ICES CM 2009/E:10 (Ref. Num.: 1069)
Not to be cited without prior reference to the author
Do climate patterns explain by themselves the oscillations observed for
the Bluefin tuna (Thunnus thynnus) at the Gibraltar Strait-Western
Mediterranean ‘almadrabas’ traps catches since 1525 to 1969?
Ángela M. Caballero-Alfonso*, Ganzedo-López, U., Trujillo-Santana, A., Santana del
Pino, A. and Castro-Hernández, J. J.
* Ángela M. Caballero-Alfonso.
University of Las Palmas de Gran Canaria (Spain).
Campus Universitario de Tafira. Edificio de Ciencias Básicas. Facultad de Ciencias del Mar, sn.
Lab.: B-203.1. C. P.: 35017. Las Palmas de Gran Canaria. Spain.
Tlf.: +34 928454549 / +34 646248655
Fax: +34 928452922
E-mail: [email protected]
ABSTRACT
In the Gibraltar Strait-Western Mediterranean, the bluefin tuna (Thunnus thynnus) has
been caught for centuries using trapnets (‘almadraba’ in Spanish terminology). The first
evidence dates roughly from 900 years BC. Historical registers of these catches reveals
a decreasing trend, almost regularly, between 1525 and 1995, with some cyclical
fluctuations. However, and in order to minimise the overfishing effects, we have
considered for our analyses only the period from 1525 until 1969. The markedly and
heterogeneous seesaw that can be observed in the almadraba capture series, seems to
show the influence of climatic factors on the fisheries. Time series analysis was carried
out for different climatic variables, over the last 400 years, and compared with bluefin
tuna capture series obtained from various almadraba datasets from the Gibraltrar strait
area and Western Mediterranean Sea. Results highlights that the low temperature
occurred during the Gleissberg minimums (i.e.: Spörer, Maunder and Dalton
Minimum’s) might have produced a significantly decrease in tuna recruitment and
abundance in the North Atlantic and Mediterranean Sea. Our results indicate that
environmental factors play a key role on the tuna population dynamic, as well as the
efficiency with which the trapnets sample them.
Keywords: Bluefin tuna (BFT), Thunnus thynnus, almadraba, climatic factors, Gibraltar
Strait, Western Mediterranean Sea.
INTRODUCTION
The Atlantic Bluefin Tuna (Thunnus thynnus, Linné 1758) (BFT hereafter) has
been historically important, due to it commercial interest since Phoenicians times (900
years BC) (López-Capont, 1997; Pairman-Brown, 2001), and it is one of the oldest
fisheries organised on an industrial scale (Lemos and Gomes, 2004). Since fishermen
understood that this specie appears regularly near the coast, keeping the shore on their
right side and performing an anticyclonic movement around the Mediterranean Sea,
they set up specials trapnets (‘almadraba’ is the Spanish term) which guided hundreds
or thousands of fishes into the gear (Sará, 1980). This fact, favoured the development of
numerous settlements and industries, between the 16th and 19th centuries, along the
Western Mediterranean coasts of the European and African continents. However, its
biology and fishery started to be studied from the late 19th century (i.e.: Buffon, 1847;
Lozano-Rey, 1952; Rodríguez-Roda, 1964). Until the beginning of the 20th century, the
almadrabas operated along the North-east and Western Mediterranean (Sará, 1980), but
owing coastal traffic, pollution and the decrease in BFT abundance, among other
reasons, this fishing gear has been in constant declining.
Beside all the information that can be gathered from BFT historical data,
important uncertainties are still unsolved (sensus Fromentin, 2003). Some of them
related to the catches data: (i) under- and mis-reporting, (ii) illegal catches of age 0group and (iii) fleets with flags of convenience. Others related to the fishing effort: (i)
changes in the gear technology and strategies, (ii) new selectivity pattern and fishing
areas, (iii) vessels cooperation and competition and, (iv) fishing in restricted areas.
Uncertainties related to modelling: (i) ageing procedure, (ii) technical specifications in
the age-structure model and (iii) inadequacy of the projection model. And, finally,
uncertainties related to the lack of information: (i) natural mortality, (ii) stock
assessment units, (iii) variations in growth and, (iv) long-term fluctuations.
In relation with this last, it is well known that BFT is highly migratory and
widely distributed in the North Atlantic Ocean and adjacent seas, but 95% of captures
are made in the East Atlantic region (Fromentin, 2003). For this reason, is important to
understand the fluctuations that have been taking place in the historical catches serie
and which variables are behind those variations. This will allow predicting the future
evolution of the BFT stock and to manage according to it. Furthermore, long-term
fluctuations might be, in some way, environmentally forced (Cushing, 1982; Lemos and
Gomes, 2004; Fromentin and Powers, 2005). And, if these traps are samplers which
caught always the same proportion of BFT each year, because they are passive devices
that haven’t change structurally in a significant way through time (Ravier and
Fromentin, 2001); we can assume, for this reason, that the almadrabas that exploit the
same population should show the same long-term fluctuations (sensus Ravier and
Fromentin, 2001; Lemos and Gomes, 2004). In this sense, Ravier and Fromentin (2001)
found a noteworthy dominant 100-120 year periodic fluctuation in traps around the
Mediterranean and the East Atlantic. In 2004, they related those long-term fluctuations
to the inverse of the temperature registered in the almadrabas domain. In contrast, in a
local scale approach, only the short-term fluctuations (periodicity ≤ 10 years) are
comparable (Ravier and Fromentin, 2001).
The influence of the North Atlantic Oscillation (NAO) on marine species of the
North Atlantic area has been documented in several studies (e.g.: Fromentin and
Planque, 1996; Alheit and Hagen, 1997; Lloret et al., 2001; Ottersen et al., 2001).
However, others highlight the relevance that other climatic parameters, besides the
NAO, have for instance on tuna populations (Pepin, 1991; Fromentin and Fonteneau,
2001; Ravier and Fromentin, 2004; Ganzedo et al., 2009), where temperature seems to
be the predominant factor. In addition, Crowley (2000), using an energy-balanced
climate model, highlighted that 41-64% of the decadal-scale temperature variations, in
the pre-industrial time (before 1850), was the result of changes in solar irradiance and
volcanic activity. Furthermore, Usoskin et al, (1998; 2005) have observed negative
correlation between temperature and cosmic rays.
The aim of this study is to try to shed light on the BFT fluctuations and to try to
elucidate the causes of that variability. To reach this objective, climatic variables of
different nature (solar, atmospheric and oceanographic) were considered. Temporal and
spatial evaluations of biological and climatic long-term series were conducted, since
previous works (see Ravier and Fromentin, 2001; 2004) support the hypothesis of
synchrony in these long-term fluctuations.
MATERIAL AND METHODS
1. Data
1.1. Tuna catch data series
The historical BFT catch series were extracted from different sources (Table 1).
Therefore, is important to identify each one in order to determine the geographic
domain of each almadraba. All the data considered were recorded as number of tuna
caught during defined periods. Nevertheless, it must be taken into account, that the
series contain numerous missing values, due to different facts (e.g., number of traps
varied along time, destruction/loss of archives or data were available in weight instead
of number of tuna), or the number of tuna caught was not available for all the years (see
references for more details).
Furthermore, since the aim of this study is to elucidate if the fluctuations
observed in the almadrabas catches were due to climate variations, series with less than
100 year data were not considered. Moreover, despite that capture information was
gathered and contrasted until 1995, 1969 was the more recent year considered to avoid
the possible effect of the current fishing pressure on the tuna fluctuations.
1.2. Climatic data series
Data series of different climate variables have been considered, much of which
has been obtained from internet, both from climate reconstruction models and scientist
work on climatology items (proxy data).
1.2.1. Modelled Data
Zorita et al. (2005) data were modelled from a grid of eight longitudes and four
latitudes, from 15º W to 11.25º E, and from 42.678º N to 31.545º N; longitudes were
3.75º equidistant, while latitudes were 2.78º equidistant. These models were carried out
for (i) Sea Surface Temperature (SST), (ii) Radiance and (iii) Precipitation.
For our analyses, mean data series were obtained for the East Atlantic and West
Mediterranean domains. The nomenclature considered hereafter is: East SST, West
SST, East Radiance, West Radiance, East Precipitation and West Precipitation.
1.2.2. Proxy Data
(i) Reconstructed annual air temperature at sea surface level from 1525 to 1969
(hereafter: Mann SST) for the whole area, from Mann et al. (1998, 1999).
(ii) Eleven years Solar Irradiance cycles (Solar Irrad., hereafter) and 11 years
Solar Irradiance cycles background (Solar Irrad. Bckrd, hereafter), from 1610 to 1969
(Lean et al., 1995).
(iii) Monthly Sun Spot numbers were gathered from the NOAA (SIDC) data
base since 1749 until 1969. From this monthly data, yearly means were done for
convenience to the present study.
(iv) Volcanic Dust and Green House Gases (GHG hereafter) obtained from
Crowley (2000), from 1525 to 1969.
2. Statistical analysis
2.1. Exploratory data analyses
Shapiro test was applied to the original almadraba catches and climatic series in
order to confirm normality, and those capture data series without normal distribution
were logarithmic transformed. However, some of the series still being non-normal
through the Shapiro test, due to the high dispersion of data and gaps.
The second step of the exploratory analysis was to examine the almadrabas and
climate data series trends through a lineal model approach. However, trend was not
treated when found, because if removed we would loose the low frequency variability
(red noise) associated to climate variability.
2.2. Hierarchical clustering analysis
To evaluate the correspondence behaviour between almadrabas, and to elucidate
which ones can be grouped under the same environmental phenomena from the
observed structure in the data series, it was performed a hierarchical clustering analysis
(Wilks, 2006). Result of this analysis is shown as a tree-diagram or dendrogram.
2.3. Correlation analyses
Due to some capture series could not be normalized, linear Spearman correlation
and a 12-years lag cross-correlation analyses were done to determine the degree of
correspondence between catches and climate data. Twelve-years, was the maximum
time considered for the delay term in the cross-correlation analyses, because of the
disparity in age-at-maturity quoted by various authors (Caton, 1991; Schaefer, 2001;
among others). Twelve-years was the maximum age at first maturity recorded for BFT
in the West Atlantic (Fromentin, 2006). However, even for the East Atlantic and the
Mediterranean sea, the later age-at-first maturity has been estimated between 4-8 years,
but we have considered 12 years to have a better range of certainty.
2.4. Generalized Lineal Model (GLM)
A GLM was conducted following Hastie and Tibshirani (1990) to test the
relation between the explained variable (Y, captures) and the predictor’s variables (Xi,
climate descriptors). From the results, only terms with highest significant correlations
were considered. As highlighted by Legendre and Legendre (1998), temporal structures
in time series may incorporate other effects, such as biotic, environmental or historical
ones. To take into account this complex temporal structure, a third-degree temporal (T,
in years) polynomial term was considered in the GLM.
Three GLMs have been constructed: (i) Model 1- a temporal analysis to the
captures, where the third degree polynomial was selected; (ii) Model 2- the
environmental variables where the only ones considered to evaluate the capture data;
(iii) Model 3- a global model, where temporal and climate factors were considered
together. In this last step, variance partitioning analysis was done to quantify the relative
contribution of environmental and temporal (autocorrelation) factors.
The BFT data series analysis, with many missing values or gaps, was carried out
dividing the series into parts, as many as necessary. This is indicated with a number in
brackets beside the almadrabas names (Table 1).
2.5. Bootstrap analysis
Finally, a bootstrap method, following Mudelsee (2003), was carried out to test
the significance of the GLM results. A common problem in climatological time series is
the non-normality of data distribution. For this reason, the stationary bootstrap may
solve this problem through a free distribution assumption which reaches normality
through a re-sampling with replacement method, where persistence is preserved. This
approach was corroborated through a Monte Carlo test (Mudelsse, 2003).
RESULTS
1. Captures series
BFT catches fluctuates in all the almadrabas, showing a general decreasing trend
(Figures 3.1 to 3.3). However, the levels reached at the beginning of the oldest
almadrabas (Zahara and Conil) never was so high again (73 000 tuna in 1568 and 65
312 ones in 1556, respectively). At the end of the 1800s and beginning of the 1900s, the
catches seem to recover in some traps (Medo das Casas, Barril, Saline, Porto Paglia,
Porto Scuso and Isola Piana). This fact coincides with the beginning of the Industrial
Revolution (1850). However, it was followed by a collapse, after reach the minimum
values.
When making the hierarchical clustering analysis for all the almadrabas in
evaluation (Figure 2), it can be appreciate that the Spanish ones (Zahara and Conil) have
a totally different behaviour than the others. Also, the Portuguese almadrabas do not
behave in a similar way among them (Barril and Medo das Casas). On the other hand,
all the Western Mediterranean (Sicilian and Sardianian) almadrabas seem to have the
same evolution trend, being Formica (Sicily) the most different one of this group. The
almadraba of Formica behaves as a mid step between Barril one and the rest of the
Western Mediterranean traps.
2. Temporal analyses (GLM model 1)
Considering only the temporal effect (autocorrelation) on the captures, it was
appreciated that in most of the cases a third degree polynomial term was selected trough
the GLM stepwise procedure (Table 2), to have the smallest estimated standard error
and the smallest p-value.
In all the cases, except for the data of the almadraba of Formica from 1839 to
1855, the temporal influence was highly significant (Table 2).
3. Climatic analyses (Spearman tests and GLM model 2)
Proxy and modelled climate parameters have been considered in our analyses.
All of them have been used in the GLM models 2 (Table 3) and 3 (Table 4).
Nevertheless, the volcanic dust was only used in the Spearman lineal and crosscorrelation tests because it was a punctual phenomenon (detailed results not shown).
Due to this, it was considered of no sense to take into account it effect in the long-term
trends (GLM).
The volcanic dust is high-lineally correlated with almost all the almadrabas
capture series, excluding Bonagia and Formica ones. If we consider a lag between zero
and twelve years when correlating dust with captures in the different almadrabas, the
highest significant results were obtained for lag = 0 with Barril (rho = -0.68), Zahara
(rho = -0.46) and Conil (rho = -0.31) in the Gibraltar Strait (for Medo das Casas, the
highest significant correlation was found for lag = 7, rho = -0.59). For the almadrabas in
the Italian domain, the highest correlation at lag zero were found for almost all the
trapnets, excepting for Porto Paglia (lag = 1; rho = -0.20) and for San Giuliano (lag = 3;
rho = 0.47). Again, no significant Spearman cross-correlations were obtained for
Bonagia and Formica.
The Green House Gasses (GHG) was the parameter that seems to influence most
on tuna capture series. However, the difference with the Sea Surface Temperature
(Mann SST) proxy influence was no considerable. On the other hand, the number of
Sun Spots and the modelled SST (in the East) and Precipitation (in the West) were the
lesser ones. See Table 3 (model 2). However, when the bootstrap was carried out to
these variables obtained from model 2, most of them result no significant (Table 3).
In conclusion, when taking into account the environmental factors only, as the
causes of the BFT fluctuation, the GHG and the Lean et al. (1995) Solar Irradiance
Background were the parameters that can be considered as influencing variables.
4. Global analyses (GLM model 3)
Through the global stepwise GLM model (model 3), almost all the
environmental variables were significantly correlated with BFT captures at the
almadrabas (Table 4). Once more, GHG and proxy SST (Mann, 1998) were the most
frequent factors controlling this tuna fluctuations. But, when carrying out the bootstrap
test, GHG was the most influencing parameter on BFT dynamics, followed by the proxy
SST and the modelled SST for the western part of the domain (Gibraltar Strait) (Table
4).
When analysing the deviance (Table 4), climatic and temporal terms (third
degree polynomial) for all the almadrabas, it can be seen that the temporal factor
(autocorrelation) explains an slightly more than the climatic variables (16.85% and
13.85%, respectively). However, both terms together explained the 35.12% of tuna
captures variability at the Gibraltar Strait-Western Mediterranean almadrabas.
DISCUSSION
Almadrabas catches time-series since 1525 to 1969 were analysed against
temporal and environmental parameters in order to detect their influence on the
observed fluctuations and global decreasing trend in the captures of BFT. It is important
to consider each almadraba from an individual point of view. As it can be seen in our
results (Figs. 2, and 3.1 to 3.3; Table 4) some of them reflect a similar behaviour, but all
together have a meaning in the shade. The most eye-catching cases are those of Porto
Paglia and Porto Scuso (Southwest of Sardinia; Fig. 3.2). Here it can be seen that a
high-frequency processes must be occurred among the temporal and global models,
since it is not possible to differentiate them (lines superposed). In addition, climatic
terms could not be included in the models (Table 4). Even though, the trend of the
climatic model (model 2. Fig. 3.2) looks like the captures series, bringing up the idea of
the influence of other climatic phenomenon, of larger frequency than those taken into
account, on BFT dynamics.
From a global point of view, it can be seen that the solar activity influence
somehow the tuna captures in the Gibraltar Strait-Western Mediterranean Sea region.
The Spörer (1460-1550), Maunder (1645-1715) and Dalton (1790-1820) Minimums,
even the latest minimum of the Gleissberg cycle (Gleissberg, 1958) which took place
between 1895 and 1930 (Landscheidt, 2003), can be detected in different almadraba
series (Fig. 3.1. to 3.3) as a reduction in catches. Even the extremely cold period
occurred in West Europe from 1560 to 1600 (Fagan, 2000) can be detected in the
capture series of the almadrabas located off the Gulf of Cadiz (Fig. 3.1). After those
intervals of cool climate, an increase (coinciding with warmer periods) was always
observed; as happens with the solar activity events associated with several Gleissberg
Maxima occurred after 1600 (Landscheidt, 2003). However, no significant correlation,
after the bootstrap validation, has been observed with none of the solar indices
considered by us (Solar Irrad., Solar Irrad. Bckrd. and Sun Spots). This may be due to
the fact that other solar parameters, might affect the Earth climate more than the
irradiance itself; for instance, the eruptions of the Sun or the solar wind strength
(Landscheidt, 2003). Previous works demonstrated that the flux of energy that the Sun
transfers to the atmosphere by charging particles, cause local warming and produce
changes in the circulation patterns, influencing the troposphere, the temperature, air
pressure, etc (Haigh, 1996; Balachandran et al., 1999; Shindell et al., 1999).
Furthermore, Egorova et al. (2000) and Neff et al. (2001) pointed out that there is a
tight positive connection between solar eruptions, temperature and rainfall, and a
negative relation with the air pressure. This is in accordance with the significant
correlations we have obtained with SST, mostly with the proxy one (Mann SST). This
may be because of the direct effect of the sun on temperature, and this last on tuna
metabolism and behaviour (Hazel, 1993; Korsmeyer and Dewar, 2001). According to
other authors (Fromentin et al., 2000; Fromentin, 2003; Ravier and Fromentin, 2001,
2004; Ganzedo et al., 2009), the periods of low temperature (e.g.: Maunder Minimum)
were related to low productive periods for the almadrabas fisheries off the Gibraltar
Strait, Sicily, Sardinia and North Africa.
It must also be highlighted that when adding the temporal term to the climatic
one in the global analysis, some of the climate parameters disappears from it. This must
be due to the fact that it is no possible to distinguish if the influence on captures of those
variables was because of a feedback process or the climate variability itself.
Nevertheless, the joining of environmental (i.e.: the 1895 solar minimum) and human
factors (i.e.: the Industrial Revolution), may be the predominant causes of the
decreasing tendency of the BFT captures observed in the almadraba fisheries in the
Gibraltar Strait-Western Mediterranean region since the late 1890s. Even the stock seem
to recover according to the records of Barril and Medo das Casas trapnets, but the
recruitment rapidly dropped to minimum values by around 50 years after that
maximum. In addition to this, the volcanic activity reached a maximum from 1942 to
1952, since 1860 (Simkin et al., 1981), and it is well known that the volcanic eruptions
contribute to cool the weather (Fagan, 2000). This was also observed in our results
through a significantly negative correlation between volcanic dust and temperature.
That is, immediately after a period of high volcanic activity is high, the weather became
cooler, not favouring the recovering of tuna populations.
Modelled SST (Zorita et al., 2005) explains the high frequency variability
phenomenos observed in the almadraba BFT captures, but it was only significantly
positive with those trapnet fisheries with largest records, as Zahara and Conil (0.163 and
0.174, respectively. Table 4). For this reason, with the aim of considering the SST effect
in a shorter time-scale, proxy SST data were also used (Mann et al., 1998 and 1999).
However, when significant correlation was corroborated with the bootstrap, it was
negative (i.e. Medo das Casas. Table 4). This was in accordance with Ravier and
Fromentin (2004) observation, whose suggested that long-term fluctuations were related
to the inverse of the temperature in the almadrabas domain. Temperature is a key
parameter in tuna metabolism, migrations, reproductive behaviour, larval survival and
food availability, with direct consequences on recruitment (Pepin, 1991; Polovina,
1996; Chambers et al., 2001). And, in this way, GHG are also temperature controlling
factors. However, in all the cases, where significant correlation between this last
parameter and tuna captures were obtained, it was negative (Table 4).This could be due
to BFT has a favourable range of temperature, being equally adverse very warm or very
cold waters. For example, spaw occurs when SST is around 24-25.5 ºC (Alemany et al,
2002; Rooker et al., 2003).
Our results indicate that, for the 11 almadrabas considered (mean values), the
fluctuations are due to the temporal factor in a 16.85%, and 13.85% because of climate.
The rest up to the 100% might be other climatic and socio-political parameters (wars,
famines, etc.) not considered in this evaluation. The temporal term is the autocorrelation
of the own population dynamics, in the way that what happens one year in the stock
influences the consecutive years. However, from the shared explained deviance
(35.12%) is not possible to distinguish if there are external forcing controlling the
considered parameters.
Social, political and economical factors are assumed to be of local and shorttime impact on the almadrabas exploitation. For this reason, it can be considered that
climatic factors are controlling long-term trend in the BFT captures in the Gibraltar
Strait-Western Mediterranean domain. However, for the periods when both factors
(environmental and anthropogenic) work in synergy, the effects on the tuna population
were maximised. It is difficult to isolate any of the evaluated term when analysing the
BFT almadrabas catches fluctuations, but long-term climate processes seems to play a
key role on this species. To sum up, it must be highlighted the influence of coldest
periods (Gleissberg minimum), which affect negatively BFT larval survival and
recruitment. GHG also play a key role on this population due to the effect that they have
on temperature and solar irradiation through the troposphere.
ANKNOWLEDGMENT
A. M. Caballero-Alfonso has a fellowship from the Spanish Ministry of Science
and Innovation (MICINN). The EKLIMAXXI project (Basque Government,
Department of Industry and Basque Meteorological Service-Euskalmet, Project
ETORTEK07/01 – IE07-190) is acknowledged for funding the position of U. Ganzedo.
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FIGURES AND TABLES
Figure 1. Almadraba’s geographical locations. The soft-grey dot lines is the Zorita’s
model grid.
Figure 2. Hierarchical clustering almadrabas analysis.
Figure 3.1. Catches data per almadraba and predicted values from the GLM models
(temporal, environmental and global).
Figure 3.2. Catches data per almadraba and predicted values from the GLM models
(temporal, environmental and global).
Figure 3.3. Catches data per almadraba and predicted values from the GLM models
(temporal, environmental and global).
Table 1. Almadraba data series reference relation.
Country
Almadraba
Period
Reference
Portugal
Barril
1867-1966
Ravier, 2003; Lemos & Gomes, 2004
Medo das Casas (1)
1776-1808
Ravier, 2003
Medo das Casas (2)
1861-1969
Zahara (1)
1525-1756
Zahara (2)
1910-1936
Conil
1525-1756
López-Capont, 1997
Saline (1)
1823-1843
Ravier, 2003
Saline (2)
1864-1960
Isola Piana
1820-1960
Ravier, 2003
Porto Scuso
1820-1960
Ravier, 2003
Porto Paglia
1830-1960
Ravier, 2003
Bonagia (1)
1603-1806
Ravier, 2003
Bonagia (2)
1870-1949
Formica (1)
1602-1816
Formica (2)
1839-1855
Formica (3)
1876-1959
San Giuliano (1)
1601-1804
San Giuliano (2)
1885-1914
Spain
Italy (Sardinia)
Italy (Sicily)
López-Capont, 1997; Ravier, 2003
Ravier, 2003
Ravier, 2003
Table 2. Temporal GLM (model 1).
Almadraba
Temporal
term
p-value
Barril
T3
5.67·10-5
M. Casas (1)
T3
0.19311
M. Casas (2)
3
T
0.01167
Zahara (1)
T3
3.06·10-14
Zahara (2)
T3
0.0180
Conil
T3
<2·10-16
Saline (1)
T3
0.00160
Saline (2)
2
0.0218
Isola Piana
T
3
4.99·10-13
Porto Scuso
T2
3.16·10-14
Porto Paglia
T3
9.86·10-8
Bonagia (1)
T3
1.30·10-5
Bonagia (2)
T3
0.00386
Formica (1)
3
T
4.15·10-5
Formica (2)
T3
0.162
Formica (3)
3
T
1.50·10-5
San Giuliano (1)
T3
1.26·10-9
San Giuliano (2)
T2
0.0274
T
Table 3. Climate GLM and correlation analyses (model 2).
Almadraba
Barril
Climate
term
W. Rad.
W. SST
M. Casas (1)
Climate
term
p-value
Bootstrap
Almadraba
Climate
term
p-value
Bootstrap
-0.30
W. Rad.
1.60·10-3
Bonagia (1 cont)
GHG
3.2·10-13
-4
E. SST
0.05
Bonagia (2)
Solar Irrad.
0.01
-4
Solar I. Bck.
1.01·10-3
W. Precip.
2.48·10-3
E. SST
0.03
0.05
Zahara (2 cont)
7.57·10
W. SST
0.01
Sun Spot
1.88·10-5
GHG
0.02
GHG
2.3·10-15
W. Rad.
2.32·10-7
-3
Conil
Formica (1)
W. SST
0.02
W. SST
2.86·10
M. SST
1.68·10-3
M. SST
0.03
GHG
<2·10-16
GHG
3.02·10-7
0.01
E. Precip.
0.04
E. Rad.
M. SST
Saline (1)
Saline (2)
3.54·10
0.01
E. Rad.
8.5·10-10
0.01
W. Rad.
0.01
-6
1.25·10
4.70·10
-0.40
GHG
1.68·10-6
-0.42
Isola Piana
Sun Spot
0.01
E. Precip.
0.01
W. Precip.
0.01
S. Giulia. (2)
M. SST
4.18·10
-3
Solar I. Bck.
0.02
2.82·10
Formica (3)
S. Giulia. (1)
-3
4.53·10
Formica (2)
-3
5.72·10
Solar I. Bck.
W. Rad.
E. Precip.
-3
E. Precip.
-8
W. SST
Zahara (2)
Almadraba
3.26·10
Solar Irrad.
Zahara (1)
Bootstrap
Solar I. Bck.
Solar Irrad.
M. Casas (2)
p-value
-4
-0.32
-5
-0.25
-3
Solar I. Bck.
6.69·10
Sun Spot
6.83·10-3
M. SST
8.1·10-3
GHG
1.92·10-5
-3
M. SST
7.01·10
GHG
7.96·10-3
W. Precip.
0.04
E. Rad.
0.01
W. Rad.
0.011
M. SST
-3
3.86·10
Porto Scuso
M. SST
2.6·10
W. SST
6.98·10-4
GHG
6.3·10-16
Porto Paglia
GHG
2.67·10-8
M. SST
1.45·10-3
E. Precip.
0.03
Bonagia (1)
E Precip.
3.04·10-3
Solar Irrad.
2.31·10-3
E. Rad.
4.59·10-3
W. SST
0.02
-0.35
Table 4. GLM and correlation analyses (model 3).
Almadraba
Barril
Variab.
Climate
Temp.
(years)
Term
Sol. I. Bck.
Sun Spot
GHG
p-value
3.97·10-9
2.88·10-9
4.92·10-3
-8
T
3.99·10
T2
T3
4.40·10-8
4.87·10-8
Shared
Unexpl.
M. Casas
(1)
Climate
Temp.
(years)
Climate
Temp.
(years)
Zahara (1)
Shared
Unexpl.
Climate
Bootstrap
M. SST
0.020
Solar Irr.
5.08·10-3
T
0.06
T2
T3
0.06
0.06
12.38
41.15
0.01
M. SST
Sol. I. Bck.
0.02
0.01
T
Zahara (2)
Shared
Unexpl.
Climate
-0.35
Temp.
(years)
1.49
Conil
-0.36
-0.40
-3
2.53·10
T
2.95·10
T
3
3.44·10-3
Shared
Unexpl.
Climate
14.34
Tempor
al
(years)
-3
2.17·10-4
7.11·10-3
0.01
Term
p-value
GHG
1.89·10-5
T
2.05·10-3
2
-3
2.4·10-3
0.04
1.82·10-4
T
0.03
2
0.03
0.03
Shared
Unexpl.
50.57
28.86
5.72
0.16
W. Rad.
W. SST
GHG
T
Dev. (%)
Bootstrap
18.88
2.79·10
3.78·10-3
E. Rad.
W. Rad.
W. SST
T
T3
6.22
2
W. Rad.
W. SST
M. SST
Variab.
T
T3
7.42
52.91
E. Rad.
Almadraba
Zahara (1
cont)
Temp.
(years)
54.70
17.47
Shared
Unexpl.
M. Casas
(2)
Dev. (%)
15.44
3.87·10-9
6.11·10-5
7.17·10-8
51.08
24.31
18.78
0.47
2.65
72.05
11.83
3.86
0.17
21.12
T2
T3
54.40
20.61
Almadraba
Variab.
Term
p-value
Dev. (%)
Bootstrap
Saline (1)
Climate
E. Precip.
7.23·10-3
19.41
-0.69
Temp.
(years)
T
5.32·10-3
6.37
T2
T3
5.32·10-3
5.32·10-3
Saline (2)
Shared
Unexpl.
Climate
Temp.
(years)
E. Precip.
E. Rad.
W. Rad.
Sun Spot
GHG
0.01
2.67·10-9
0.01
8.91·10-4
9.45·10-6
T
0.08
2
0.06
T
Isola Piana
Shared
Unexpl.
Climate
Temp.
(years)
Porto Scuso
Shared
Unexpl.
Climate
Temp.
(years)
Bonagia (1)
-0.27
0.03
1.75·10-6
T
2.66·10-3
T2
T3
2.68·10-3
2.75·10-35
52.58
22.63
11.62
27.46
Bonagia (2)
0.21
-0.73
Formica (1)
13.84
47.09
0
T
2
-14
3.40·10
Variab.
Climate
Temp.
(years)
Shared
Unexpl.
Climate
Temp.
(years)
-0.27
2.79
W. Precip.
GHG
T
Shared
Unexpl.
56.24
30.72
22.00
Almadraba
Porto Paglia
Shared
Unexpl.
Climate
Temp.
(years)
Shared
Unexpl.
Climate
Temp.
(years)
27.54
-14
3.16·10
18.25
54.21
Formica (2)
Shared
Unexpl.
Climate
Term
p-value
Dev. (%)
0
T
7.71·10-8
20.18
T2
8.70·10-8
T3
9.86·10-8
E. Precip.
GHG
2
9.08·10-3
7.24·10-12
-11
T
1.42·10
T3
1.07·10-11
Bootstrap
28.55
51.27
16.93
-0.30
14.58
13.50
54.99
3.01
T
1.186·10-3
T2
T3
1.21·10-3
1.24·10-3
W. Precip.
E. SST
M. SST
GHG
6.18·10-4
0.03
7.52·10-5
5.27·10-8
-5
T
1.26·10
T2
T3
1.24·10-5
1.22·10-5
12.20
15.69
69.09
22.27
-0.25
8.97
0.455
69.212
12.57
Almadraba
Formica (2
cont)
Formica (3)
Variab.
Temp.
(years)
Shared
Unexpl.
Climate
Temp.
(years)
S. Giul. (1)
Shared
Unexpl.
Climate
Temp.
(years)
Term
p-value
GHG
0.131
65.77
75.33
1.25
T
2.22·10-4
7.27
T2
T3
2.24·10-4
2.26·10-4
M. SST
GHG
T
2
0.03
1.25·10-4
-5
9.32·10
Temp.
(years)
Shared
Unexpl.
-0.45
38.44
1.01·10
1.1·10-4
9.57
45.34
E. Rad.
0.02
W. SST
M. SST
Sol. Irrad.
8.97·10-4
3.05·10-3
0.01
T
0.01
T2
0.01
-0.35
51.73
39.76
6.64
-4
Shared
Unexpl.
Climate
Bootstrap
53.67
T
T3
San
Giuliano (2)
Dev. (%)
42.35
12.98
15.80
28.87