automated cyclic tasks and analysis of results.
Conclusions | 
 | Conclusions
 References
Alexandrino, J.; J. W. Arntzen & N. Ferrand (2002). Nested clade analysis and
the genetic evidence for population expansion in the phylogeography of the
golden-striped salamander, Chioglossa lusitanica(Amphibia: Urodela). Heredity, 88(1): 66-74
Alexandrino, J.; E. Froufe; J. W. Arntzen & N. Ferrand (2000). Genetic subdivision,
glacial refugia and postglacial recolonization in the golden-striped salamander,
Chioglossa lusitanica (Amphibia: Urodela). Molecular Ecology, 9(6): 771-781
Alfano, M. J.; E. J. Barron; D. Pollard; B. Huntley & J. R. M. Allen (2003). Comparison of climate model results with European vegetation and permafrost
during oxygen isotope stage three. Quaternary Research, 59(1): 97-107
Alley, R. B. & P. U. Clark (1999). The deglaciation of the northern hemisphere: A
global perspective. Annual Review of Earth and Planetary Sciences, 27: 149-182
Alley, R. B.; P. A. Mayewski; T. Sowers; M. Stuiver; K. C. Taylor & P. U. Clark
(1997). Holocene climatic instability: A prominent, widespread event 8200 yr
ago. Geology, 25(6): 483-486
Anderson, N. J.; M.-J. Gaillard; H. Bugmann & J. A. Dearing (2006). Linking
palaeoenvironmental data and models to understand the past and to predict
the future. Trends in Ecology & Evolution, 21(12): 696-704
Arntzen, J. W. & M. Garcia-Paris (1995). Morphological and allozyme studies of
midwife toads (genus Alytes), including the description of two new taxa from
Spain. Contributions to Zoology, 65: 5-34
Augustin, L.; C. Barbante; P. R. F. Barnes; J. M. Barnola; M. Bigler; E. Castellano;
O. Cattani; J. Chappellaz; D. Dahl-Jensen; B. Delmonte; G. Dreyfus; G.
Durand; S. Falourd; H. Fischer; J. Flückiger; M. E. Hansson; P. Huybrechts;
G. Jugie; S. J. Johnsen; J. Jouzel; P. Kaufmann; J. Kipfstuhl; F. Lambert; V.
Y. Lipenkov; G. C. Littot; A. Longinelli; R. Lorrain; V. Maggi; V. MassonDelmotte; H. Miller; R. Mulvaney; J. Oerlemans; H. Oerter; G. Orombelli; F.
Parrenin; D. A. Peel; J.-R. Petit; D. Raynaud; C. Ritz; U. Ruth; J. Schwander;
U. Siegenthaler; R. Souchez; B. Stauffer; J. P. Steffensen; B. Stenni; T. F.
Stocker; I. E. Tabacco; R. Udisti; R. S. W. v. d. Wal; M. v. d. Broeke; J. Weiss;
References | 
F. Wilhelms; J.-G. Winther; E. W. Wolff & M. Zucchelli (2004). Eight glacial
cycles from an Antarctic ice core. Nature, 429(6992): 623-628
Baldini, J. U. L.; F. McDermott & I. J. Fairchild (2002). Structure of the 8200Year Cold Event Revealed by a Speleothem Trace Element Record. Science,
296(5576): 2203-2206
Belaj, A.; C. MunOz-Diez; L. Baldoni; A. Porceddu; D. Barranco & Z. Satovic
(2007). Genetic Diversity and Population Structure of Wild Olives from the
North-western Mediterranean Assessed by SSR Markers. Annals of Botany,
Published online
Bernabo, J. C. & T. Webb (1977). Changing patterns in the Holocene pollen record
of northeastern North America: A mapped summary. Quaternary Research,
8(1): 64-96
Besnard; G. Besnard; Khadari; B. Khadari; Baradat; P. Baradat; Bervillé & A. Bervillé (2002). Olea europaea (Oleaceae) phylogeography based on chloroplast
DNA polymorphism. Theoretical and Applied Genetics, 104(8): 1353-1361
Besnard, G.; R. Rubio de Casas & P. Vargas (2007). Plastid and nuclear DNA
polymorphism reveals historical processes of isolation and reticulation in the
olive tree complex (Olea europaea). Journal of Biogeography, 34(4): 736-752
Bradshaw, R. H. W. & T. W. III (1985). Relationships between Contemporary
Pollen and Vegetation Data from Wisconsin and Michigan, USA. Ecology,
66(3): 721-737
Branco, M.; N. Ferrand & M. Monnerot (2000). Phylogeography of the European
rabbit (Oryctolagus cuniculus) in the Iberian Peninsula inferred from RFLP
analysis of the cytochrome b gene. Heredity, 85: 307-17
Branco, M.; M. Monnerot; N. Ferrand & A. R. Templeton (2002). Postglacial
dispersal of the European rabbit (Oryctolagus cuniculus) on the Iberian Peninsula reconstructed from nested glade and mismatch analyses of mitochondrial
DNA genetic variation. Evolution, 56(4): 792-803
Brown, J. H.; M. V. Lomolino & B. R. Riddle (2005). Biogeography. Sinauer Associates, 845
Burjachs, F.; R. Pérez-Obiol; J. M. Roure & R. Juliá (1994). Dinamica de la vegetation durante el Holoceno en la isla de Mallorca. X Simposio de Palinologia
(APLE). Valencia, Spain.
Carrion, J. S. (2002). Patterns and processes of Late Quaternary environmental
change in a montane region of southwestern Europe. Quaternary Science
Reviews, 21(18-19): 2047-2066
Carrion, J. S.; A. Andrade; K. D. Bennett; C. Navarro & M. Munuera (2001).
Crossing forest thresholds: inertia and collapse in a Holocene sequence from
south-central Spain. Holocene, 11(6): 635-653
Carrion, J. S. & M. Dupre (1996). Late Quaternary Vegetational History at Navar | References
res, Eastern Spain. A Two Core Approach New Phytologist, 134(1): 177-191
Cincotta, R. P.; J. Wisnewski & R. Engelman (2000). Human population in the
biodiversity hotspots. Nature, 404(6781): 990-992
Clark, J. S. (1998). Why Trees Migrate So Fast: Confronting Theory with Dispersal
Biology and the Paleorecord. The American naturalist 152(2): 21
COHMAP (1988). Climatic Changes of the Last 18,000 Years: Observations and
Model Simulations. Science, 241: 1043-1052
Conedera, M.; P. Krebs; W. Tinner; M. Pradella & D. Torriani (2004). The cultivation of Castanea sativa (Mill.) in Europe, from its origin to its diffusion on a
continental scale. Vegetation History and Archaeobotany, 13(3): 161-179
Cox, C. B. & P. D. Moore (2005). Biogeography, An Ecological and Evolutionary
Approach. Blachwell Publishing, Oxford, 428
Crowley, T. J. & G. R. North (1988). Abrupt Climate Change and Extinction
Events in Earth History. Science, 240(4855): 996-1002
Davis, M. B. (1994). Ecology and paleoecology begin to merge. Trends in Ecology &
Evolution, 9(10): 357-358
Davis, M. B. & R. G. Shaw (2001). Range Shifts and Adaptive Responses to Quaternary Climate Change. Science, 292(5517): 673-679
Desprat, S.; M. F. Sanchez Goni & M.-F. Loutre (2003). Revealing climatic variability of the last three millennia in northwestern Iberia using pollen influx data.
Earth and Planetary Science Letters, 213(1-2): 63-78
Dumolin-Lapègue, S.; B. Demesure; S. Fineschi; V. L. Corre & R. J. Petit (1997).
Phylogeographic Structure of White Oaks Throughout the European Continent. Genetics, 146(4): 1475-1487
Edwards, G. & M.-J. Fortin (2001). A cognitive view of spatial uncertainty. In:
Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS
Applications [C. T. Hunsaker, M. F. Goodchild, M. A. Friedl and T. J. Case
(eds.)]. Springer-Verlag, New York, 424 p.
Elenga, H.; O. Peyron; R. Bonnefille; D. Jolly; R. Cheddadi; J. Guiot; V. Andrieu; S.
Bottema; G. Buchet; J. L. De Beaulieu; A. C. Hamilton; J. Maley; R. Marchant; R. Perez-Obiol; M. Reille; G. Riollet; L. Scott; H. Straka; D. Taylor;
E. Van Campo; A. Vincens; F. Laarif & H. Jonson (2000). Pollen-based
biome reconstruction for southern Europe and Africa 18,000 yr bp. Journal of
Biogeography, 27(3): 621-634
ESRI (2006). ArcGis 9.2, Environmental Systems Research Institute, Inc., Redlands, California.
Fineschi, S.; D. Taurchini; F. Villani & G. G. Vendramin (2000). Chloroplast DNA
polymorphism reveals little geographical structure in Castanea sativa Mill.
(Fagaceae) throughout southern European countries. Molecular Ecology,
9(10): 1495-1503
References | 
Folland, C. K.; T. R. Karl; J. R. Christy; R. A. Clarke; G. V. Gruza; J. Jouzel; M. E.
Mann; J. Oerlemans; M. J. Salinger & S. W. Wang (2001). Observed Climate
Variability and Change. In: Climate Change 2001: The Scientific Basis.
Contribution of Working Group I to the Third Assessment Report of the
Intergovernmental Panel on Climate Change [ J. T. Houghton, Y. Ding, D. J.
Griggs, M. Noguer, P. J. v. d. Linden, X. Dai, K. Maskell and C. A. Johnson
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 881
Fonseca, A.; J. W. Arntzen; E. Crespo & N. Ferrand (2003). Regional differentiation
in the common midwife toad (Alytes obstetricans) in Portugal: a picture from
mitochondrial DNA. Zeitschrift für Feldherpetologie, 10: 83-89
Garcia-Paris, M.; M. Alcobendas; D. Buckley & D. B. Wake (2003). Dispersal of
viviparity across contact zones in iberian populations of fire salamanders
(Salamandra) inferred from discordance of genetic and morphological traits.
Evolution, 57(1): 129-143
Garcia-Paris, M. & E. L. Jockusch (1999). A mitochondrial DNA perspective on
the evolution of Iberian Discoglossus (Amphibia: Anura). Journal of Zoology,
248: 209-218
Garcia, M. J. G.; M. D. Valino; A. V. Rodriguez & M. B. R. Zapata (2002). Lateglacial and Holocene palaeoclimatic record from Sierra de Cebollera (northern Iberian Range, Spain). Quaternary International, 93-4: 13-18
Garzón, M. B.; R. S. d. Dios & H. S. Ollero (2007). Predictive modelling of
tree species distributions on the Iberian Peninsula during the Last Glacial
Maximum and Mid-Holocene. Ecography, 30(1): 120-134
Gómez, A.; G. R. Carvalho & D. H. Lunt (2000). Phylogeography and regional
endemism of a passively dispersing zooplankter: mitochondrial DNA variation in rotifer resting egg banks. Proceedings of the Royal Society B: Biological
Sciences, 267(1458): 2189-2197
Gómez, A. & D. H. Lunt (2006). Refugia within refugia: patterns of phylogeographic concordance in the Iberian Peninsula. In: Phylogeography of Southern
European Refugia [S. Weiss and N. Ferrand (eds.)]. Springer, 155-188
Goni, M. F. S. & G. E. Hannon (1999). High-altitude vegetational pattern on the
Iberian Mountain Chain (north-central Spain) during the Holocene. Holocene, 9(1): 39-57
Gonzalez-Samperiz, P.; B. L. Valero-Garces; A. Moreno; G. Jalut; J. M. Garcia-Ruiz;
C. Marti-Bono; A. Delgado-Huertas; A. Navas; T. Otto & J. J. Dedoubat
(2006). Climate variability in the Spanish Pyrenees during the last 30,000 yr
revealed by the El Portalet sequence. Quaternary Research, 66(1): 38-52
Grafenstein, U. v.; H. Erlenkeuser; A. Brauer; J. Jouzel & S. J. Johnsen (1999). A
Mid-European Decadal Isotope-Climate Record from 15,500 to 5000 Years
 | References
B.P. Science, 284(5420): 1654-1657
Grime, J. P.; K. Thompson; R. Hunt; J. G. Hodgson; J. H. C. Cornelissen; I. H.
Rorison; G. A. F. Hendry; T. W. Ashenden; A. P. Askew; S. R. Band; R. E.
Booth; C. C. Bossard; B. D. Campbell; J. E. L. Cooper; A. W. Davison; P. L.
Gupta; W. Hall; D. W. Hand; M. A. Hannah; S. H. Hillier; D. J. Hodkinson;
A. Jalili; Z. Liu; J. M. L. Mackey; N. Matthews; M. A. Mowforth; A. M. Neal;
R. J. Reader; K. Reiling; W. Ross-Fraser; R. E. Spencer; F. Sutton; D. E.
Tasker; P. C. Thorpe & J. Whitehouse (1997). Integrated Screening Validates
Primary Axes of Specialisation in Plants. Oikos, 79(2): 259-281
Grootes, P. M. & M. Stuiver (1997). Oxygen 18/16 variability in Greenland snow
and ice with 103 to 105-year time resolution. Journal of Geophysical Research,
102: 26455-26470
Grootes, P. M.; M. Stuiver; J. W. C. White; S. J. Johnsen & J. Jouzel (1993). Comparison of oxygen isotope records from the GISP2 and GRIP Greenland ice
cores. Nature, 366: 552-554
Guillaume, C.-P.; B. Heulin; M. J. Arrayago; A. Bea & F. Brana (2000). Refuge areas
and suture zones in the Pyrenean and Cantabrian regions: geographic variation of the female MPI sex-linked alleles among oviparous populations of the
lizard Lacerta (Zootoca) vivipara. Ecography, 23(1): 3-10
Hannon, G. E. (1985). Late Quaternary vegetation of Sanabria Marsh, North West
Spain. M.Sc. Thesis. Trinity College, Dublin, Ireland.
Heiri, O.; A. F. Lotter; S. Hausmann & F. Kienast (2003). A chironomid-based
Holocene summer air temperature reconstruction from the Swiss Alps. The
Holocene, 13(4): 477–484
Hewitt, G. (2000). The genetic legacy of the Quaternary ice ages. Nature,
405(6789): 907-913
Hewitt, G. (2004a). The structure of biodiversity - insights from molecular phylogeography. Frontiers in Zoology, 1(1): 4
Hewitt, G. M. (2004b). Genetic consequences of climatic oscillations in the Quaternary. Philosophical Transactions of the Royal Society of London Series B-Biological
Sciences, 359(1442): 183-195
Hugall, A.; C. Moritz; A. Moussalli & J. Stanisic (2002). Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest
land snail Gnarosophia bellendenkerensis (Brazier 1875). PNAS, 99(9): 61126117
Huntley, B. (2001). Reconstructing past environments from the Quaternary palaeovegetation record. Biology and Environment: proceedings of the Royal Irish
Academy, 101(1-2): 3-18
Huntley, B.; P. J. Bartlein & I. C. Prentice (1989). Climatic Control of the Distribution and Abundance of Beech (Fagus L) in Europe and North-America.
References | 
Journal of Biogeography, 16(6): 551-560
Huntley, B. & T. Webb (1989). Migration - Species Response to Climatic Variations
Caused by Changes in the Earths Orbit. Journal of Biogeography, 16(1): 5-19
Isaaks, E. H. & R. M. Srivastava (1989). Applied Geostatistics. Oxford University
Press, Oxford, 561 pp.
Isarin, R. F. B. (1997). Permafrost Distribution and Temperatures in Europe During
the Younger Dryas. Permafrost and Periglacial Processes, 8(3): 313-333
Jaarola, M. & J. B. Searle (2002). Phylogeography of field voles (Microtus agrestis)
in Eurasia inferred from mitochondrial DNA sequences. Molecular Ecology,
11(12): 2613-2621
Jost, A.; D. Lunt; M. Kageyama; A. Abe-Ouchi; O. Peyron; P. J. Valdes & G. Ramstein (2005). High-resolution simulations of the last glacial maximum climate
over Europe: a solution to discrepancies with continental palaeoclimatic
reconstructions? Climate Dynamics, 24(6): 577-590
Judy R. M. Allen, B. H. W. A. W. (1996). The vegetation and climate of northwest
Iberia over the last 14,000 years. Journal of Quaternary Science, 11(2): 125147
Julià, R.; F. Burjachs; M. J. Dasí; F. Mezquita; M. R. Miracle; J. R. Roca; G. Seret
& E. Vicente (1998). Meromixis origin and recent trophic evolution in the
Spanish mountain lake La Cruz. Aquatic Sciences - Research Across Boundaries,
60(4): 279-299
King, R. A. & C. Ferris (1998). Chloroplast DNA phylogeography of Alnus glutinosa (L.) Gaertn. Molecular Ecology, 7(9): 1151-1161
Kropf, M.; H. P. Comes & J. W. Kadereit (2006). Long-distance dispersal vs vicariance: the origin and genetic diversity of alpine plants in the Spanish Sierra
Nevada. New Phytologist, 172(1): 169-184
Leira, M. & L. Santos (2002). An early Holocene short climatic event in the
northwest Iberian Peninsula inferred from pollen and diatoms. Quaternary
International, 93-94: 3-12
Lumaret, R.; N. Ouazzani; H. Michaud; G. Vivier; M. F. Deguilloux & F. d. Giusto
(2004). Allozyme variation of oleaster populations (wild olive tree) (Olea
europaea L.) in the Mediterranean Basin. Heredity, 92(4):
Magri, D.; G. G. Vendramin; B. Comps; I. Dupanloup; T. Geburek; D. Gomory;
scaron; an; M. Latalowa; T. Litt; L. Paule; J. M. Roure; I. Tantau; W. O. van
der Knaap; R. J. Petit & J.-L. de Beaulieu (2006). A new scenario for the
Quaternary history of European beech populations: palaeobotanical evidence
and genetic consequences. New Phytologist, 171(1): 199-221
Maliouchenko, O.; A. E. Palme; A. Buonamici; G. G. Vendramin & M. Lascoux
(2007). Comparative phylogeography and population structure of European
Betula species, with particular focus on B. pendula and B. pubescens. Journal of
 | References
Biogeography (OnlineEarly Articles),
Mariscal, B. (1983). Estudio polinico de la turbera del Cueto de la Avellanosa
polaciones (Cantabria). Cuadernos do laboratorio xeloxico de laxe. 5: 205-226
Mariscal, B. (1986). Analisis polinico de la turbera del Pico del Sertal de la sierra
de Pena Labra. Reconstrucion de la paleoflora y de la climatologia durante el
Holoceno en la zona oriental de la cordillera cantabria. In: Quaternary climate
in Western Mediterranean (eds.)]. Univ. Auto. de Madrid, 205-220
Mariscal, B. (1989). Comparacion palinologica entre una turbera de la cordillera
central y unas turberas de la cordillera cantabrica. II European Paleobotanical
conference. Univ. Complutense Madrid.
Mariscal, B. (1993). Variacion de la vegetacion Holocena (4300-280 BP) de Cantabria a traves del analisis polinico de la turbera del Alsa. Estudios Geologicos,
Museo Nacional Ciencias Naturales
McKeever, M. H. (1984). Comparative palynological studies of two lake sites in
western Ireland and northwestern Spain. Thesis. Trinity College, Dublin,
Ireland.
Meese, D. A.; R. B. Alley; R. J. Fiacco; M. S. Germani; A. J. Gow; P. M. Grootes;
M. Illing; P. A. Mayewski; M. C. Morrison; M. Ram; K. C. Taylor; Q. Yang &
G. A. Zielinski. (1994). Preliminary depth-agescale of the GISP2 ice core. .
Series, Preliminary depth-agescale of the GISP2 ice core. US.
Mugica, F. F.; G. Anton; J. M. Ruiz; C. M. Juaristi & H. S. Ollero (2001). The
Holocene history of Pinus forests in the Spanish Northern Meseta. Holocene,
11(3): 343-358
Mugica, F. F.; M. G. Anton & H. S. Ollero (1998). Vegetation dynamics and human
impact in the Sierra de Guadarrama, Central System, Spain. Holocene, 8(1):
69-82
Mugica, F. F.; M. Garcia-Anton; J. Maldonado-Ruiz; C. Morla-Juaristi & H. SainzOllero (2005). Ancient pine forest on inland dunes in the Spanish northern
meseta. Quaternary Research, 63(1): 1-14
Muñoz Sobrino, C.; P. Ramil-Rego; L. Gómez-Orellana & R. Díaz Varela (2005).
Palynological data on major Holocene climatic events in NW Iberia. Boreas,
34(3): 381-400
Myers, N.; R. A. Mittermeier; C. G. Mittermeier; G. A. B. da Fonseca & J. Kent
(2000). Biodiversity hotspots for conservation priorities. Nature, 403(6772):
853-858
Odgaard, B. V. (1999). Fossil pollen as a record of past biodiversity. Journal of Biogeography, 26(1): 7-17
Olalde, M.; A. Herran; S. Espinel & P. G. Goicoechea (2002). White oaks phylogeography in the Iberian Peninsula. Forest Ecology and Management, 156(1-3):
89-102
References | 
Paez, M. M.; F. Schabitz & S. Stutz (2001). Modern pollen-vegetation and isopoll
maps in southern Argentina. Journal of Biogeography, 28(8): 997-1021
Palme, A. E.; Q. Su; A. Rautenberg; F. Manni & M. Lascoux (2003). Postglacial
recolonization and cpDNA variation of silver birch, Betula pendula. Molecular
Ecology, 12(1): 201-212
Pantaleon Cano, J. (1997). Estudi palinologic de sediments litorals de la provincia
d’Almeria. Transformacion del paisatge vegetal dins un territori semiarid.
PhD Thesis. Universitat Autònoma de Barcelona, Barcelona, Spain
Paulo, O. S.; C. Dias; M. W. Bruford; W. C. Jordan & R. A. Nichols (2001). The
persistence of Pliocene populations through the Pleistocene climatic cycles:
evidence from the phylogeography of an Iberian lizard. Proceedings of the Royal
Society B: Biological Sciences, 268(1476): 1625-1630
Paulo, O. S.; W. C. Jordan; M. W. Bruford & R. A. Nichols (2002). Using nested
clade analysis to assess the history of colonization and the persistence of
populations of an Iberian Lizard. Molecular Ecology, 11(4): 809-819
Penalba, C. (1989). Dynamique de vegetation Tardiglaciaire et Holocene du centrenord de l’Espagne d’après l’analyse pollinique. PhD Thesis. Université d’AixMarseille, Marseille, France.
Pérez-Obiol, R. & R. Julia (1994). Climatic change on the Iberian Peninsula recorded in a 30,000-year pollen record from Lake Banyoles. Quaternary Research,
41
Pérez-Suárez, G.; F. Palacios & P. Boursot (1994). Speciation and paraphyly in
western mediterranean hares (Lepus castroviejoi, L. europaeus, L. granatensis,
and L. capensis) revealed by mitochondrial DNA phylogeny. Biochemical
Genetics, 32(11): 423-436
Petit, J. R.; J. Jouzel; D. Raynaud; N. I. Barkov; J. M. Barnola; I. Basile; M. Bender;
J. Chappellaz; M. Davis; G. Delaygue; M. Delmotte; V. M. Kotlyakov; M.
Legrand; V. Y. Lipenkov; C. Lorius; L. Pepin; C. Ritz; E. Saltzman & M.
Stievenard (1999). Climate and atmospheric history of the past 420,000 years
from the Vostok ice core, Antarctica. Nature, 399(6735): 429-436
Petit, R. J.; R. Bialozyt; P. Garnier-Gere & A. Hampe (2004). Ecology and genetics
of tree invasions: from recent introductions to Quaternary migrations. Forest
Ecology and Management, 197(1-3): 117-137
Petit, R. J.; S. Brewer; S. Bordacs; K. Burg; R. Cheddadi; E. Coart; J. Cottrell; U.
M. Csaikl; B. van Dam; J. D. Deans; S. Espinel; S. Fineschi; R. Finkeldey; I.
Glaz; P. G. Goicoechea; J. S. Jensen; A. O. Konig; A. J. Lowe; S. F. Madsen; G.
Matyas; R. C. Munro; F. Popescu; D. Slade; H. Tabbener; S. G. M. de Vries;
B. Ziegenhagen; J. L. de Beaulieu & A. Kremer (2002). Identification of
refugia and post-glacial colonisation routes of European white oaks based on
chloroplast DNA and fossil pollen evidence. Forest Ecology and Management,
 | References
156: 49-74
Petit, R. J.; A. Hampe & R. Cheddadi (2005). Climate changes and tree phylogeography in the Mediterranean. Taxon, 54(4): 877-885
Peyron, O.; J. Guiot; R. Cheddadi; P. Tarasov; M. Reille; J. L. de Beaulieu; S.
Bottema & V. Andrieu (1998). Climatic reconstruction in Europe for 18,000
yr B.P. from pollen data. Quaternary Research, 49(2): 183-196
Prentice, I. C.; P. J. Bartlein & T. Webb (1991). Vegetation and Climate Change in
Eastern North America Since the Last Glacial Maximum. Ecology, 74(4):
2038-2056
Prentice, I. C.; J. Guiot & S. P. Harrison (1992). Mediterranean vegetation, lake
levels and palaeoclimate at the Last Glacial Maximum. Nature, 360(6405):
658-660
Prentice, I. C.; J. Guiot; B. Huntley; D. Jolly & R. Cheddadi (1996). Reconstructing
biomes from palaeoecological data: A general method and its application to
European pollen data at 0 and 6 ka. Climate Dynamics, 12(3): 185-194
Prentice, I. C.; S. P. Harrison; D. Jolly & J. Guiot (1998). The climate and biomes of
Europe at 6000 yr BP: Comparison of model simulations and pollen-based
reconstructions. Quaternary Science Reviews, 17(6-7): 659-668
Prentice, I. C. & T. Webb III (1998). BIOME 6000: reconstructing global mid-Holocene vegetation patterns from palaeoecological records. Journal of Biogeography, 25(6): 997-1005
Ramil-Rego, P.; C. Muñoz-Sobrino; M. Rodríguez-Guitián & L. Gómez-Orellana
(1998). Differences in the vegetation of the North Iberian Peninsula during
the last 16,000 years. Plant Ecology, 138(1): 41-62
Ramsey, C. B. (1995). Radiocarbon calibration and analysis of stratigraphy: The
OxCal program. Radiocarbon, 37(2): 425-430
Ramsey, C. B. (2001). Development of the radiocarbon calibration program OxCal.
Radiocarbon, 43(2A): 355-363
Reid, W. V. (1998). Biodiversity hotspots. Trends in Ecology and Evolution, 13(275280
Reimer, P. J.; M. G. L. Baillie; E. Bard; A. Bayliss; J. W. Beck; C. J. H. Bertrand; P. G.
Blackwell; C. E. Buck; G. S. Burr; K. B. Cutler; P. E. Damon; R. L. Edwards;
R. G. Fairbanks; M. Friedrich; T. P. Guilderson; A. G. Hogg; K. A. Hughen;
B. Kromer; G. McCormac; S. Manning; C. B. Ramsey; R. W. Reimer; S.
Remmele; J. R. Southon; M. Stuiver; S. Talamo; F. W. Taylor; J. van der Plicht
& C. E. Weyhenmeyer (2004). IntCal04 terrestrial radiocarbon age calibration, 0-26 cal kyr BP. Radiocarbon, 46(3): 1029-1058
Rivas-Martínez, S. (2005). Mapa de Series, Geoseries y Geopermaseries de Vegetación de España, Parte 1. Memoria del Mapa de Vegetación Potencial de España,
1-28
References | 
Rivas-Martínez, S.; A. Penas & T. E. Díaz (2004). Biogeographic Map of Europe.
Cartographic Service. University of León, Spain
Roberts, N. (1998). The Holocene: An Environmental History. Blackwell Publishers Ltd, Oxford, 344
Rode, A. L. & B. S. Lieberman (2004). Using GIS to unlock the interactions
between biogeography, environment, and evolution in Middle and Late Devonian brachiopods and bivalves. Palaeogeography, Palaeoclimatology, Palaeoecology, 211(3-4): 345-359
Rohling, E. J. & H. Palike (2005). Centennial-scale climate cooling with a sudden
cold event around 8,200 years ago. Nature, 434(7036): 975-979
Rusch, G. M.; J. G. Pausas & J. Lepš (2003). Plant Functional Types in relation to
disturbance and land use: Introduction. Journal of Vegetation Science, 14(307310
Santos, L. & M. F. S. Goni (2003). Lateglacial and Holocene environmental changes
in Portuguese coastal lagoons 3: vegetation history of the Santo Andre coastal
area. Holocene, 13(3): 459-464
Santos, L.; J. R. V. Romani & G. Jalut (2000). History of vegetation during the Holocene in the Courel and Queixa Sierras, Galicia, northwest Iberian Peninsula.
Journal of Quaternary Science, 15(6): 621-632
Sobrino, C. M.; P. Ramil-Rego & L. Gomez-Orellana (2004). Vegetation of the
Lago de Sanabria area (NW Iberia) since the end of the Pleistocene: a palaeoecological reconstruction on the basis of two new pollen sequences. Vegetation
History and Archaeobotany, 13(1): 1-22
Sobrino, C. M.; P. Ramil-Rego & M. A. R. Guitián (2001). Vegetation in the
mountains of northwest Iberia during the last glacial-interglacial transition.
Vegetation History and Archaeobotany, 10(1)
Sobriño, C. M.; P. Ramil-Rego & M. R. Guitián (1997). Upland vegetation in the
north-west Iberian peninsula after the last glaciation: Forest history and
deforestation dynamics. Vegetation History and Archaeobotany, 6(4)
Steig, E. J.; P. M. Grootes & M. Stuiver (1994). Seasonal precipitaion timing and ice
core records. Science, 266: 1885-1886
Steinfartz, S.; M. Veith & D. Tautz (2000). Mitochondrial sequence analysis of
Salamandra taxa suggests old splits of major lineages and postglacial recolonizations of Central Europe from distinct source populations of Salamandra
salamandra. Molecular Ecology, 9(4): 397-410
Stevenson, A. C. (1985). Studies in the Vegetational History of S. W. Spain. II.
Palynological Investigations at Laguna de las Madres, S. W. Spain. Journal of
Biogeography, 12(4)
Stevenson, A. C. (1988). Studies in the Vegetational History of S. W. Spain. IV.
Palynological Investigations of a Valley Mire at El Acebron, Huelva. Journal of
 | References
Biogeography, 15(2)
Stigall, A. L. & B. S. Lieberman (2006). Quantitative palaeobiogeography: GIS,
phylogenetic biogeographical analysis, and conservation insights. Journal of
Biogeography, 33(12): 2051-2060
Stuiver, M.; P. M. Grootes & T. F. Braziunas (1995). The GISP2 18O climate
record of the past 16,500 years and the role of the sun, ocean and volcanoes.
Quaternary Research, 44: 341-354
Stuiver, M.; P. J. Reimer; E. Bard; J. W. Beck; G. S. Burr; K. A. Hughen; B. Kromer;
G. McCormac; J. V. D. Plicht & M. Spurk (1998). IntCal98 radiocarbon age
calibration, 24,000–0 cal BP. Radiocarbon, 40(3): 43
Surget-Groba, Y.; B. Heulin; C. P. Guillaume; R. S. Thorpe; L. Kupriyanova; N.
Vogrin; R. Maslak; S. Mazzotti; M. Venczel; I. Ghira; G. Odierna; O. Leontyeva; J. C. Monney & N. Smith (2001). Intraspecific Phylogeography of
Lacerta vivipara and the Evolution of Viviparity. Molecular Phylogenetics and
Evolution, 18: 449-459
Taberlet, P. & R. Cheddadi (2002). Quaternary Refugia and Persistence of Biodiversity. Science, 297(5589): 2009-2010
Taberlet, P.; L. Fumagalli; A.-G. Wust-Saucy & J.-F. Cosson (1998). Comparative
phylogeography and postglacial colonization routes in Europe. Molecular
Ecology, 7(4): 453-464
Taylor, D. M.; H. M. Pedley; P. Davies & M. W. Wright (1998). Pollen and mollusc
records for environmental change in central Spain during the mid- and late
Holocene. The Holocene, 8(5): 605-612
Tenorio, M. C.; C. M. Juaristi & H. S. Ollero (2001). Los bosques ibéricos. Una
interpretación geobotánica. Planeta, Barcelona, 597
Trenberth, K. E. & B. L. Otto-Bliesner (2003). Toward Integrated Reconstruction
of Past Climates. Science, 300(5619): 589-590
Valero-Garces, B. L.; P. Gonzalez-Samperiz; A. Delgado-Huertas; A. Navas; J.
Machin & K. Kelts (2000). Lateglacial and Late Holocene environmental and
vegetational change in Salada Mediana, central Ebro Basin, Spain. Quaternary
International, 73-74: 29-46
Valiño, M. D.; A. V. Rodríguez; M. B. R. Zapata; J. G. García & I. d. B. Gutiérrez
(1999). Evolución climática durante el Holoceno en la cuencaalta del Guadiana (submeseta sur Ibérica). Cuaternario y Geomorfología, 13(1-2):
Valiño, M. D.; A. Valdeolmillos Rodriguez; M. Blanca Ruiz Zapata; M. Jose Gil
Garcia & I. de Bustamante Gutierrez (2002). Climatic changes since the
Late-glacial/Holocene transition in La Mancha Plain (South-central Iberian
Peninsula, Spain) and their incidence on Las Tablas de Daimiel marshlands.
Quaternary International, 93-94
van der Knaap, W. O. & J. F. N. van Leeuwen (1995). Holocene vegetation sucReferences | 
cession and degradation as responses to climatic change and human activity
in the Serra de Estrela, Portugal. Review of Palaeobotany and Palynology, 89:
153-211
van der Knaap, W. O. & J. F. N. van Leeuwen (1997). Late Glacial and early
Holocene vegetation succession, altitudinal vegetation zonation, and climatic
change in the Serra da Estrela, Portugal. Review of Palaeobotany and Palynology, 97: 239-285
Vandenberghe, J.; R. Coope & K. Kasse (1998). Quantitative reconstructions of palaeoclimates during the last interglacial-glacial in western and central Europe:
an introduction. Journal of Quaternary Science, 13(5): 361-366
Weiss, S. J. & N. Ferrand eds. (2006). Phylogeography of European Refugia.
Springer, Amsterdam.
Wick, L. & W. Tinner (1997). Vegetation changes and timberline fluctuations in the
Central Alps as indicators of holocene climatic oscillations. Arctic and Alpine
Research, 29(4): 445-458
Williams, J. W.; B. N. Shuman; T. Webb; P. J. Bartlein & P. L. Leduc (2004).
Late-quaternary vegetation dynamics in north america: Scaling from taxa to
biomes. Ecological Monographs, 74(2): 309-334
Williams, J. W.; R. L. Summers & T. Webb (1998). Applying plant functional types
to construct biome maps from eastern North American pollen data: comparisons with model results. Quaternary Science Reviews, 17(6-7): 607-627
Yll, E. I.; R. Perez-Obiol; J. Pantaleon-Cano & J. M. Roure (1997). Palynological
Evidence for Climatic Change and Human Activity during the Holocene on
Minorca (Balearic Islands). Quaternary Research, 48: 339-347
Yonge (1854). The Deipnosophists, or, Banquet of the Learned of Athenaeus. Vol. I,
H. G. Bohn eds., http://digital.library.wisc.edu/1711.dl/Literature.DeipnoSub
Zachos, J.; M. Pagani; L. Sloan; E. Thomas & K. Billups (2001). Trends, Rhythms,
and Aberrations in Global Climate 65 Ma to Present. Science, 292(5517):
686-693
 | References
Appendices
Appendices | 
 | Appendices
Appendix I - Biome affinity scores by sampled site
The affinity scores of each biome by sampled site. The first and second highest scores
are highlighted in bold and italic, respectively. The STEP biome achieves the highest
value several times and has the highest average of all sites. This is masking the results
of other biomes and the second highest value comprises a large extent of
information.
Site name
CLDE
TAIG
CLMX
COCO
TEDE
COMX
WAMX
TUND
XERO
STEP
DESE
Ria de Vigo
8.16
8.16
9.16
9.16
16.22
16.22
13.22
13.36
0.00
5.55
0.00
Sierra de Gádor
2.73
2.73
3.69
3.69
8.69
8.10
10.58
6.50
7.21
9.78
0.35
Rascafria (Sierra de Guadarrama)
Laguna Lucenza (Sierra de Courel)
Fraga (Sierra de Queixa)
5.94
6.10
7.86
7.86
13.99
13.68
9.47
6.81
1.02
13.03
0.00
11.34
11.34
12.96
12.96
14.36
14.36
6.02
15.74
0.00
6.85
0.00
5.74
5.74
8.60
8.60
10.88
10.88
6.86
9.99
0.00
6.16
0.00
Espinosa Cerrato
2.66
3.24
4.40
4.40
7.48
7.36
7.65
5.38
2.61
11.65
0.18
Villaverde
1.55
3.03
3.43
3.43
6.34
6.34
9.36
3.07
6.80
8.17
0.45
Las Pardillas
Laguna Lucenza
3.34
3.40
5.44
5.44
9.39
9.39
7.53
5.56
0.96
4.02
0.00
10.81
10.81
12.78
12.78
28.71
28.71
18.77
13.90
0.00
3.79
0.00
Salada Mediana (Ebro Basin)
0.18
2.36
3.87
3.87
4.05
4.05
5.95
2.09
4.26
6.36
0.00
Lleguna
7.01
12.86
14.41
14.41
21.72
21.63
17.83
11.15
6.91
13.08
0.09
Laguna de las Sanguijuelas
4.22
5.04
5.87
5.87
11.45
11.45
9.17
7.96
1.22
9.89
0.00
Hoyos de Iregua (Sierra de Cebollera)
6.52
7.18
10.05
10.05
12.80
12.80
8.81
10.02
1.37
22.15
0.00
Santo André
4.20
4.20
4.55
4.55
7.94
7.94
7.44
6.34
1.54
5.86
0.00
Lagoa de Marinho
2.53
2.53
2.86
2.86
4.52
4.52
3.79
10.37
0.00
20.32
0.00
Mougás
8.19
10.49
14.30
14.30
20.42
20.14
16.17
13.76
2.30
10.05
0.00
Pena Vella
8.84
10.19
15.41
15.48
22.76
22.38
18.93
12.65
1.29
8.12
0.00
Chan do Lamoso
6.78
9.20
11.99
11.99
19.19
18.65
14.97
11.09
3.30
12.98
0.00
Pozo do Carballal
8.63
12.42
14.18
14.18
23.38
23.00
20.84
12.74
5.58
9.03
0.00
La Piedra
7.52
12.76
14.37
14.37
17.74
17.62
14.46
13.15
6.85
14.98
0.08
Laguna de Lucenza
7.72
11.45
13.56
13.56
20.30
20.02
16.66
11.76
5.41
15.22
0.11
Siles
2.42
10.50
11.41
11.41
13.53
12.93
13.94
6.94
11.16
10.08
0.83
Cañada de la Cruz
0.41
8.31
8.31
8.31
9.66
9.66
9.97
4.64
8.22
14.87
0.52
Charco da CandieiraA
9.90
10.60
10.96
10.96
13.79
13.43
4.95
16.24
0.71
29.45
0.00
Charco da CandieiraB
14.04
14.74
15.45
15.45
19.86
19.39
9.21
21.96
3.54
33.38
0.47
Charco da CandieiraC
14.09
14.80
15.51
15.51
20.78
19.84
10.87
23.92
2.59
36.95
0.00
Charco da CandieiraD
14.12
14.83
15.89
15.89
19.78
18.72
11.89
22.74
2.12
40.82
0.00
Charco da CandieiraE
13.43
14.14
14.85
14.85
16.61
16.26
12.60
17.10
4.02
30.90
0.00
Quintanar de la sierra
4.44
7.29
10.25
10.25
12.65
12.37
10.63
6.27
4.39
11.95
0.10
Appendices | 
Site name
La Cuenca alta
TAIG
CLMX
COCO
TEDE
COMX
WAMX
TUND
XERO
STEP
DESE
3.51
3.51
3.57
3.57
6.84
6.84
9.32
9.09
4.88
17.94
0.00
13.47
13.82
14.53
14.53
18.95
18.95
14.28
16.71
2.89
12.97
0.00
Chan do Lamoso
7.24
7.30
12.17
12.17
19.84
19.84
15.76
10.54
0.06
5.49
0.00
Penido Vello
8.94
8.94
11.30
11.30
16.79
16.79
18.60
13.25
3.70
6.67
0.00
Turbera de pelagallinas
Puerto de los Tornos
5.71
5.71
12.05
12.05
18.34
18.34
15.01
9.98
0.71
6.78
0.00
Suárbol
7.57
10.69
13.55
13.55
21.44
21.09
16.81
12.14
4.07
14.12
0.00
A Golada
8.40
9.34
12.81
12.81
19.10
18.16
13.93
14.62
0.94
14.08
0.00
Brañas de Lamela
9.29
10.35
13.66
13.66
19.78
19.43
15.26
13.40
2.65
16.07
0.00
Pozo do Carballal
11.05
12.88
16.19
16.19
22.20
22.02
14.68
13.35
3.60
6.19
0.00
A Cespedosa
10.25
10.61
13.56
13.56
17.40
17.05
13.09
16.17
1.65
14.71
0.00
Porto Ancares
11.26
11.97
15.05
15.05
19.19
18.84
16.19
17.49
2.83
15.20
0.00
LA Mancha plain
2.80
5.30
5.30
5.30
8.06
8.06
10.88
8.36
7.13
16.94
0.00
El Jardin
2.09
2.09
2.21
2.21
2.32
2.32
1.85
6.96
0.00
7.62
0.00
Alcaraz
1.77
1.77
1.94
1.94
1.94
1.94
1.94
5.36
0.00
9.20
0.00
Portalet
2.96
6.04
8.59
8.59
10.84
10.37
8.36
6.95
3.08
25.46
0.66
Charco da Candieira
8.09
8.66
8.66
8.66
10.22
10.07
3.68
13.80
0.85
34.56
0.42
Lagoa Comprida 1
5.66
5.66
6.36
6.36
7.78
7.78
4.24
10.78
0.71
23.51
0.00
Charca dos Cões
4.24
4.95
4.95
4.95
5.66
5.66
4.24
11.94
1.41
29.87
0.00
Lagoa Clareza
3.18
3.54
4.24
3.89
4.95
4.60
3.18
7.33
0.71
23.60
0.71
Laguna de las madres 2
9.94
12.32
12.32
12.32
13.02
13.02
12.32
18.89
6.07
32.59
0.00
El Acebron (Huelva)
15.18
15.18
15.18
15.18
21.40
21.40
15.18
17.30
1.27
5.37
0.00
Pelagallinas
15.00
15.53
16.42
16.42
20.85
20.85
15.63
18.43
2.65
15.45
0.00
0.71
3.79
3.79
3.79
5.20
4.50
9.01
5.12
7.60
12.19
0.00
La Cruz
El Carrizal
2.97
3.68
4.45
4.45
8.57
8.50
8.78
7.27
2.90
21.21
0.00
Albufera Alcudia
1.91
4.02
5.08
5.08
7.79
7.57
9.50
3.93
6.48
9.11
1.29
Algendar
Alsa
 | Appendices
CLDE
3.38
5.08
6.11
6.11
8.67
6.70
9.54
5.16
4.35
11.58
1.48
10.34
10.34
16.33
16.33
20.88
20.41
12.83
12.48
0.00
6.15
0.00
Antas
0.00
0.17
0.26
0.23
0.63
0.60
2.62
2.96
3.75
14.24
0.59
Atxuri01
4.61
4.61
11.16
11.16
17.78
17.78
14.91
7.90
0.00
4.39
0.00
Banyoles
1.18
1.95
2.60
3.00
4.40
4.80
3.35
4.93
0.73
13.81
0.11
Puerto de Belate
5.64
5.64
12.66
12.66
18.62
18.62
15.65
8.61
0.00
5.24
0.00
Cala Galdana
4.11
4.26
5.16
5.16
6.05
6.05
6.14
6.34
3.92
13.12
0.40
Cala’n Porter
1.67
3.10
4.96
4.96
10.69
7.24
11.35
3.54
7.21
10.70
0.68
Cueto de Avellanosa
6.44
6.44
12.42
12.42
13.88
13.88
9.66
8.18
0.00
1.74
0.00
Hort Timoner
0.87
2.13
3.24
3.24
7.48
4.27
8.86
4.13
4.92
13.25
0.36
Lago de Ajo
4.43
4.68
9.13
9.13
9.44
9.35
5.01
6.57
0.25
3.77
0.00
Laguna de la Roya
3.02
3.27
3.63
3.63
3.69
3.69
0.89
6.57
0.25
7.38
0.01
Navarres (core 1)
0.20
0.20
0.20
0.20
0.52
0.52
1.51
3.27
1.04
7.89
0.02
Navarres (core 2)
0.59
0.59
0.60
0.60
0.83
0.83
1.57
3.15
0.95
8.21
0.12
Pico del Sertal
12.63
12.63
16.27
16.27
20.31
20.31
11.92
13.77
0.00
5.36
0.00
Puerto de las Estaces de Trueba
10.01
10.01
14.08
14.08
15.75
15.75
10.60
10.01
0.00
3.77
0.00
Puerto de Los Tornos
6.15
6.15
10.40
10.40
15.61
15.61
11.50
10.22
0.13
6.48
0.00
Quintanar de la Sierra
3.99
4.78
5.75
5.75
8.06
8.06
4.81
7.45
0.86
8.98
0.17
Roquetas de Mar
0.59
0.59
0.59
0.59
0.92
0.92
2.69
1.29
2.79
12.31
0.19
Saldropo
6.69
6.74
11.37
11.42
16.93
16.99
12.62
9.77
0.10
4.92
0.00
San Rafael
0.42
0.59
0.71
0.83
2.04
2.11
4.80
2.40
5.17
16.64
0.63
Sanabria Marsh
2.43
2.77
3.49
3.51
3.96
3.98
2.35
5.30
0.87
5.38
0.00
Sou Bou
0.83
1.57
2.05
2.05
5.42
2.59
6.26
4.31
3.01
14.08
0.12
Average
5.99
7.10
8.93
8.93
12.60
12.30
10.02
9.88
2.66
13.27
0.14
Appendix II – Script for interpolating affinity surfaces
This script was developed in Python free programming language and implemented
as a toolbox in ArcGIS 9.2 (ESRI 2006). It calculates the interpolated affinity
surface using a pre-made model of interpolation made in the GeoStatistic Extension.
It will search for data in a GeoDatabase and generate all rasters for needed biomes
and sampled ages.
######################################################################
#Exports a raster map of interpolated affinity surfaces for each
#
#biome and for available ages. It needs a geodatabase with different #
#features representing biomes with affinity for each age. Ages field #
#name must begin with “a” (example: for age 5000 field must be
#
#”a5000”). “Null” data is interpreted as NoData. A geostatistical
#
#interpolation model saved as layer file is required.
#
######################################################################
# Create the Geoprocessor object.
import arcgisscripting
gp = arcgisscripting.create()
try:
begin = int(gp.GetParameterAsText(0))
end = int(gp.GetParameterAsText(1))
step = int(gp.GetParameterAsText(2))
end = end + step
anos = range(begin, end, step)
biomas = str(gp.GetParameterAsText(3))
biomas = biomas.split(“,”)
# Set the input GA layer.
inputGALayer = gp.GetParameterAsText(4)
# Set input and output parameters
inputDir = “’” + gp.GetParameterAsText(5)
outputDir = gp.GetParameterAsText(6)
# Check out Geostatistical Analyst extension license.
gp.CheckOutExtension(“GeoStats”)
# Write Parameters file
exportParam = open(outputDir + “/parameters.txt”, ‘w’)
for bioma in biomas:
exportParam.write(“%s “ % bioma)
Appendices | 
exportParam.write(“\n”)
for ano in anos:
exportParam.write(“%s “ % ano)
exportParam.write(“\n”)
exportParam.write(“%s” % outputDir)
exportParam.close()
# Parameters for grid
cell_size = gp.GetParameterAsText(7)
points_horiz = 1
points_vert = 1
# Set the field name
print “Creating surfaces and exporting to grid.”
print “Biome / Age”
for bioma in biomas:
for ano in anos:
inputDset = inputDir + “/”
+ bioma + “’ a” + str(ano)
# Set output layer name
outLayer = bioma + “_” + str(ano)
outputGrid = outputDir + “/” + outLayer
print str(bioma) + “ / “ + str(ano)
# Process: Create a Geostatistical layer
gp.GACreateGeostatisticalLayer(
inputGALayer, inputDset, outLayer)
#Save Geostatistical layer to grid
gp.GALayerToGrid_ga (outLayer, outputGrid,
cell_size, points_horiz, points_vert)
#Deletes Geostatistical layer from memory
for ano in anos:
outLayer = bioma + “_” + str(ano)
print “Deleting “ + outLayer + “ from memory”
gp.delete(outLayer)
except:
# If an error occurred while running
a tool, then print the messages.
print “I’m almost certain that there
is probably an error...”
print gp.GetMessages()
raw_input(“Press Enter to finish...”)
wait till the Enter key is pressed
 | Appendices
#
Appendix III – Script for classify Biomes
This script was developed in Python free programming language and implemented
as a toolbox in ArcGIS 9.2 (ESRI 2006). It generates the map of the maximum
biome affinity. It uses the interpolated affinity scores to search, in a cell by cell basis,
the maximum affinity value and which biome it represents.
######################################################################
#Geoprocessing script to classify Biomes maps based on the maximum
#
#affinity values for each cell and for every age available. Uses the #
#data of Parameters.txt file exported by interpolated Affinity
#
#surfaces. Exports a text file with legend
#
######################################################################
# Create the Geoprocessor object.
import arcgisscripting
gp = arcgisscripting.create()
try:
# Reads parameters file
parametersFile = open(gp.GetParameterAsText(0), “r”)
contents = parametersFile.readlines()
parametersFile.close()
biomas = contents[0].split(“ “)
biomas = biomas[0:len(biomas)-1]
anos = contents[1].split(“ “)
anos = anos[0:len(anos)-1]
inputDir = str(contents[2])
print “Biomes available in parameters file:”
print biomas
print “\n”
print “Years available in parameters file:”
print anos
print “\n”
# Set other variables
OutDir = gp.GetParameterAsText(1)
# Writes legend.txt to identify biomes
legend = open(str(OutDir) + “/legend.txt”, “w”)
i = 1
legend.write(“Value - Biome”)
for bioma in biomas:
legend.write(“\n” + str(i) + “ - “ + str(bioma))
i = i + 2
legend.close()
Appendices | 
# Check out Spatial Analyst extension license
gp.CheckOutExtension(“Spatial”)
# Classification of Biomes for each age
for ano in anos:
print “Calculating Maximum raster for year “ + ano
MaxGrid = OutDir + “/max_” + str(ano)
inGrids = “\””
for bioma in biomas:
inGrids = inGrids + “’” + str(inputDir)
+ “/” + bioma + “_” + str(ano)+ “’;”
inGrids = inGrids[0:-1] + “\””
# Calculates the maximum raster by year
gp.CellStatistics_sa(inGrids, MaxGrid, “MAXIMUM”)
# Calculates biome maximum affinity in raster
print “Classification of Biomes by
Maximum Affinity for year: “ + ano
expression = “”
i = 1
for bioma in biomas:
BiomeGrid = str(inputDir) +
“\\” + str(bioma) + “_” + str(ano)
OutGrid = OutDir + “/c_” +
str(bioma) + “_” + str(ano) + “t”
gp.EqualTo_sa(BiomeGrid, MaxGrid, OutGrid)
gp.Times_sa(OutGrid, int(i), OutGrid[0:-1])
gp.delete(OutGrid)
expression = expression + OutGrid[0:-1] + “ + “
print bioma + “ completed”
i = i + 2
expression = expression[0:-3]
OutFinal = OutDir + “/Biomes_” + str(ano)
gp.SingleOutputMapAlgebra_sa(expression, OutFinal)
print “Biome classfication for
year “ + ano + “ completed”
raw_input(“Press Enter...”)
till the Enter key is pressed
# wait
except:
# If an error occurred while running
a tool, then print the messages.
print “I’m almost certain that there
is probably an error...”
print gp.GetMessages()
raw_input(“Press Enter to finish...”)
wait till the Enter key is pressed
 | Appendices
#
Appendix IV – Script for calculating correlations between maps
This script was developed in Python free programming language and implemented as
a toolbox in ArcGIS 9.2 (ESRI 2006). It calculates the correlation value of all pair of
raster cells. The output is an image of correlation cloud with the general trend and
correlation value. It outputs also a text file with all correlations.
from pylab import *
from numpy import *
# Create the Geoprocessor object.
import arcgisscripting
gp = arcgisscripting.create()
###############################################################
def read_ascii(filename):
#Reads a ascii raster to a numpy array
#Doesn’t output the header data
try:
myfile = open(filename, “r”)
contents = myfile.readlines()
myfile.close()
#reads header and convert data to variables
ncols, nrows, xllcorner, yllcorner, cellsize,
nodata = int(contents[0][14:]), int(contents[1][14:]),
float(contents[2][14:]), float(contents[3][14:]),
float(contents[4][14:]), float(contents[5][14:])
#Create a numpy array with data
MyArray = zeros([nrows-1, ncols], float)
i = 0
for line in contents[7:]:
add(MyArray[i], [float(value) for
value in line.split()], MyArray[i])
i = i + 1
return MyArray
except:
print “Error readind ascii file”
###############################################################
def graphs(dataX, dataY, subplotDef, labels, **kwargs):
#Creates correlation graphs
#kwargs may be labely and labelx
Appendices | 
try:
lim = [0,25]
fontS = int((1.0000/subplotDef[0])*30)
if labels == 0: #sem labels
subplot(subplotDef[0],subplotDef[1],subplotDef[2])
m, b = polyfit(dataX,dataY,1)
plot(dataX, dataY, ‘o’, dataX,
m*dataX+b, ‘r’, linewidth=1, markersize =0.01)
xticks(color = ‘k’, size = fontS)
yticks(color = ‘k’, size = fontS)
elif labels == 1: #labels so no eixo dos X
subplot(subplotDef[0],subplotDef[1],subplotDef[2])
m, b = polyfit(dataX,dataY,1)
plot(dataX, dataY, ‘o’, dataX,
m*dataX+b, ‘r’, linewidth=1, markersize =0.01)
xticks(color = ‘k’, size = fontS)
yticks(color = ‘k’, size = fontS)
xlabel(kwargs[“labely”])
elif labels == 2: #labels so no eixo dos Y
subplot(subplotDef[0],subplotDef[1],subplotDef[2])
m, b = polyfit(dataX,dataY,1)
plot(dataX, dataY, ‘o’, dataX,
m*dataX+b, ‘r’, linewidth=1, markersize =0.01)
xticks(color = ‘k’, size = fontS)
yticks(color = ‘k’, size = fontS)
ylabel(kwargs[“labely”])
elif labels == 3: #labels em ambos os eixos
subplot(subplotDef[0],subplotDef[1],subplotDef[2])
m, b = polyfit(dataX,dataY,1)
plot(dataX, dataY, ‘o’, dataX,
m*dataX+b, ‘r’, linewidth=1,
markersize =0.01)
xticks(color = ‘k’, size = fontS)
yticks(color = ‘k’, size = fontS)
xlabel(kwargs[“labelx”])
ylabel(kwargs[“labely”])
elif labels == 4: #texto no centro sem labels
subplot(subplotDef[0],subplotDef[1],subplotDef[2])
setp(gca(), xticklabels=[], yticklabels=[])
gca().text(0.5,0.5,kwargs[“textgraph”],horizontal
alignment= ‘center’,verticalalignment=’center’, fontsize =8)
subplots_adjust(wspace=0.4, hspace=0.4)
except StandardError, e:
print “Error defining graphs”
print e
###############################################################
# Reads parameters file
###############################################################
parametersFile = open(gp.GetParameterAsText(0),
“r”)
#Parameters file
 | Appendices
contents = parametersFile.readlines()
parametersFile.close()
biomas = contents[0].split(“ “)
biomas = biomas[0:len(biomas)-1]
anos = contents[1].split(“ “)
anos = anos[0:len(anos)-1]
#inputDir = str(contents[2])
###############################################################
# Set other variables
###############################################################
InDir = gp.GetParameterAsText(1)
#Input folder
OutDir = gp.GetParameterAsText(2)
#Output folder
#define subplot position matrix
position = arange(power(len(biomas), 2))
position = reshape(position, (len(biomas),len(biomas)))
###############################################################
#Creates correlation graphs and matrix for each year
###############################################################
try:
for ano in anos:
myoutfile = open(OutDir + “\\” + ano + “_cc.txt”, “w”)
myoutfile.write(“Corelation
coeficient for year “ + ano + “\n”)
for posX in range(len(biomas)):
for posY in range(len(biomas)):
subGraph = [len(biomas),len
(biomas),(position[posY, posX] + 1)]
fileX = InDir + “\\” +
biomas[posX] + “_” + ano + “.txt”
coordX = ravel(read_ascii(fileX))
fileY = InDir + “\\” +
biomas[posY] + “_” + ano + “.txt”
coordY = ravel(read_ascii(fileY))
correCoef =
corrcoef(ravel(coordX), ravel(coordY))
myoutfile.write(biomas[posX] + “ vs “ +
biomas[posY] + “ = “ + str(correCoef[1][0]) + “\n”)
if posX == 0 and posY != len(biomas)-1:
#na primeira coluna, excepto a ultima linha
graphs(coordX, coordY,
subGraph, 2, labely=biomas[posY])
elif posX == 0 and posY == len(biomas)-1:
#no canto inferior esquerdo
graphs(coordX, coordY, subGraph,
3, labely=biomas[posY], labelx=biomas[posX])
Appendices | 
elif posX != 0 and posY == len(biomas)-1:
#na ultima linha, excepto a primeira coluna
graphs(coordX, coordY,
subGraph, 1, labely=biomas[posX])
elif posY <= posX:
graphs(coordX, coordY,
subGraph, 4, textgraph=str(
correCoef[1][0].round(4)))
else:
graphs(coordX, coordY,
subGraph, 0, labely=biomas[posY])
myoutfile.close()
savefig(OutDir + “\\” + ano + “.svg”, dpi=300)
print ano + “.svg saved!”
close(‘all’)
#show()
except StandardError, e:
# If an error occurred while running
a tool, then print the messages.
print “I’m almost certain that there
is probably an error...”
print e
raw_input(“Press Enter to finish...”)
wait till the Enter key is pressed
 | Appendices
#
Appendix V – Script for smoothing rasters
This script was developed in Python free programming language and implemented
as a toolbox in ArcGIS 9.2 (ESRI 2006). It searches for every raster in a folder and
executes a smoother filter by finding the average value of a moving window.
from os import *
from glob import *
import arcgisscripting
gp = arcgisscripting.create()
dir = gp.GetParameterAsText(0)
chdir(dir)
files = glob(‘*.aux’)
makedirs(path.join(dir,’smooth’)) #Creates new folder.
###############################################################
# Converts all GRID rasters in a folder to txt
###############################################################
try:
#Check out Spatial Analyst extension license
gp.CheckOutExtension(“Spatial”)
gp.OverwriteOutput = 1
i = len(files) - 1
for file in files:
print “Smoothing file “ + file[:4] + “ - No files to finish: “ + str(i)
inRaster = dir + “\\” + file[:-4]
outRaster = dir + “\\smooth\\” + file[:-4]
InNeighborhood = “Rectangle 8 8 CELL”
gp.FocalStatistics_sa(inRaster,
outRaster, InNeighborhood, “MEAN”, “DATA”)
i = i -1
except:
# If an error occurred while running
a tool, then print the messages.
print “I’m almost certain that there
is probably an error...”
print gp.GetMessages()
raw_input(“Press Enter to finish...”)
wait till the Enter key is pressed
#
Appendices | 
 | Appendices
Appendix VI – Script for converting ascii files
This script was developed in Python free programming language and implemented
as a toolbox in ArcGIS 9.2 (ESRI 2006). It searches for grid maps in a folder and
converts them to ASCII files.
from os import *
from glob import *
import arcgisscripting
gp = arcgisscripting.create()
dir = gp.GetParameterAsText(0)
chdir(dir)
files = glob(‘*.aux’)
makedirs(path.join(dir,’txt’)) #Creates new folder.
###############################################################
# Converts all GRID rasters in a folder to txt
###############################################################
try:
# Check out Spatial Analyst extension license
#gp.CheckOutExtension(“Spatial”)
#gp.OverwriteOutput = 1
i = len(files) - 1
for file in files:
print “Converting file “ + file[:4] + “ - No files to finish: “ + str(i)
inRaster = dir + “\\” + file[:-4]
outRaster = dir + “\\txt\\” + file[:-4] + “.txt”
gp.RasterToASCII_conversion(inRaster, outRaster)
i = i -1
except:
# If an error occurred while running
a tool, then print the messages.
print “I’m almost certain that there
is probably an error...”
print gp.GetMessages()
raw_input(“Press Enter to finish...”)
wait till the Enter key is pressed
#
Appendices | 
 | Appendices
Appendix VII – Script for masking rasters
This script was developed in Python free programming language and implemented
as a toolbox in ArcGIS 9.2 (ESRI 2006). It extracts the masked area defined by an
input of all rasters files in a folder.
from os import *
from glob import *
import arcgisscripting
gp = arcgisscripting.create()
dir = gp.GetParameterAsText(0)
mask = gp.GetParameterAsText(1)
chdir(dir)
files = glob(‘*.aux’)
makedirs(path.join(dir,’mask’)) #Creates new folder.
###############################################################
# Cuts all GRIDs in a folder by a defined mask
###############################################################
try:
#Check out Spatial Analyst extension license
gp.CheckOutExtension(“Spatial”)
gp.OverwriteOutput = 1
i = len(files) - 1
for file in files:
print “Cuting grid “ + file[:-4] + “
- No of grids to finish: “ + str(i)
inRaster = dir + “\\” + file[:-4]
outRaster = dir + “\\mask\\” + file[:-4]
gp.ExtractByMask_sa(inRaster, mask, outRaster)
i = i -1
except:
# If an error occurred while running
a tool, then print the messages.
print “I’m almost certain that there
is probably an error...”
print gp.GetMessages()
raw_input(“Press Enter to finish...”)
wait till the Enter key is pressed
#
Appendices | 
 | Appendices
Appendix VIII – Script for classifying rasters
This script was developed in Visual Basic for Applications inside ArcGIS 9.2(ESRI
2006). It copies the classification scheme of the selected layer to all other raster layers
in the Table of Contents.
Private Sub CopyRasterRender_Click()
Dim
Dim
Dim
Dim
pMxDoc As IMxDocument
pMap As IMap
pInLayer As IRasterLayer
pOutLayer As IRasterLayer
Set pMxDoc = Application.Document
Set pMap = pMxDoc.FocusMap
Set pFromLayer = pMxDoc.SelectedLayer
Dim pId As New UID
pId = “{6CA416B1-E160-11D2-9F4E-00C04F6BC78E}” ‘datalayers
Dim pEnumLayer As IEnumLayer
Set pEnumLayer = pMap.Layers(pId, True)
pEnumLayer.Reset
Set pOutLayer = pEnumLayer.Next
Do While Not pOutLayer Is Nothing
If TypeOf pOutLayer Is IRasterLayer Then
Set pOutLayer.Renderer = pInLayer.Renderer
End If
Set pOutLayer = pEnumLayer.Next
Loop
End Sub
Appendices | 
 | Appendices
Appendix IX – Script for exporting maps
This script was developed in Visual Basic for Applications inside ArcGIS 9.2 (ESRI
2006). It exports all layers in the Table of Contents to an individual map with a
defined resolution.
Private Sub Ligadesliga_Click()
‘*******************************************************
‘ Creates the layers list and turns on/off sequentially
‘ to export all, with exception of the first
‘*******************************************************
Dim pDoc As IMxDocument
Set pDoc = ThisDocument
Dim pMap As IMap
Set pMap = pDoc.FocusMap
Dim pLayer As IFeatureLayer
Dim i As Long
Dim NumLayer As Long
‘Export definitions
Dim caminho As String
Dim nome As String
Dim tipo As String
Dim dpi As Integer
Dim comp As Double
Dim alt As Double
caminho = “c:\export\”
tipo = “.jpg”
dpi = 300
comp = 4
alt = 2.6
For i = 1 To pMap.LayerCount - 1
pMap.Layer(i).Visible = True
nome = pMap.Layer(i).name
Exporta caminho, nome, tipo, dpi, comp, alt
pMap.Layer(i).Visible = False
Next i
pDoc.ActiveView.Refresh
Appendices | 
End Sub
Public Function Exporta(caminho As String, nome
As String, tipo As String, dpi As Integer,
Comprimento As Double, Altura As Double)
‘**************************************************
‘ Exporta o layout definido com a resolução
‘ definida para um ficheiro de imagem
‘**************************************************
Dim pDoc As IMxDocument
Set pDoc = ThisDocument
Dim pActiveView As IActiveView
Set pActiveView = pDoc.ActiveView
Dim pPageLayout As IPageLayout
Set pPageLayout = pDoc.PageLayout
Dim pGC As IGraphicsContainer
Set pGC = pPageLayout
Dim pGCS As IGraphicsContainerSelect
Dim pElement As IElement
Set pElement = pGC.FindFrame(pDoc.FocusMap)
Dim pVisibleBounds As IEnvelope
Set pVisibleBounds = pElement.Geometry.Envelope
Dim pPixelBounds As IEnvelope
Set pPixelBounds = pElement.Geometry.Envelope
Dim pExport As IExport
If tipo =
Dim
Set
Set
“.ai” Then
pAi As IExportAI
pAi = New ExportAI
pExport = pai
ElseIf tipo = “.emf” Then
Dim pEMF As IExportEMF
Set pEMF = New ExportEMF
Set pExport = pEMF
ElseIf tipo = “.jpg” Then
Dim pJpeg As IExportJPEG
Set pJpeg = New ExportJPEG
Set pExport = pJpeg
ElseIf tipo = “.pdf” Then
Dim pPdf As IExportPDF
Set pPdf = New ExportPDF
Set pExport = pPdf
ElseIf tipo = “.tif” Then
Dim pTiff As IExportTIFF
Set pTiff = New ExportTIFF
 | Appendices
Set pExport = pTiff
End If
Dim hDc As OLE_HANDLE
Dim ExportFrame As tagRECT
pExport.ExportFileName = caminho & nome & tipo
pExport.Resolution = dpi
ExportFrame.Left = 0
ExportFrame.Top = 0
ExportFrame.Right = Comprimento * 0.393700787 * dpi
ExportFrame.bottom = Altura * 0.393700787 * dpi
pPixelBounds.PutCoords ExportFrame.Left, ExportFrame.Top,
ExportFrame.Right, ExportFrame.bottom
pExport.PixelBounds = pPixelBounds
hDc = pExport.StartExporting
pActiveView.Output hDc, pExport.Resolution,
ExportFrame, pVisibleBounds,
Nothing
pExport.FinishExporting
End Function
Appendices | 