Supporting Information
Reynolds et al (2015)
SI Species ecology and study sites
Study sites. The Scopoli’s shearwater (Sh), the Cory’s shearwater (Cs) and Cape Verde shearwater (CVs)
are three closely related species belonging to the Cory’s shearwater complex (Sangster et al. 2012; see also
Genovart et al. 2013 for a discussion of its taxonomic status). They are pelagic seabirds, which breed in the
Mediterranean, North Atlantic Ocean, and Central Atlantic Ocean, respectively. All three species spend the
nonbreeding season in the Central and Southern Atlantic Ocean (González-Sólis et al. 2007; Muller et al.
2014), with a few birds from Berlenga also overwintering in the north-west Atlantic Ocean (Catry et al.
2011, Dias et al. 2012). They generally feed on pelagic fish species, cephalopods and crustaceans (Paiva et
al. 2010a; Xavier et al. 2011; Alonso et al. 2014). In the Mediterranean, the Sh also seems widely to exploit
discards from fishing boats (Cecere et al. 2014a). The three species nest in the ground in burrows or in rock
crevices. During incubation, the adults spend several days incubating the egg until their partner, which is
foraging at sea, comes back for the changeover. Foraging trips last on average 7.53 ± 0.7 days in Sh (Cecere
et al. 2013), 9.20 ± 6.80 days in Cs (Paiva et al. 2010b) and 9.90 ± 2.13 days in CVs (V.H. Paiva
unpublished data). A few days after hatching, adults leave the chick alone and return for a daily feed during
the night (Rubolini et al. 2014). In most studied colonies parents perform the dual foraging strategy
alternating short trips, which last about 1-4 days and are mostly for chick provisioning, with longer trips for
self-provisioning (Paiva et al. 2010d; Cecere et al. 2014b).
Fieldwork on Sh was carried out during both the incubation and chick-rearing periods (Table S1) at three
Mediterranean colonies: one on Linosa island, one on the islet of Capraia in the Tremiti Archipelago, and
one on La Maddalena Archipelago (Fig. S1). Linosa (35°52’N; 12°52’E), located in the Tunisian
Plateau/Gulf of Sidra region between Sicily, Tunisia and Libya, hosts the second largest colony of Sh with an
estimated 10,000 breeding pairs (Baccetti et al. 2009). The Tremiti Archipelago (42°08’N; 15°31’E) hosts
approximately 400 pairs (Baccetti et al. 2009), and it represents the largest breeding colony of the species in
the Adriatic Sea. La Maddalena Archipelago (41°13’N; 9°24’E), located in the Tyrrhenian Sea between
1
Sardinia and Corsica, hosts 400-1,000 pairs (Baccetti et al. 2009); fieldwork was carried out on the islets of
Spargiotto (120-180 pairs) and Barettini (50-100 pairs). Birds from the three colonies inhabit different
seascapes: compared to the relatively shallow and flat Adriatic sea, both the Tyrrhenian Sea and the Tunisian
Plateau present much lower primary production (Bosc et al. 2004), deeper waters and higher mean sea
surface temperature (Coll et al. 2010).
Cs birds were tracked during the chick-rearing period (Table S1) at three north Atlantic islands: Corvo,
Berlenga and Selvagem Grande (Fig. S1). Corvo (39°40’N; 31°06’W), located at the oriental group of the
Azores Archipelago, hosts roughly 30,000 individuals (Ramírez et al. 2008). Berlenga (39°24’N; 9°30’W),
the largest island of the Berlengas Archipelago, holds 300 breeding pairs (~1,100 breeding pairs for the
whole archipelago; Lecoq et al. 2011). Selvagem Grande (30°08’N; 15°51’W), the largest island of the
Selvagens Archipelago, hosts the largest population of Cs in the Atlantic, with ~ 30,000 breeding pairs
(Granadeiro et al. 2006). At Corvo island, the birds breed within an oceanic system, i.e. oceanic islands with
a short continental shelf. On Berlengas, birds breed within a neritic system, i.e. an island within a long
continental shelf of about 200 m depth. Selvagem Grande, surrounded by an oceanic environment, is situated
within 375 km of a large neritic system, the African continental shelf (Fig. S1). Surroundings of the former
oceanic colonies are usually less productive than the nearby area of the neritic colony, in which the shallow
waters and the strong upwelling phenomenon fuels productivity to the system and naturally enhances prey
availability for seabirds (Paiva et al. 2010b). Cs from the three former colonies typically alternate between
short foraging trips exploiting its colony surroundings (Paiva et al. 2010c) and long excursions to the north
of Azores (Corvo population; Ceia et al. 2014a), seamounts offshore mainland Portugal (Berlenga
population; Paiva et al. 2013; Ceia et al. 2014b) and the north African shelf (Selvagem Grande population;
Paiva et al. 2010d)
Fieldwork on CVs was carried out during both incubation and chick-rearing periods at Raso islet (16°36’N;
24°35’W), the southernmost islet between São Vicente and São Nicolau islands. This place holds nearly
10,000 breeding pairs of CVs (Biosfera I unpublished data), which should be the largest population of this
near threatened species from the Cape Verde Archipelago. The colony surroundings are generally low in
productivity, but the species exploits frequently the turbulent and productive waters on the “channels”
2
between Cape Verde islands and perform longer trips to exploit the upwelling area at the shelf break off the
western African coast (V. H. Paiva unpublished data).
Alonso, H., Granadeiro, J.P., Waap, S., Xavier, J., Symondson, W.O.C., Ramos, J.A., Catry, P. (2014) An
holistic ecological analysis of the diet of Cory’s shearwaters using prey morphological characters and
DNA barcoding. Molecular Ecology 23:3719-3733. doi: 10.1111/mec.12785.
Baccetti N, Capizzi D, Corbi F, Massa B, Nissardi S, Spano G, Sposimo P (2009) Breeding shearwaters on
Italian islands: population size, island selection and co-existence with their main alien predator, the black
rat. Riv. Ital. Orn. 78: 83-100.
Bosc, E., Bricaud, A., Antoine, D. (2004) Seasonal and interannual variability in algal biomass and primary
production in the Mediterranean Sea, as derived from 4 years of SeaWiFS observations. Glob.
Biogeochem. Cycles. 18: GB1005.
Catry P, Dias MP, Phillips RA, Granadeiro JP (2011) Different Means to the Same End: Long-Distance
Migrant Seabirds from Two Colonies Differ in Behaviour, Despite Common Wintering Grounds. PLoS
ONE 6(10): e26079.
Cecere, J.G., Catoni, C., Maggini, I., Imperio, S.; Gaibani, G. (2013) Movement patterns and habitat use
during incubation and chick-rearing of Cory’s shearwaters (Calonectris diomedea diomedea) (Aves:
Vertebrata) from Central Mediterranean: influence of seascape and breeding stage. It. J. Zool. 80, 82-89.
Cecere JG, Catoni C, Gaibani G, Geraldes P, Celada C, Imperio S. (2014a) Anthropogenic and
environmental variables affecting foraging locations of Scopoli’s shearwater in the Mediterranean sea.
Ibis.
Cecere, J.G., Gaibani, G. , Imperio, S. 2014b: Effects of environmental variability and offspring growth on
the movement ecology of breeding Scopoli’s shearwater Calonectris diomedea. Curr. Zool. 60: 622-630.
Ceia, F.R., Paiva, V.H., Ceia, R.S., Hervías, S., Garthe, S., Marques, J.C., Ramos, J.A. (2014a) Spatial
foraging segregation by close neighbours of a highly mobile seabird species. Oecologia. DOI:
10.1007/s00442-014-3109-1
3
Ceia, F.R., Paiva, V.H., Garthe, S., Marques, J.C., Ramos, J.A. (2014b) Can variations in the spatial
distribution at sea and isotopic niche width be associated with consistency in the isotopic niche of a
pelagic seabird species? Marine Biology. DOI: 10.1007/s00227-014-2468-9
Coll, M., Piroddi, C., Steenbeek, J., Kaschner, K., Ben Rais Lasram, F. et al. (2010) The Biodiversity of the
Mediterranean Sea: Estimates, Patterns, and Threats. PLoS ONE 5(8): e11842.
Dias, M. P., Granadeiro, J.P., Catry, P. (2012) Working the day or the night shift? Foraging schedules of
Cory’s shearwaters vary according to marine habitat. Mar. Ecol. Prog. Ser. 467: 245-252.
Genovart, M., Thibault, J.-C., Igual, J.M., Bauzà-Ribot, M.M., Rabouam, C. & Bretagnolle, V. (2013)
Population Structure and Dispersal Patterns within and between Atlantic and Mediterranean Populations
of a Large-Range Pelagic Seabird. PLoS ONE 8, e70711.
González-Solís, J., Croxall, J.P., Oro, D., Ruiz, X. (2007) Trans-equatorial migration and mixing in the
wintering areas of a pelagic seabird. Frontiers in Ecology and the Environment 5: 297-301.
Granadeiro J. P., Dias M. P., Rebelo R., Santos C. D, Catry P. (2006) Numbers and population trends of
Cory’s Shearwater Calonectris diomedea at Selvagem Grande, Northeast Atlantic. Waterbirds 29(1):5660.
Muller MS, Massa B, Phillips RA, Dell’Omo G. (2014) Individual consistency and sex differences in
migration strategies of Scopoli shearwaters despite year differences. Current Zoology 60: 631-641.
Paiva, V.H., Xavier, J., Geraldes, P., Ramirez, I., Meirinho, A., Ramos, J.A. & Garthe, S. (2010a). Foraging
ecology of Cory’s shearwaters in different ecological environments of the North Atlantic. Marine
Ecology Progress Series 410: 257-268. DOI:10.3354/meps08617
Paiva VH, Geraldes P, Ramírez I, Meirinho A, Garthe S, Ramos JA (2010b). Foraging plasticity in a pelagic
seabird species along a marine productivity gradient. Marine Ecology Progress Series, 398, 259-274.
Paiva, V.H., Geraldes, P., Ramirez, I., Ramos, J.A., Garthe, S. (2010c). How Area Restricted search of a
pelagic seabird changes while performing a dual foraging strategy. Oikos 119: 1423-1434. DOI:
10.1111/j.1600‐0706.2010.18294.
Paiva, V.H., Geraldes, P., Ramirez, I., Meirinho, A., Ramos, J.A., Garthe, S. (2010d). Oceanographic
characteristics of areas used by Cory’s shearwaters during short and long foraging trips in the North
Atlantic. Marine Biology 157: 1385-1399.
4
Paiva, V.H., Geraldes, P., Marques, V., Rodríguez, R., Garthe, S., Ramos, J.A. (2013). Effects of
environmental variability on different trophic levels of the North Atlantic food web. Marine Ecology
Progress Series 477: 15-28.
Ramírez, I., Geraldes, P., Meirinho, A., Amorim, P., Paiva, V.H. (eds.) (2008) Important Areas for Seabirds
in Portugal. Project LIFE04NAT/PT/000213 - Sociedade Portuguesa Para o Estudo das Aves. Lisboa.
http://lifeibasmarinhas.spea.pt/pt/y-book/ibasmarinhas/
Rubolini D, Maggini I, Ambrosini R, Imperio S, Paiva VH, Gaibani G, Saino N, Cecere JG. (2014) The
Effect of Moonlight on Scopoli’s Shearwater Calonectris diomedea Colony Attendance Patterns and
Nocturnal Foraging: A Test of the Foraging Efficiency Hypothesis. DOI: 10.1111/eth.12338.
Sangster, G., Collinson, M., Crochet, P.A., Knox, A.G., Parkin, D.T., Votier, S.C. (2012) Taxonomic
recommendation for British birds: eighth report. Ibis 154, 874-883.
Xavier, J.C., Magalhães, M.C., Mendonça, A.S., Antunes, M., Carvalho, N., Machete, M., Santos, R.S.,
Paiva, V.H., Hamer, K.C. (2011) Changes in diet of Cory’s Shearwaters Calonectris diomedea breeding
in the Azores . Marine Ornithology 39: 129-134.
5
Table S1. Sample sizes used to characterize the foraging behaviour of three species of Calonectris sp.
shearwaters, breeding on 7 different colonies and foraging over the Mediterranean Sea and North and Central
Atlantic Ocean.
SPECIES
Scopoli's shearwaters (Sh)
Cory's shearwaters (Cs)
Cape Verde shearwaters (CVs)
COLONY
PERIOD
N° TRACKED
BIRDS
YEAR
Linosa
chick-rearing
55
2008-2012
Linosa
incubation
14
2008
Tremiti
incubation
26
2009-2010
La Maddalena
chick-rearing
32
2013
Corvo
chick-rearing
20
2007
Berlenga
chick-rearing
29
2010
Selvagem
Grande
chick-rearing
16
2007
Raso
incubation
11
2013
Raso
chick-rearing
7
2013
6
Figure S1. Illustrative trips from Cory’s shearwaters (Cs) tracked on (1) Corvo, (2) Berlenga and (6) Selvagem
Grande islands; Scopoli’s shearwaters (Sh) tracked on (3) La Maddalena, (4) Tremiti and (5) Linosa islands and
Cape Verde shearwaters (CVs) tracked on (7) Raso islet. Different colours represent different individuals
(Bird_ID) and the black star their breeding colonies location.
7
SI Level-crossing statistics of concentration fluctuations in odour plumes dispersing in turbulent flows.
Here, using standard methods (Risken 1996), it is shown that the distribution of durations,  , during which
concentrations remain continually above some threshold, detectable to the birds, exhibits power-law scaling,
p   3 / 2 with an exponential truncation that is dependent upon the local atmospheric conditions and is
expected to increase with increasing atmospheric stability, i.e., with decreasing atmospheric turbulence (Fig.
S2).
The odour concentrations in turbulent flows are best represented by a clipped-normal distribution (Lewellen &
Sykes 1986). However, an exponential distribution is only slightly conservative and has the advantage of
simplicity (Sawford 1987) which facilitates mathematically analysis (Sawford, 1987) It is for this reason that the
analysis presented here is based on the exponential probability distribution of concentrations, c ,
pc c   exp  c / C  / C
(1)
where C is the mean concentration over time. When spatial variations of C are sufficiently small, the
exponential distribution (1) is a stationary solution of the Fokker-Planck equation,
p C p C 2  2 p


t T c T c 2
(2)
where T is the autocorrelation timescale, i.e., the durations over which concentrations remain significantly
correlated (Thomson, 1987).
A general, time-dependent, solution to (2) corresponding to the initial condition
pc c    c  c0 
is

 2 C 2 t 
d
pc c   exp  c / C  exp  t / 4T  sin  c  c sin  c0  c exp  
T


0
8
(3)
where λs are eigenvalues of Eqn. (2). The distribution (3) vanishes at c  c , where c is the threshold
concentration above which birds may detect the odour. This constraint amounts to the imposition of an
absorbing boundary. Once a concentration, c , falls below c it is ‘absorbed’ and does not rise above c at some
*
later time. This facilitates the determination of the distribution, p , of durations  , during which concentrations
*
remain continually above the threshold concentration. This is because p is just the flux of concentrations into
the absorbing boundary,
p *    
C 2 p
T c
c c

 2 C 2
C2

exp  c / 2C  exp   / 4T   sin  c0  c exp  
T
T

0


d

1
T 1 / 2  3 / 2 exp  c / 2C exp   / 4T c0  c exp   T2 c  c0 2 
4C
 4C 

(4)
An analogous result can be found for the distribution of durations during which concentrations remain
continually above some threshold concentration. It is noteworthy that the long-time,  3 / 2 , asymptotic
behaviour is a manifestation of the Sparre Andersen Theorem (Sparre Andersen 1953, 1954) and so generic
rather than model specific.The model is displayed in Fig. S2. We compute the time when odour concentration is
continuously above the detection threshold c . According to our theory 1
𝑇(𝑐𝜏 − 𝑐0 )2⁄
4𝐶 2
= 1 / 4T
and  2 =
.
Furthermore, our analytic result is consistent with the observations of Yee et al., (1994a, b) who made highspeed recordings of odour concentrations within the atmospheric boundary-layer and found  3 / 2 scaling
behavior over about 3 orders of magnitude. Yee et al. (1994a,b) reported on concentration fluctuations and
scales in a dispersing plume in the atmospheric surface layer. They found clear evidence for power-law scaling
on time intervals, τ , from 0.1 s up to and including the longest recorded intervals which lasted several 100 s.,
i.e. comparable to the duration of the observations (600 sec).These experiments do not preclude the possibility
9
that the  3 / 2 scaling extends too much longer time intervals that were not captured by the measurements.
Power-law scaling at much longer time intervals is expected to be more in evidence above the ground where the
spatial-temporal scales of turbulence are larger. We do not imagine that shearwaters respond to the fastest
turbulent fluctuations in odour concentration but instead expect them to react to the more prolonged
fluctuations.
When the odours are present above the threshold of detection then the odour map is present and the birds
‘know’ to keeping flying forward. But when the odour concentration fall below this level the birds are without
their map and so effectively lost. They may change course either because they become disoriented or because
they are attempting to re-establish contact with the odour map. A flight-segment length distribution with an
exponentially-truncated -3/2 power-law tail is therefore predicted to be hallmark of olfactory-cued navigation.
Our predictions apply equally well to pure odours and blends having several components because the ratio
among constituents of a blend is largely preserved as the mixture is carried by a turbulent airstream (Duplat et
al. 2010). The ratio of constituent components of an odour blend will change only gradually over time with the
rate limiting process being driven by differences in the molecular diffusivities of the components.
Duplat, J., Innocenti C., Villermaux E. (2010) A nonsequential turbulent mixing process, Physics of Fluids 22
article 035104.
Lewellen W.S. & Sykes A.I. (1986) Analysis of concentration fluctuations from Lidar observations of
atmospheric plumes. J Climate Appl Meteorol 25, 1145-1150.
Risken H. (1996) The Fokker-Planck Equation: Methods of Solutions and Applications (Springer Series in
Synergetics) ISBN-13 978-3540615309 Springer.
Sawford B.L. (1987) Conditional concentration statistics for surface plumes in the atmospheric boundary-layer,
Boundary-Layer Meteorology 38, 209-223.
Sparre Andersen E. (1953) On the fluctuations of sums of random variables, Mathematica Scandinavica 1, 263285.
Sparre Andersen E. (1954) On the fluctuations of sums of random variables II, Mathematica Scandinavica 2,
195-223.
10
Thomson D.J. (1987) Criteria for the selection of stochastic models of particle trajectories in turbulent flows,
Journal of Fluid Mechanics 180, 529-556
Yee E., Chan R., Kosteniuk P.R., Chandler G.M., Biltoft C.A., Bowers J.F. (1994a) Experimental measurements
of concentration fluctuations and scales in a dispersing plume in the atmospheric surface layer obtained using
a very fast response concentration detector, J. Applied Meteorology 33, 996-1016,.
Yee E., Chan R., Kosteniuk P.R., Chandler G.M., Biltoft C.A. & Bowers J.F. (1994b) Measurements of levelcrossing statistics of concentration fluctuations in plumes dispersing in the atmospheric surface layer’,
Boundary-Layer Meteorology 73, 53-90
11
Figure S2. Graphic representation of the model of time series of odour concentration. The blue line represents
the fluctuation in the odour concentration in the environment. The dotted line represents the critical
concentration detectable to birds and the green line represents the mean concentration over time. We aimed to
find the distribution of times, , when odour concentration is above the detection threshold.
12
SI Synthetic flights
Synthetic flight lengths were drawn at random from an exponentially-truncated power-law distribution, the
hallmark distribution of an olfactory-cued navigation:
p l   N1l   exp  1l  exp  2 / l 
where pl  is the probability of a move of length l,   3 / 2
is the power-law (Lévy) exponent,1 and2
describe the truncation and N1 is a normalization factor.
To evaluate the statistical power of our method (which is expected to increase with trip length, because of a
larger sample of displacements) we computed the probability of detecting the true model (ETPL) in synthetic
flights as a function of trip length and overlap them to the results obtained with a sample of 210 shearwaters, in
Fig. S3. It appears evident that the sample size plays a sharp role in determining the possibility of correctly
detecting an ETPL model in data. Accordingly, almost 80% of birds with a trip length of 1,000 km appear to
perform ETPL. Note that the statistical power function derived by the synthetic trajectories overlaps to what it is
observed for actual birds, in the studied range of trip length. This suggest that most of actual birds whose
movement pattern was biexponential or power law, were misidentified due to a small number of recorded
displacements, also reported in Table S2.
For experimental data (Table S2) we tested whether it exists an interaction between colony and trip length
which might have an effect in detecting ETPL, but this hypothesis was less supported (AIC=228.9) than the
only effect of trip length (AIC=226.4). The effect of trip length on the probability of performing ETPL
movements is highly significant (2 = 33.43, P<0.0001), while the effect of colony is not (2 = 5.37, P=0.50).
13
Figure S3. Synthetic flights: red dotted line represents the proportion of synthetic odour-cued flights that are
misidentified as being bi-modal flights by the maximum likelihood method, because the Akaike weights for an
odour-cued flight are <0.5. Actual data: the blue continuous line represents the probability of performing an
ETPL movement pattern as a function of trip length. Light blue band represents 95% confidence interval.
14
SI Individual flight characteristics
The main statistical features of our samples are reported in Table S2. It shows that 70% of birds show an ETPL
movement model, 20% exhibit a PL model and 9.5% a biexponential pattern. No bird exhibits an exponential
movement pattern. For the ETPL model we report also the results of a goodness of fit test which is not
significant (at P=0.05 level) for 75 birds and is weakly significant (0.05<P≤0.01) for 72 birds, overall indicating
a quite good fit of the ETPL model.
In Fig. S4 we report some examples of model fitting at the individual level, selecting at random one animal per
colony. It appears clear that the fitting is quite good with a nice overlap between theoretical predictions and
actual data.
15
Table S2. For each bird (ring) we report the year of study, the breeding colony, the period (CHI=chick rearing,
INC=incubation), the
best-fit model (ETPL = exponentially truncated power law, PL=power law,
BIEXP=biexponential and EXP=exponential), the ETPL Akaike weight (AW), duration of the trip (Dur), total
flight length, number of displacements (Displ) and the probability of the Kolmogorv-Smirnoff goodness-of-fit test
(KS). is reported with 95% confidence interval.
Obs Year
Colony
Ring
Period
Dur Length
Displ Model AW
(hours) (km)

2
1
KS
1 2010 Berlenga
22372
CHI
44
3058
298 ETPL
2 2010 Berlenga
22388
CHI
23 1261.2
303 ETPL
0.87 1.36[1.25,1.47]
0.06 0.02 0.02
3 2010 Berlenga
22525
CHI
23
996.3
235 ETPL
0.7 2.15[2.02,2.28]
0.021 2.45 0.03
4 2010 Berlenga
39109
CHI
21 2219.6
316 ETPL
5 2010 Berlenga
39114
CHI
41 1571.8
326 ETPL
0.98 1.99[1.88,2.10]
6 2010 Berlenga
44531
CHI
21 2963.5
456 ETPL
1 1.36[1.27,1.45]
7 2010 Berlenga
44824
CHI
21 3463.2
240 ETPL
1 1.52[1.39,1.65] 0.0019 0.24 0.03
8 2010 Berlenga
54006
CHI
208 1411.1
195 ETPL
1 1.56[1.42,1.70] 0.0005 0.16 0.05
9 2010 Berlenga
59360
CHI
65
761
76 ETPL
0.67 1.58[1.35,1.81]
0
10 2010 Berlenga
59755
CHI
16
58.2
16 ETPL
0.87 1.00[0.50,1.50]
0 0.04 0.12
11 2010 Berlenga
61358
CHI
71 1053.1
117 ETPL
1 1.40[1.22,1.58] 0.0054
12 2010 Berlenga
64211
CHI
307 3814.5
428 ETPL
1 2.00[1.90,2.10]
0 1.76 0.03
13 2010 Berlenga
64222
CHI
25
320.1
36 ETPL
0.39 1.48[1.15,1.81]
0 0.44 0.08
14 2010 Berlenga
64228
CHI
21
116.4
29 BIEXP 0.42
15 2010 Berlenga
64331
CHI
16
88
20 PL
0.73 1.45[1.00,1.90]
0
0
16 2010 Berlenga
64338
CHI
165 2942.1
149 PL
1 1.44[1.28,1.60]
0
0
17 2010 Berlenga
64391
CHI
41
601.1
74 ETPL
0.89 1.69[1.46,1.92]
0 1.05 0.08
18 2010 Berlenga
64393
CHI
40
373.9
90 ETPL
0.72 2.11[1.90,2.32]
0 1.72 0.06
19 2010 Berlenga
64397
CHI
21
88.7
27 PL
0.77 1.63[1.25,2.01]
0
0
20 2010 Berlenga
67769
CHI
41
451.5
51 PL
0.97 1.59[1.31,1.87]
0
0
21 2010 Berlenga
73254
CHI
45
427.3
71 ETPL
0.74 1.67[1.43,1.91]
0 0.64 0.05
22 2010 Berlenga
73290
CHI
69
828.7
111 ETPL
23 2010 Berlenga
76503
CHI
21
85
23 ETPL
0.75 1.57[1.15,1.99]
0 0.62 0.11
24 2010 Berlenga
76516
CHI
47
571.8
126 ETPL
0.94 2.23[2.05,2.41]
0 2.89 0.04
25 2010 Berlenga
76519
CHI
120 1762.4
196 ETPL
26 2010 Berlenga
76603
CHI
44
403.9
76 BIEXP 0.45
27 2010 Berlenga
78615
CHI
20
107.5
26 ETPL
0.73 2.16[1.77,2.55]
0 2.29 0.09
28 2010 Berlenga
78646
CHI
184 1696.9
265 ETPL
1 1.58[1.46,1.70]
0.01 0.39 0.03
29 2010 Berlenga
8043
CHI
69 1815.3
272 ETPL
0.99 1.86[1.74,1.98]
0.001 1.75 0.05
30 2007 Corvo
44207
CHI
91 1376.3
130 ETPL
1 2.00[1.82,2.18]
0 1.33 0.05
31 2007 Corvo
44242
CHI
17
60.6
21 ETPL
0.63 2.99[2.55,3.43]
0 3.66 0.12
32 2007 Corvo
44350
CHI
41
522.7
89 ETPL
0.83 1.49[1.28,1.70] 0.0034
33 2007 Corvo
61794
CHI
255 3705.1
319 ETPL
1 1.82[1.71,1.93]
34 2007 Corvo
63373
CHI
37 ETPL
0.9 1.14[0.81,1.47]
20
227.6
16
1 1.64[1.52,1.76] 0.0009 0.47 0.03
1 1.55[1.44,1.66] 0.0074 0.01 0.03
0 0.73 0.02
0.022
0 0.02
0 0.05
0 0.04
0.92 1.63[1.44,1.82] 0.0082 0.84 0.04
1 1.42[1.28,1.56] 0.0057
0 0.06
0 0.05
0 1.04 0.05
0
0 0.08
35 2007 Corvo
63822
CHI
49
1122
43 ETPL
0.97 1.58[1.28,1.88]
0 0.77 0.05
36 2007 Corvo
63855
CHI
16
104.4
16 PL
0.83 1.10[0.60,1.60]
0
37 2007 Corvo
63870
CHI
19
270
79 ETPL
0.64 1.93[1.70,2.16]
38 2007 Corvo
63890
CHI
17
59.5
20 PL
0.63 2.02[1.57,2.47]
0
0
39 2007 Corvo
63892
CHI
17
102.1
20 PL
0.58 1.45[1.00,1.90]
0
0
40 2007 Corvo
66724
CHI
102
268.1
9 ETPL
0.61 1.45[0.78,2.12]
0 1.88 0.11
41 2007 Corvo
69033
CHI
17
225.4
28 ETPL
0.82 1.25[0.87,1.63]
0 0.08 0.09
42 2007 Corvo
73366
CHI
17
248.7
55 BIEXP
43 2007 Corvo
73367
CHI
16
87.1
26 PL
0.69 1.54[1.15,1.93]
0
0
44 2007 Corvo
73369
CHI
17
4001
13 PL
0.71 1.53[0.98,2.08]
0
0
45 2007 Corvo
73370
CHI
21
148.1
33 ETPL
46 2007 Corvo
73372
CHI
16
100.1
24 BIEXP 0.43
47 2007 Corvo
78306
CHI
17
73.9
16 ETPL
0.83 1.37[0.87,1.87]
0
48 2007 Corvo
78307
CHI
15
89.7
25 PL
0.84 1.24[0.84,1.64]
0
49 2007 Corvo
78310
CHI
16 4055.4
27 ETPL
0.82 1.68[1.30,2.06]
0 1.49 0.07
50 2008 Linosa
T72729
CHI
47
247.2
47 ETPL
0.71 1.44[1.15,1.73]
0.001
51 2012 Linosa
T73139
CHI
25
138.3
12 PL
0.77 1.16[0.58,1.74]
0
52 2008 Linosa
T73735
CHI
69
559.8
67 ETPL
0.56 1.67[1.43,1.91]
0 1.24 0.10
53 2008 Linosa
T73783
CHI
28
119.1
27 ETPL
0.65 1.57[1.19,1.95]
0 0.54 0.10
54 2012 Linosa
T73800
CHI
89
645.7
112 ETPL
0.65 1.61[1.42,1.80]
0.014 0.69 0.05
55 2012 Linosa
T74208
CHI
47
154.1
15 BIEXP 0.21
56 2012 Linosa
T74401
CHI
125
968.6
97 ETPL
0.98 1.39[1.19,1.59]
57 2012 Linosa
T74554
CHI
27
148.8
23 ETPL
0.75 1.55[1.13,1.97]
58 2012 Linosa
T75203
CHI
26
135
14 BIEXP 0.39
59 2008 Linosa
T75215
CHI
44
264.9
48 BIEXP 0.43
60 2008 Linosa
T75237
CHI
51
317.1
49 ETPL
0.86 1.52[1.23,1.81]
61 2012 Linosa
T75239
CHI
22
924.4
6 ETPL
0.71 1.49[1.31,1.67]
0.023 0.05 0.39
62 2008 Linosa
T75239
INC
148
126.9
129 ETPL
0.89 1.00[0.18,1.82]
0 4.99 0.05
63 2012 Linosa
T75280
CHI
25
277.7
0.62 1.38[1.05,1.71]
0
64 2008 Linosa
T75298
CHI
255 1630.1
213 ETPL
0.98 1.87[1.73,2.01]
0.003 2.23 0.05
65 2008 Linosa
T75409
CHI
213 1065.1
165 ETPL
0.99 2.39[2.23,2.55]
0 2.97 0.03
66 2012 Linosa
T75478
CHI
27
148.7
21 PL
0.81 1.38[0.94,1.82]
0
0
67 2012 Linosa
T75499
CHI
40
299.8
33 PL
0.79 1.18[0.83,1.53]
0
0
68 2012 Linosa
T75500
CHI
22
168.8
15 PL
0.66 1.24[0.72,1.76]
0
0
69 2012 Linosa
TB4602
CHI
24
228.2
24 ETPL
0.7 1.72[1.31,2.13]
0 2.34 0.07
70 2012 Linosa
TB4667
CHI
51
306.8
43 PL
0.6 1.42[1.12,1.72]
0
71 2008 Linosa
TE9522
INC
16
92.2
72 2012 Linosa
TH1744
CHI
19
98.9
5 PL
0.5 1.00[0.11,1.89]
73 2008 Linosa
TH1759
INC
132
865.7
140 ETPL
0.97 1.96[1.79,2.13]
0.002 1.59 0.05
74 2008 Linosa
TH1863
INC
244 1394.9
255 ETPL
0.96 1.89[1.76,2.02]
0 0.62 0.02
17
36 PL
14 ETPL
0
0.058 2.02 0.06
0.4
0.8 1.52[1.17,1.87]
0.75 1.28[0.75,1.81]
0 0.18 0.08
0.007
2.1 0.10
0
0 0.08
0
0 0.05
0 0.45 0.09
0
0 0.06
0
0
0 0.95 0.12
0
0
75 2008 Linosa
TH1871
INC
162 1350.1
141 ETPL
0.86 1.35[1.18,1.52]
76 2008 Linosa
TH4333
INC
252 1637.4
325 ETPL
0.98 2.09[1.98,2.20]
0.006 1.91 0.03
77 2008 Linosa
TH4335
CHI
363.3
57 ETPL
0.6 1.00[0.74,1.26]
0.043 0.75 0.10
78 2008 Linosa
TH4336
CHI
189 1683.2
159 ETPL
0.97 1.32[1.16,1.48]
0.013 0.63 0.03
79 2012 Linosa
TH4343
CHI
80 2008 Linosa
TH4398
CHI
81 2012 Linosa
TH5971
CHI
21
155.7
30 PL
82 2008 Linosa
TH7178
INC
77
338.6
48 BIEXP 0.46
83 2008 Linosa
TH7179
INC
102
585.7
91 PL
84 2008 Linosa
TH7183
INC
267 1411.1
256 ETPL
85 2008 Linosa
TH7185
INC
273 1675.4
276 BIEXP 0.84
86 2008 Linosa
TH7203
CHI
18
57.6
10 PL
0.63 1.34[0.71,1.97]
87 2012 Linosa
TH7234
CHI
117
751
84 ETPL
0.77 1.37[1.15,1.59]
88 2012 Linosa
TH7487
CHI
14
88.6
15 PL
89 2012 Linosa
TH7489
CHI
23
131.4
13 ETPL
90 2012 Linosa
TH7491
CHI
27
182.9
18 BIEXP 0.46
91 2012 Linosa
TH7492
CHI
25
146.4
16 PL
0.71 1.13[0.63,1.63]
0
92 2008 Linosa
TH8040
INC
74
580.9
72 ETPL
0.71 1.60[1.36,1.84]
0 0.81 0.04
93 2008 Linosa
TH8412
INC
46
129.8
32 PL
0.73 1.82[1.47,2.17]
0
0
94 2012 Linosa
TH8414
CHI
18
142.5
24 PL
0.79 1.05[0.64,1.46]
0
0
95 2008 Linosa
TH8438
INC
18
118.6
20 ETPL
0.76 1.33[0.88,1.78]
0 2.03 0.16
96 2008 Linosa
TH8444
INC
254 1566.1
286 ETPL
0.94 1.57[1.45,1.69]
0.029 0.91 0.02
97 2008 Linosa
TH8447
CHI
26
118.8
98 2008 Linosa
TH8450
CHI
98
341
99 2012 Linosa
TH8485
CHI
157
860.8
100 2012 Linosa
TH8507
CHI
70
462.7
63 ETPL
0.85 1.51[1.26,1.76]
0 0.33 0.05
101 2008 Linosa
TH8510
CHI
188
943.4
144 ETPL
0.93 1.99[1.82,2.16]
0 1.59 0.03
102 2008 Linosa
TH8530
CHI
25
59.1
16 ETPL
0.64 1.75[1.25,2.25]
0 0.91 0.15
103 2008 Linosa
TH8534
CHI
233 1392.4
219 ETPL
0.99 1.22[1.08,1.36]
0.03 0.01 0.04
104 2008 Linosa
TH8535
CHI
188
981
171 ETPL
0.97 1.88[1.73,2.03]
0 1.81 0.03
105 2008 Linosa
TH8544
CHI
73
470.7
70 ETPL
0.79 1.66[1.42,1.90]
0 1.26 0.05
106 2012 Linosa
TJ8151
CHI
44
287.1
50 PL
107 2012 Linosa
TJ8152
CHI
22
197.9
15 ETPL
0.55 1.16[0.64,1.68]
0 1.27 0.19
108 2012 Linosa
TJ8154
CHI
22
133.2
13 ETPL
0.83 1.00[0.45,1.55]
0 1.17 0.16
109 2012 Linosa
TJ8155
CHI
173
1205
110 2012 Linosa
TJ8156
CHI
88
493.1
74 ETPL
0.77 1.84[1.61,2.07]
0 1.32 0.06
111 2012 Linosa
TJ8157
CHI
91
546.3
60 ETPL
0.83 1.89[1.63,2.15]
0 1.81 0.09
112 2012 Linosa
TJ8160
CHI
41
490.2
47 ETPL
0.91 1.13[0.84,1.42]
0 0.01 0.08
113 2012 Linosa
TJ8165
CHI
260 1664.2
224 ETPL
0.99 1.74[1.61,1.87]
0.006 1.14 0.03
114 2012 Linosa
TJ8168
CHI
82 ETPL
0.55 1.73[1.51,1.95]
0 1.53 0.04
70
91
902.4
192 1301.5
72
606.7
18
0.01
0 0.05
65 BIEXP 0.38
162 ETPL
1 1.72[1.56,1.88]
0.81 1.36[0.99,1.73]
0.004 0.89 0.03
0
0
0.87 1.45[1.24,1.66]
0
0
0.96 1.94[1.80,2.08]
0 1.38 0.03
0.8 1.00[0.48,1.52]
0.81 1.00[0.45,1.55]
26 PL
0.75 1.79[1.40,2.18]
73 ETPL
0.77 1.84[1.61,2.07]
0
0
0.014 0.43 0.07
0
0
0 0.96 0.15
0
0
0
0.023 2.52 0.06
151 BIEXP 0.31
0.9 1.59[1.31,1.87]
0
0
144 BIEXP 0.16
115 2012 Linosa
TJ8170
CHI
79
882.1
123 ETPL
0.91 1.45[1.27,1.63]
0.012 0.57 0.04
116 2012 Linosa
TJ8171
CHI
48 2416.3
311 ETPL
1 1.79[1.68,1.90]
0 1.54 0.05
117 2012 Linosa
TJ8172
CHI
24
118.3
15 ETPL
0.79 1.36[0.84,1.88]
0 2.21 0.09
118 2012 Linosa
TJ8173
CHI
42
604.7
66 ETPL
0.87 1.14[0.89,1.39]
0.01 0.01 0.04
119 2013 Maddalena TA6976
CHI
17
151.1
24 ETPL
0.75 1.28[0.87,1.69]
120 2013 Maddalena TA7167
CHI
19
298.2
18 BIEXP 0.14
121 2013 Maddalena TC4838
CHI
24
285.2
35 ETPL
122 2013 Maddalena TJ1376
CHI
23
167.2
22 PL
123 2013 Maddalena TJ8101
CHI
21
304.7
23 BIEXP 0.01
124 2013 Maddalena TJ8103
CHI
45
376
38 ETPL
0.72 1.81[1.49,2.13]
0 3.23 0.04
125 2013 Maddalena TJ8123
CHI
142 1712.6
255 ETPL
1 1.70[1.57,1.83]
0.014 1.22 0.03
126 2013 Maddalena TJ8124
CHI
24
96.6
22 PL
0.63 1.96[1.53,2.39]
0
0
127 2013 Maddalena TJ8126
CHI
20
207
27 PL
0.66 1.32[0.94,1.70]
0
0
128 2013 Maddalena TJ8135
CHI
24
77.3
19 ETPL
0.93 1.00[0.54,1.46]
0 0.64 0.12
129 2013 Maddalena TJ8137
CHI
91
799.7
96 ETPL
0.56 1.62[1.42,1.82] 0.0064 1.11 0.05
130 2013 Maddalena TJ8141
CHI
24
77.6
131 2013 Maddalena TJ8150
CHI
95
593.3
107 ETPL
0.67 1.97[1.78,2.16]
0 1.57 0.04
132 2013 Maddalena TK0413
CHI
19
91.6
32 ETPL
0.7 1.69[1.34,2.04]
0 0.09 0.09
133 2013 Maddalena TK0414
CHI
21
184.6
27 ETPL
0.63 1.07[0.69,1.45]
0.01
134 2013 Maddalena TK0415
CHI
20
154
21 PL
0.74 1.25[0.81,1.69]
0
135 2013 Maddalena TK0416
CHI
24
143.4
28 BIEXP 0.27
136 2013 Maddalena TK0418
CHI
35
237.3
49 ETPL
0.77 1.83[1.54,2.12]
0 0.93 0.06
137 2013 Maddalena TK0419
CHI
23
123.8
25 ETPL
0.69 2.00[1.60,2.40]
0 2.38 0.07
138 2013 Maddalena TK0420
CHI
52
181.8
29 ETPL
0.64 1.49[1.12,1.86]
0 1.26 0.10
139 2013 Maddalena TK0421
CHI
52
496.6
63 PL
0.66 1.55[1.30,1.80]
0
140 2013 Maddalena TK0422
CHI
141 2013 Maddalena TK0423
CHI
27
60.1
142 2013 Maddalena TK0424
CHI
21
143 2013 Maddalena TK0425
CHI
144 2013 Maddalena TK0426
166 1608.1
0.74 1.71[1.37,2.05]
0.7 1.56[1.13,1.99]
22 EXPL 0.63 1.33[0.90,1.76]
228 ETPL
1 2.07[1.94,2.20]
0
0.3 0.12
0 1.11 0.08
0
0.043
0
0
0 0.11
0
0
0 1.93 0.02
16 PL
0.84 1.11[0.61,1.61]
0
193.5
57 ETPL
0.67 2.58[2.32,2.84]
0
20
244.5
15 ETPL
0.75 1.01[0.49,1.53]
0 0.78 0.10
CHI
25
260.8
20 BIEXP 0.41
145 2013 Maddalena TK0430
CHI
46
396.7
58 ETPL
0.79 1.49[1.23,1.75]
0 0.57 0.08
146 2013 Maddalena TK0431
CHI
18
121.9
17 ETPL
0.58 1.77[1.28,2.26]
0 1.44 0.09
147 2013 Maddalena TK0432
CHI
82
196.4
63 ETPL
0.87 2.25[2.00,2.50] 0.0002
148 2013 Maddalena TK0433
CHI
43
484.5
54 BIEXP 0.37
149 2013 Maddalena TK0435
CHI
67
435.4
75 ETPL
150 2013 Maddalena TK0439
CHI
19
155.4
17 BIEXP 0.47
0.87 1.43[1.20,1.66]
0.017
0
2.7 0.06
0.6 0.05
0 0.05
151 2013 Raso
CRlog100F CHI
14 3880.5
349 ETPL
1 1.79[1.68,1.90]
0 1.28 0.03
152 2013 Raso
CRlog110F CHI
16 5674.2
532 ETPL
1 1.37[1.28,1.46]
0.009 1.44 0.02
153 2013 Raso
CRlog118F CHI
66 4728.1
436 ETPL
154 2013 Raso
CRlog119F CHI
403 5231.4
470 ETPL
19
0.95 1.73[1.63,1.83] 0.0008 1.54 0.04
1 1.65[1.56,1.74]
0.003 1.24 0.02
155 2013 Raso
CRlog34F
CHI
15 5238.1
365 ETPL
1 1.60[1.50,1.70]
0.001 1.11 0.03
156 2013 Raso
CRlog88F
CHI
15 4766.4
296 ETPL
0.83 1.36[1.24,1.48]
0.004 0.54 0.03
157 2013 Raso
CRlog92F
CHI
22 4694.7
432 ETPL
158 2013 Raso
INlog100F INC
159 2013 Raso
17
1 1.51[1.41,1.61] 0.0003 0.48 0.02
591
97 ETPL
0.91 2.01[1.81,2.21]
0 1.25 0.06
INlog104F INC
213 2806.2
192 ETPL
0.76 1.75[1.61,1.89]
0.001 2.19 0.03
160 2013 Raso
INlog110F INC
258 3250.2
273 ETPL
1 1.86[1.74,1.98]
0.001 1.79 0.04
161 2013 Raso
INlog118F INC
261 4705.5
274 ETPL
1 1.77[1.65,1.89] 0.0005 1.68 0.03
162 2013 Raso
INlog119F INC
281 2760.3
300 ETPL
1 2.41[2.29,2.53]
0 3.51 0.04
163 2013 Raso
INlog122F INC
197 1664.4
218 ETPL
1 2.17[2.03,2.31]
0 2.33 0.05
164 2013 Raso
INlog123F INC
281 3233.1
329 ETPL
1 1.86[1.75,1.97]
0.001 1.46 0.03
165 2013 Raso
INlog129F INC
238 2553.7
214 ETPL
0.85 2.15[2.01,2.29]
0 3.42 0.04
166 2013 Raso
INlog131F INC
231 1923.7
304 ETPL
1 1.74[1.63,1.85]
0.016
167 2013 Raso
INlog135F INC
0.54 1.51[0.99,2.03]
0
168 2013 Raso
INlog79F
INC
112 1103.1
74 ETPL
0.9 1.47[1.24,1.70]
0 0.71 0.05
169 2008 Selvagem
20101
CHI
91 1002.1
53 ETPL
0.84 1.45[1.18,1.72]
0 0.74 0.04
170 2008 Selvagem
33203
CHI
14
160.2
36 PL
0.8 1.38[1.05,1.71]
0
0
171 2008 Selvagem
37488
CHI
14
296.7
16 PL
0.67 1.22[0.72,1.72]
0
0
172 2008 Selvagem
40242
CHI
14
127.4
28 ETPL
0.73 1.82[1.44,2.20]
0 1.71 0.07
173 2008 Selvagem
43975
CHI
15
118.1
26 PL
0.57 1.65[1.26,2.04]
0
174 2008 Selvagem
43992
CHI
235 2739.8
290 ETPL
1 1.60[1.48,1.72]
0.005 0.53 0.03
175 2008 Selvagem
74835
CHI
184 2369.5
266 ETPL
1 1.43[1.31,1.55]
0.01 0.65 0.03
176 2008 Selvagem
76429
CHI
104 PL
1 1.71[1.51,1.91]
177 2008 Selvagem
76430
CHI
159 2042.3
154 ETPL
1 1.32[1.16,1.48]
0.01 0.48 0.04
178 2008 Selvagem
76434
CHI
188 1709.8
207 ETPL
0.97 2.00[1.86,2.14]
0.002 2.77 0.05
179 2008 Selvagem
76436
CHI
133 1235.6
136 ETPL
0.97 1.97[1.80,2.14]
0 1.67 0.04
180 2008 Selvagem
768379
CHI
15
117.2
31 PL
0.92 1.07[0.71,1.43]
0
0
181 2008 Selvagem
78380
CHI
14
78.1
17 PL
0.65 1.36[0.87,1.85]
0
0
182 2008 Selvagem
78383
CHI
20
271.6
33 BIEXP 0.16
183 2008 Selvagem
78384
CHI
10
262.5
13 ETPL
184 2008 Selvagem
78386
CHI
143 1646.8
165 ETPL
185 2009 Tremiti
T97749
INC
119
706.3
148 PL
0.91 1.66[1.50,1.82]
186 2010 Tremiti
T97769
INC
100
416
141 ETPL
0.56 2.25[2.08,2.42]
0.094 2.53 0.03
187 2010 Tremiti
TB0524
INC
70
549.7
90 ETPL
0.81 1.92[1.71,2.13]
0 2.56 0.06
188 2009 Tremiti
TC7503
INC
161
813.7
171 ETPL
0.55 1.30[1.15,1.45]
0.046 0.16 0.04
189 2009 Tremiti
TC7505
INC
45
276
190 2009 Tremiti
TC7506
INC
80 1009.7
191 2009 Tremiti
TC7507
INC
124
192 2010 Tremiti
TC7509
INC
193 2010 Tremiti
TC7510
194 2009 Tremiti
TC7511
17
97
197.7
1100
15 PL
0
0
0
0 1.52 0.15
1 1.78[1.62,1.94] 0.0006 0.95 0.06
0
0
0.97 1.17[0.89,1.45]
0
141 ETPL
0.73 1.61[1.44,1.78]
0 0.48 0.03
477.2
102 ETPL
0.92 1.72[1.52,1.92]
0
81
605
106 ETPL
0.73 1.36[1.17,1.55]
0.021 0.02 0.05
INC
69
301.3
65 ETPL
0.7 2.26[2.01,2.51]
0 2.75 0.05
INC
89
524
20
51 PL
0.77 1.13[0.58,1.68]
0
1.6 0.03
114 -
0.5 2.10[1.91,2.29]
0
1.1 0.06
0.006 1.69
195 2010 Tremiti
TC7514
INC
196 2010 Tremiti
TC7519
197 2010 Tremiti
63
304.2
55 ETPL
0.7 2.23[1.96,2.50]
0 3.34 0.06
INC
249 2212.3
345 ETPL
0.99 1.32[1.21,1.43]
0.028 0.54 0.03
TC7521
INC
137 1174.5
157 ETPL
0.99 1.48[1.32,1.64]
0.015 0.88 0.06
198 2010 Tremiti
TC7522
INC
20
100.2
199 2010 Tremiti
TC7524
INC
178
1428
232 ETPL
0.93 2.12[1.99,2.25]
0 2.74 0.03
200 2010 Tremiti
TC7525
INC
99
944.1
103 ETPL
0.95 1.22[1.02,1.42]
0.017 0.09 0.04
201 2010 Tremiti
TC7526
INC
194 1429.4
205 ETPL
1 1.47[1.33,1.61]
0.017 0.42 0.03
202 2010 Tremiti
TC7527
INC
237 1763.3
313 ETPL
0.79 1.72[1.61,1.83]
203 2010 Tremiti
TC7529
INC
82
510
121 ETPL
0.85 2.71[2.53,2.89]
0 3.77 0.04
204 2010 Tremiti
TC7533
INC
39
222.5
52 ETPL
0.57 1.96[1.68,2.24]
0 1.65 0.06
205 2009 Tremiti
TE1339
INC
18
111.9
19 ETPL
0.66 1.00[0.54,1.46]
0.031 0.01 0.11
206 2009 Tremiti
TE1341
INC
174
957.8
221 ETPL
0.62 1.80[1.67,1.93]
0.062 2.46 0.04
207 2009 Tremiti
TE1342
INC
68
490.6
63 ETPL
0.53 1.81[1.56,2.06]
0 1.61 0.07
208 2010 Tremiti
TE1343
INC
224 2425.2
250 ETPL
1 1.48[1.35,1.61]
0.009 0.88 0.03
209 2009 Tremiti
TE1345
INC
143
888.2
165 ETPL
0.87 1.82[1.66,1.98]
0.019 1.66 0.03
210 2009 Tremiti
TE1347
INC
161
766.3
166 ETPL
0.85 2.42[2.26,2.58]
0 2.55 0.03
21
23 PL
0.8 1.43[1.01,1.85]
0
0.018
0
1.5 0.02
Figure S4. Rank frequency plots of theoretically predicted distribution of displacement derived by the ETPl model (black
continous line) and actual data (red open circles).
22