Introduction
The distribution pattern of biodiversity today is the result of a dynamic
process driven by geological events and climatic oscillations at a broad
temporal scale . The climate change since the last glacial
period was tracked by species through major range shifts, migrations, and/or
extinctions which may be analysed at the genetic level or from the fossil
record . The relationship
between climate and biodiversity will be maintained in the future with major
consequences due to the current trend of climate warming related to
anthropogenic activities, including range shifts , diversity depletion , and, more
dramatically, species extinction . The biodiversity
hotspots retain high levels of endemism and are considered as the best
candidates for preserving species diversity for the future .
The Mediterranean Basin hotspot, in particular, played the role of refugium to
diverse ecosystems over several hundreds of millennia . Often, those areas where
species have persisted during glacial times are referred to as glacial
refugia , and the predicted high levels of diversity found at species level
in these areas are corroborated at molecular level . Understanding how the past processes impacted biodiversity
patterns offers invaluable knowledge for the current species conservation
effort dealing with the ongoing global climate change .
Species' glacial refugia have been generally defined based on species survival
with a strong relationship with climate . Nevertheless, the term has been used recently with
multiple definitions . The classic
definition of refugia is related to the physiological limits of species that
under an increasingly stressing environment experience distributional shifts
to near suitable areas . Palaeoenvironmental and molecular
data have proven useful to locate species diversity and migration routes
. However, the locations and extension range of putative refugia
still lack spatial consensus and quantification of the refugia's dynamic nature. Reconstructing past environments from
proxy data will help understand climate dynamics and how it may have affected
biodiversity patterns. The past climate changes, species distributions, and
the interplay between them may be reconstructed from the fossil record.
Fossil pollen records have proven to be an appropriate proxy for quantifying
past climate variables . Using proxy data to derive a definition of refugia in terms of
suitable climate in a spatial context may provide further insights into not only the
persistence of species in the past but also the location of potential
areas that may serve as future refugia for the species persistence.
Climate oscillations in Europe during the last 15 000 years exhibited
latitudinal and longitudinal variations . During the Last Glacial Maximum
(LGM), several species persisted in refugia located in the southern
peninsulas . The Iberian Peninsula, with
a milder climate than northern European latitudes , served as a general refugium to several
species that persisted in this area during the LGM. The current patterns of
high biological diversity in the Iberian Peninsula derive partially from this
favourable climate during harsh glacial conditions and highlight the
importance of this area in the broader Mediterranean hotspot
. However, the Iberian Peninsula is not a
geographically homogenous area. Currently Iberian Peninsula is divided into two
main climate zones: the temperate at the northern portion of the peninsula
and the mediterranean, occupying most of the central and southern part
. This pronounced difference in climate patterns in the
Iberian Peninsula also promotes differentiation of the biodiversity patterns
. Additionally, the past vegetation and climate dynamics
reveal a quite complex picture . Thus, multiple areas were identified as small refugia which
lead to the refugia-within-refugia “concept” . Altogether
it renders the Iberian Peninsula as a unique area for studying the
late-Quaternary climate processes with a highly dynamic vegetation response
which is of a high importance for biodiversity
conservation.
Our main objective in this study is to define areas within the Iberian
Peninsula (Balearic Islands included) that share similar climate trends and
which may have served as a potential refugium. We reconstructed three climate
variables and quantified their changes over the past 15 000 years. We also
summarised the geographical areas that have undergone similar climate changes
and analysed their spatial dynamics between 15 000 and 3000 years.
Origin and description of the data sources of fossil pollen used to
reconstruct the climate in the Iberian Peninsula. Source is either the
European Pollen Database (EPD) or author contribution. Longitude and
latitudes correspond to the centroid of the nearest cell to the site and
altitude as extracted from WorldClim data set, all at 5′ spatial resolution.
Each site has information about the number of 14C dates available, the
temporal range covered (see also Fig. for the spatial
distribution) and the respective biome following the classification of
.
Name
Source
Longitude
Latitude
Altitude
14C
Range
Biome
Albufera Alcudia
EPD;
3.125
39.792
11
4
3000–11 000
Mediterranean
Algendar
EPD;
3.958
39.958
80
4
3000–9000
Mediterranean
Antas
EPD;
-1.792
37.208
14
6
3000–9000
Mediterranean
Barbaroxa
-8.792
38.042
38
4
3000–7000
Mediterranean
Cala Galdana
EPD;
3.958
39.958
80
5
3000–8000
Mediterranean
Cala'n Porter
EPD;
4.125
39.875
81
4
4000–9000
Mediterranean
CC-17
-3.875
39.042
617
3
3000–12 000
Mediterranean
Charco da Candieira
EPD;
-7.542
40.375
1221
30
3000–14 000
Mediterranean
Gádor
-2.958
36.875
1413
6
3000–6000
Mediterranean
Golfo
-9.125
38.542
53
5
3000–14 000
Mediterranean
Guadiana
-7.458
37.292
52
8
3000–13 000
Mediterranean
Hoya del Castilho
EPD;
-0.542
41.292
271
3
6000–10 000
Mediterranean
Lago de Ajo
EPD;
-6.125
43.042
1744
6
3000–15 000
Temperate
Laguna de la Roya
EPD;
-6.792
42.208
1780
6
3000–15 000
Mediterranean
Lake Racou
EPD;
2.042
42.542
1906
8
3000–12 000
Mediterranean
Las Pardillas Lake
-3.042
42.042
23
5
3000–11 000
Mediterranean
Navarres 1
EPD;
-0.708
39.125
278
5
4000–15 000
Mediterranean
Puerto de Belate
EPD;
-2.042
43.042
622
3
3000–8000
Temperate
Puerto de Los Tornos
EPD;
-3.458
43.125
893
4
3000–9000
Temperate
Quintanar de la Sierra
EPD;
-3.042
42.042
1546
20
3000–15 000
Mediterranean
Roquetas de Mar
EPD;
-2.625
36.792
94
3
3000–6000
Mediterranean
Saldropo
EPD;
-2.708
43.042
645
3
3000–8000
Temperate
Sanabria Marsh
EPD;
-6.708
42.125
1220
8
3000–14 000
Mediterranean
San Rafael
EPD;
-2.625
36.792
94
6
3000–15 000
Mediterranean
Santo André
-8.792
38.042
38
8
3000–15 000
Mediterranean
Siles
-2.542
38.375
1246
12
3000–15 000
Mediterranean
Padul
-3.708
37.042
1236
17
5000–15 000
Mediterranean
Lourdes
-0.042
43.042
727
9
3000–15 000
Temperate
Monge
-0.042
43.042
727
15
3000–14 000
Temperate
Moura
-1.542
43.458
40
6
4000–12 000
Temperate
Banyoles
EPD;
2.708
42.125
172
2
3000–15 000
Mediterranean
Methods
Study area with sample points. The black area inside each circle
represents the ages available in each pollen sequence.
The study area extends throughout the Iberian Peninsula and the Balearic
Islands (Fig. ). The method used to reconstruct past
climate variables is based on the probability density functions (PDFs) of
plant taxa identified in fossil pollen records, and it requires a
georeferenced distribution of modern plant taxa and a database of modern
climate variables. PDFs for each taxon were built relating the modern
distributions in the climate space geographically. The raw fossil pollen data
were gathered from author's contributions and from the European Pollen
Database (www.europeanpollendatabase.net). Each selected site fits quality
criteria regarding the number of radiometric dates (> 3 in each site) and a
sampling resolution of at least 200 years. Using these criteria we selected a
total of 31 records which cover different time spans between 15 000 and 3000 yr BP (Table ; Fig. ). Although having
the LGM as a lower limit would have provided interesting data, the availability
of sites for the spatial interpolation is very limited before the Holocene.
Thus, we focused on the 15 000 years when there are still 10 sites available
that are necessary for a reliable spatial data interpolation
(Table ; Fig. ). For the climate
reconstruction we assume that modern distributions are in equilibrium with
climate over the species range. This is a reasonable assumption when
considering the spatial resolution of this study. Using georeferenced full
plants distributions reduces the bias resulting from local or isolated
presence of species. These biases are also balanced by the inclusion of
multiple taxa identified in each core for the climate reconstruction.
Data sources
The current distribution data for 246 taxa were obtained by georeferencing the
Atlas of Florae Europaeae . We gathered additional occurrence data for the Mediterranean
flora from the Global Biodiversity Information Facility data portal
(http://www.gbif.org/). These data were checked and
corrected by removing species' presences from botanical and herbaria
collections and/or observations with lower spatial resolution than 30′
(∼ 55 km). The final taxa list and the assignment of pollen taxa
to and modern taxa distributions is given in Table S1.
The georeferenced geographical distributions were rescaled to the resolution
of 30′ (∼ 55 km). The global observed climate data (1950–2000)
for January minimum temperature (Tjan), July maximum temperature (Tjul) and
annual precipitation (Pann) data were obtained from WorldClim database
www.worldclim.org with 5′ resolution
(∼ 10 km). The climate data were downscaled to the same spatial
resolution of the plant distribution data by aggregating the mean value to
the resolution of 30′. All computing was performed using R
with the package rgdal .
Reconstruction of past climate variables
The climate reconstruction method is based on the PDF of each taxon
identified in a fossil dated pollen assemblage. Pollen taxa were assigned to
georeferenced plant taxa (see Table S1). This approach was successfully
used to reconstruct climate variables from fossil pollen data
. Using the PDFs intersection of all
taxa identified in a fossil sample we obtain the most likely climate value
within which the fossil plant assemblage may occur . It has
been observed that normal and log-normal (right skewed) distributions fitted
to temperature and precipitation, respectively, tend to better represent the
data . To avoid sampling the climate spatial
distribution instead of the species tolerance, we corrected for the potential
bias by using binned climate within the species range as a weighting factor
for each climate value . The chosen bin size is 2 ∘C for
temperature variables and 20 mm for precipitation. This procedure decreases
the weight of the most frequent climate values and increases those, in the
distribution of the species, that occur less frequently in the study area
.
Example of the influence of pollen proportion (pp) on the
calculation of the density of taxa presence intersection. The shades of grey
indicate the effect of different pp when the pollen adjustment value (pa)
is set to 0.9 and arrows indicate the assumed presence range. The first case
(dark grey) results from pp =1.0, which represents the highest
detectability and is assumed to be found near the core distribution area and,
thus, near-optimum conditions. The presence is assumed in a narrow range
around peak density with α=pp×pa2 (corresponding to 10 %
of the area). When pp =0.5 (medium grey) the corresponding area is 55 %
and with pp =0.2 (light grey) is used the widest presence range (82 % of
the PDF area).
The reconstructed climate using the PDF method results from combining the
individual PDFs of the species identified in each pollen sample. The product
of the PDFs provides the most likely climate value . To identify a taxon as present in the sample, a threshold of
three pollen grains was chosen. A minimum of five taxa present is required to
reconstruct a climate value for each fossil sample.
Using presence data is seen as an advantage of the PDF method
as well as a weakness due to the exclusion of the
quantitative data resulting from the pollen abundances .
Fluctuations in pollen abundances are related to multiple factors such as the
species ecophysiology, differential pollen production, dispersal capacity, and
other traits . We have used the pollen proportions to weight
the PDFs of the respective taxa. The minimum positive pollen proportion
corresponds to the presence of the taxon, while the maximum defines its
highest abundance within the fossil record. Using pollen proportions of a
taxon within a time series instead of within a sample avoids the bias of
differential pollen production and thus allows estimating the presence of a
species relative to its maximum percentages in the whole record.
Distribution of the reconstructed climate variables in the Iberian
Peninsula and Balearic Islands in the last 15 kyr. Colours show the proportion
of area covered with each class of (a) minimum temperature of
January,
(b) maximum temperature of July, and (c) annual precipitation.
The pollen proportions were converted to alpha values, reducing the species
climate tolerance towards the peak density values (Fig. ).
We assumed that the pollen proportion has an inverse relation to the
proximity of near-optimal conditions. To avoid the selection of a unique
climate value from the PDF when the maximum detection of a species occurs –
i.e. when its pollen proportion is found to be 1, we use a pollen
adjustment value set to 0.9. This means that, at the maximum taxon detection,
the PDF will be reduced to the area of the density corresponding to 10 % of
the probability (Fig. ). The maximum detection of a taxon
indicates a near-optimal climate niche and the adjustment value set to a
value near but not equal to one allows some degree of uncertainty in the
reconstruction. On the other hand, setting this value to zero will not allow
any influence of the pollen proportion, resulting in a binary
presence/absence reconstruction . For each sample, the
collection of the taxa tolerance intervals built this way are added resulting
in a taxon profile, showing where in the climate space the frequency of the
taxon is higher, taking into account the proximity to optimal conditions. The
final climate reconstruction value is the product of the climate PDF with the
taxa profile. The reconstructed value and associated uncertainty are usually
extracted from the PDF as the mean and standard deviation . Assuming a normal distribution, we extract the peak density value
and the 95 % confidence interval from the density profile. The confidence
interval range shows the uncertainty around the reconstructed value and is
related to the standard deviation. The reconstructed values for each site
were fitted with a smoothing spline to produce continuous time series, from
which 1000-year time slices were extracted.
In order to evaluate the robustness of the reconstruction method, we have
compared modern reconstructed and observed climate data (1950–2000) from
WorldClim database. For reconstructing climate from pollen data, we have used
all samples available within the last 500 years. Climate values were averaged
for all sites with more than one sample. The correlation between the two
data sets was tested using a Pearson's correlation score. To provide the
significance of the correlation value, a set of 999 replicates were performed
where the observed climate variable was shuffled without repetition. Although
this evaluation does not take into account neither the climate oscillations
during the last 500 years nor the human disturbances, it still provides a
broad evaluation of the reconstruction method because (1) it depicts per site
the relationship between observed climate data with reconstructed values and
(2) the slope direction of the regression and the related correlation signal
indicate that the reconstruction is spatially coherent. A linear regression
was used to estimate a baseline for calculating the anomalies at each site
using the observed climate. The pre-industrial period around AD 1850
is commonly used as reference climatology to compute anomalies. This period
is also often used as a baseline in climate models, facilitating data–model
comparisons, and it is less biased with recent climate warming allowing past warming to be better depicted . Although a specific
year is selected, the time frame often includes ±500 years
, which is equivalent to the period we have used in our
study. The regression allows a climate baseline to be built without artificially
adding samples to compensate for differential number of samples available for
recent periods in a 4-D (spatial plus time) interpolation
and the linear equations provide all the information to generate the baseline
with the observed climate data.
Spatial analysis of past climate
Thirteen climate grids, ranging from 15 000 to 3 000 calendar years BP
(hereafter, “ka”) with a 1000-year interval, were obtained for each
reconstructed variable by spatial interpolation of the climate anomalies at
each available site. The anomalies were computed for each site between the
reconstructed climate and the modern reference climate. Anomalies were
projected onto a 30′ (∼ 55 km) resolution grid and interpolated
onto a 5′ (∼ 10 km) resolution grid using 3-D thin-plate smoothing
splines with two spatial dimensions including altitude. This interpolation
method generates accurate climate predictions and it was
used for the WorldClim variables .
To further summarise the spatial and temporal variability of the data we
applied a functional principal component analysis (fPCA). The fPCA extends
the exploratory data analysis of the principal component analysis to
functional data , depicting both spatial and time patterns
that are then summarised in a few components. applied a
fPCA to nearly the same timescale as the present study to depict January
temperature patterns from European pollen data. Here we have broadened the
approach to each climate time series available in each grid cell to produce
gridded spatial components. The functional data were built by combining
B-spline basis functions to fit the time series. We have retained the
components that explain more than 90 % of the variance and rescaled the range
from -1 to 1. We used hierarchical cluster analysis over the produced first
components grids of each variable to identify areas in the Iberian Peninsula
that share similar climate trends over the past 15 ka. Climate stability was
computed for each variable as the mean absolute deviance from the current
climate as available in WorldClim data set.
All analysis were performed using the R Project for Statistical Computing
with packages fields ,
rgdal , gstat , and fda
. The climate reconstructions were performed with R scripts
developed by the authors and available at request.
Results
The modern climate reconstructions (500 years) show a high degree of
agreement with the observed climate data (RMSETjan=5.01; RMSETjul=3.85; RMSEPann=399.85; Fig. S1). These data sets show a positive
linear trend and a significant positive correlation (p≤ 0.006 for all
variables), revealing that the reconstruction method predicts well the
spatial distribution of climate. The standard error associated with the
climate reconstruction is on average low but increases with age (Fig. S2).
The reconstructed three climate variables exhibit high spatial variability
between 15 and 3 ka (Fig. , Fig. S3). The uncertainty
associated with the spatial interpolations is usually low, suggesting a good
sampling coverage, except in the northwestern area (Fig. S4). The Iberian
Peninsula had extensive areas with extremely low Tjan that gradually
increased markedly after 10 ka. The pattern of Tjul over the same time
remains stable, with lower values before 12 ka. There is a decreasing trend
of precipitation, especially after 10 ka (Fig. ), which is
marked mostly in the south-eastern part of its area (Fig. S3).
Minimum and maximum temperatures of January and July, respectively,
and annual precipitation during the last 15 kyr. The solid line represents the
average climate in the study area. The remaining lines are the average of
each cluster found – C1: short-dash line; C2: dotted line; C3: dash-dot
line; and C4: long-dash line.
The clustering of the first fPCA component for the three reconstructed
variables are spatially structured (Fig. ), and allow their overall trends to be summarised (Fig. ). The first component of
each variable explains more than 95 % of the variation (Tjan: 95.5 %; Tjul:
99.2 %; Pann: 99.5 %). Cluster C1 (27 % of the total area) is located mostly
in northern and western Iberia and includes part of the north-Iberian
mountain ranges but also low altitudinal coastal areas (average altitude is
679 ± 454 m). This is the wettest cluster with Pann ranging from 1054 to
1115mm, the coldest in July (21.7 < Tjul < 24.2 ∘C) and with very low January
minimum temperatures (-5.5 < Tjan< 0.2 ∘C). The C2 cluster encompasses part
of the Cantabrian mountain range and the central Iberian system (28 % of the
total area with an average altitude of 859 ± 303 m). It occupies most of the
northern plateau, where it has the lowest January temperature
(-5.7 < Tjan < -1.3 ∘C), whereas July (25.1 < Tjul < 27.7 ∘C) is warmer than within C1. This
shows high seasonal amplitude with low precipitation (537 < Pann < 621 mm),
similar to C3 and C4. The dissimilarities between clusters C3 and C4 (25
and 20 % of the total area and average altitude of 613 ± 96 and 278 ± 232 m,
respectively) concern mainly the temperature. Cluster C4 is warmer and wetter
than C3. These are the warmest areas for both January (C3:
-1.7 < Tjan < 3.0; C4: 1.0 < Tjan < 6.4 ∘C) and July (C3: 29.4 < Tjul < 33.4;
C4:
27.2 < Tjul < 30.3 ∘C) and with low annual precipitation (C3:
504 < Pann < 614; C4: 555 < Pann > 682 mm). The Balearic Islands are fully included in
the C4 cluster (Fig. ).
Hierarchical cluster analysis of the functional PCA components of
Tjan, Tjul, and Pann in the last 15 kyr found in the study area. The top
dendrogram represents the size of the clusters of similar climate evolution
and the relations between them. Numbers correspond to each identified
cluster.
The mean absolute deviance from the current climate shows that the climate
stability during the last 15 kyr was not spatially uniform
(Fig. ). Tjan and Pann exhibited higher stability in the
southern Iberia, although Tjan has lower values of deviance (higher
stability) towards the eastern coast and Pann towards the western coast. Tjul
exhibited lower deviance at higher altitudes, particularly at the central
system, northern mountains, and Pyrenees, but also in the southern Sierra
Morena.
Average differences between millennia for each of the climate
variables. Calculation of the differences are computed between a given age
and the previous one. Isolines in each map indicate the average value of
change.
Discussion
Fossil pollen data provide a record of vegetation changes which constitutes a
valuable proxy for reconstructing past climate changes, especially using
large data sets . The method used here provides reliable
climate reconstructions, despite the low number of sequences selected
according to our quality criteria for spatial climate reconstruction, both in
terms of sampling resolution and number of 14C dates. The western part
of the peninsula has a better data coverage which provides more robust
spatial interpolations, particularly for the most recent to middle time
periods analysed. Nevertheless, the spatial uncertainty related to the
interpolation shows a uniform variance for all time periods (Fig. S4). The
only exception is the north-western part of the study area, where the lack of
data promotes higher uncertainty for the spatial interpolation. The residuals
between observed climate and reconstructed climate were high, resulting also
in a low coefficient of determination for the linear regression (Fig. S1).
However, this is expectable since we were comparing observed climate data
with reconstructed values of the last 500 years and averaging the climate
variation in this period tend to increase the residuals. In addition, the
anthropogenic impact on the ecosystems is likely also biasing the results.
Nevertheless, a positive linear trend with a significant positive correlation
was found between reconstructed climate and observed climate that allows us
to produce a reference data set using this model and the observed climate. The
results provided here reinforce the role of the Iberian Peninsula as a
glacial refugium and holding enough climate variation
(Fig. ) to support a network of smaller refugial areas
.
Climate of the last 15 kyr was dynamic, with oscillations of temperature and
precipitation occurring mostly at the southern part of the peninsula. Given
the link between climate and species distributions , it is
likely that these changes had an impact on the location, extent and evolution
of the refugia and the recolonisation processes during the post-glacial
period. Nonetheless, the reconstructed overall trend is a noticeable warming
in winter temperatures after 15 ka, particularly between 12 and 9 ka
(Fig. ) that is likely due to the increase in the summer
insolation in the northern hemisphere . This warming trend
tends to reduce the area in the Iberian Peninsula with very low temperatures
(Fig. ). Although insolation peaks at 9 ka and decreases
afterwards, it does not translate to a general cooling and in south-western
Europe is seen an increase in insolation in both summer and winter
. A striking pattern is the partitioning of the peninsula in
spatially structured areas that shared similar climate trend over the
late Quaternary (Fig. ). The wettest and cold cluster C1 is
predominantly located at the northern and north-western Iberia and occupies
most of the current temperate climate zone. Although very similar to C2, it
contrasts in the seasonal amplitude and precipitation amount. Interestingly,
the pattern of current bioclimate zones in Iberian Peninsula is retrieved on
the clusters scheme, suggesting the persistence of a transition area between
very different climate zones, although the magnitude of the differences have
changed in the past.
Our results show that January temperatures exhibited a general warming trend
over the last 15 000 years which corresponds on average to an increase of
∼ 5.5 ∘C. The southern part of the peninsula is more resilient
to change, particularly for Tjan and Pann, whereas the northern part recorded
major changes. This pattern is less obvious for July temperatures, where
variations showed a smaller amplitude even though this variable is markedly
different between clusters, thus contributing to the climate split of the
study area (Fig. , Fig. S5). The minimum winter temperatures
constrain the physiologic ability of plants to further development and, thus,
are a major factor restricting distributions . Higher summer
insolation provides enough energy to plant growth and July temperature in the
Mediterranean is a less limiting variable for growth than Tjan which makes
the reconstruction of summer months and its interpretation more complex.
The end of the Pleistocene
The 1000-year time interval provides enough resolution to analyse general
patterns of climate evolution. However, abrupt climate events are not
detectable. The end of the Oldest Dryas (OD; ending around 14.5 ka) is
characterised in Iberia by a vegetation change compatible with cold and humid
conditions and is followed by the
Bölling–Allerød warm period (B-A; ending around 13 ka). Our results show a
similar pattern with colder conditions between 15 ka and 13 ka and a higher
humidity (Fig. ), particularly evident in the central and
southern clusters. Although all clusters show a similar trend, the C1 and C2
are colder than average. The general pattern in the Iberian Peninsula is a
contrast between a colder north and a warmer south but, nevertheless, an
area dominated by low January temperatures (Fig. , Fig. S3, S5). The evolution of precipitation during the last 15 kyr in the Iberian
Peninsula shows a very stable pattern: northern areas comprised in C1 had
high precipitation values during the period analysed, while the south was
wetter than today (Fig. S3, S5). The increase in the moisture availability
during the B-A is in line with the slight increase in
precipitation in all clusters between 14 and 13 ka (Fig. ).
As described earlier in Europe , Tjan shows
wider changes in amplitude than Tjul. The cold to warm transitions that
occurred at ∼ 14.7 and 11.5 ka in Europe had a spatial impact that is noticeable in the
reconstructed temperatures (Fig. , Fig. S3, S5).
The Holocene
The B-A warm stage is followed by the cold Younger Dryas (YD; between
∼ 12.9 and ∼ 11.7 ka), marking the beginning of the
Holocene. This period records a warming trend for Tjan, while Tjan decreased
abruptly (Fig. ) with a reduction of the warmer areas between
13 and 12 ka (Fig. ).
The Holocene warm period (approximately between ∼ 8.2 and 5.6 ka, depending on the location in Europe) is characterised by increasing
summer temperatures . Such trend is more obvious in northern
Europe and the Alps, while we rather observe a cooling at lower latitudes
. Our results point to a slight decrease in Tjan and Tjul
around 7 ka, but the overall temperature pattern is rather stable. This is
likely affected by the temporal resolution of this study, failing to clearly
detect rapid events. Pann shows a slightly wetter climate at 7 ka
(Fig. ) which is consistent with earlier reconstruction for
the southern European lowlands .
Between 6 and 3 ka, areas with low precipitation expand in the Iberian
Peninsula (Fig. ), which allows the expansion of the
Mediterranean taxa . The
increasing aridity trend in the south is balanced by the high precipitation
values in the north (Fig. , Fig. S3, S5), contributing to
the shaping of the current Iberia pattern of two contrasting bioclimatic
regions: the north is temperate and wet while the south is a dry and warm.
The behaviour of the reconstructed variables at 5 ka is likely to be
influenced by non-natural ecosystem changes due to human activities such as
the forest degradation that began in lowlands and later in mountainous areas
. These human impacts add confounding effects in the
fossil pollen record and may lead to slightly biased temperature
reconstructions after 5 ka. On the other hand, human impacts at larger scales,
capable of leaving noticeable imprints on landscape, were likely to happen
later and, furthermore, there is evidence of a cooling
and drier stage in the Iberian Peninsula after 5 ka .
Climate role in Iberian refugia
The climate change since the LGM in the Iberian Peninsula had an impact on
the persistence of temperate species, migrating pathways, and on the overall
recolonisation processes during the post-glacial period within the peninsula
. During this period, climate
favoured migrations and expansion processes that culminated in secondary
contacts for several lineages previously isolated in patches of suitable
habitat . Particularly
the B-A warming phase and the warming stage after the YD, which we show here
and which have highly affected the spatial organisation of the climate in the
Iberian Peninsula, are likely favouring expansion processes of warmth-dependent
organisms. Given the relationship between climate change and biodiversity
patterns, the clustering scheme (Fig. ) depicting areas
with different climate evolution is consistent with the molecular evidence of
a network of putative refugia within Iberia . Refugia have
been associated with climate and habitat stability, with both playing
complementary roles . However, as shown by large-scale
landscape analysis and climate
reconstructions , both have a strong dynamic
nature in the Iberian Peninsula and likely promoted the formation of patches
of suitable habitat during harsh conditions. The highly structured
populations that many species exhibit in the Iberian Peninsula have
contributed decisively to the idea of refugia diversity . Overall, the information included in the multidimensional climate
data allowed us to define areas characterised by a climate evolution during
the late Quaternary with smaller amplitude of change (clusters C3 and C4).
These areas showed higher stability of both Tjan and Pann
(Fig. ). Cluster C4 coincides at a great extent with areas
that offered more resilience to change between millennia
(Fig. ). Within these areas, temperature and precipitation
were suitable to support the survival of temperate trees, likely acting as
glacial refugia. On the other hand, the cold areas of the first and second
cluster also associated with faster changes cluster likely diminished the
suitability for the long term persistence of species. One might infer that
the defined clusters are associated with potential isolation or dispersal
events of species throughout the studied time span. Particularly, the fourth
cluster (Fig. ) includes areas that have already been
described as glacial refugia for several animal and plant species
(; see chapter 5 for a review of refugia in the Iberian
Peninsula). In the area represented by this cluster, the
reconstructed Tjan indicate a mild climate with higher precipitation than
currently, which is compatible with the persistence of species in these
areas. The southern plateau, mostly comprised in the second cluster
(Fig. ), also recorded mild conditions which are often
associated with southern refugia; however, the rapid Tjul oscillations associated
with a cold Tjan and low precipitation may have prevented long-term
persistence but are likely compatible with a recolonisation process.
The pattern of stability indicates a southern Iberia with less change,
particularly Tjan and Pann. High altitudes offer more resilience to change,
particularly to July temperature, and lower areas may be swept rapidly with
occurring changes (see Fig. S6). Our data suggest that, at the regional
scale and with extensive time-series data, this relation is preserved. Areas
of lower velocity of change, which are hence more stable, are associated with high
levels of endemicity at global scales , and areas of high
velocity are often associated with species extinction
. Our results indicate higher stability in the
southern part of the Peninsula, similar to other studies based on climate
data . However, our studied time frame extends to 15 ka, which does not cover the glacial maximum (∼ 21 ka). At that
time period, a higher degree of fragmentation of the stability is expected
due to colder conditions, and areas compatible with refugia would be also
less contiguous. These could be seen as a macrorefugia, offering conditions
for large population during glacial times . Microrefugia are
known to occur in the northern areas of the Iberian Peninsula
e.g., but the spatial scale used and the number
of pollen sites available render microrefugia undetectable in this study.
Conclusions
The reconstruction of past climates using biological data is an invaluable
resource for the study of the dynamics of glacial refugial areas. Although
there is a limited number of available sites and time range coverage, the
spatial combination of fossil pollen data provides a continuous record with a
climate signal that can be translated into spatially explicit analysis of
climate dynamics.
The reconstructed climate variables for the post-glacial period show
different patterns of evolution but are clearly marked by the lasting impact of
climatic events. The Iberian Peninsula had areas that shared similar climate
evolution during the late Quaternary. Some areas that we have suggested as
potential refugia are consistent with those areas where genetic diversity was
found to be high and which are often considered as refugial areas for several
animal and plant species.
The analysis of these areas and the related climate provides new insights
into the dynamics of refugia through time and space, which helps in gaining a better
understanding of the evolution of biodiversity hotspots both at the species
and the intraspecific levels. Linking past climate and diversity in the
Iberian Peninsula is a major issue for conservation issues, especially under
the expected future climate change.
Data availability
The distribution data for Mediterranean species were downloaded from GBIF portal (http://www.gbif.org).
The raw fossil pollen data is accessible for most used sites at the European Pollen database (http://www.europeanpollendatabase.net).
For the remaining sites, data is available by request to the author of the respective publication
(see Table 1 for a description of the data sets and sources). The climate data is accessible at the WorldClim website
(www.worldclim.org).
The output reconstructions, anomalies and variance maps are available in NetCDF format as supplementary material.