Article
Fin Whale (Balaenoptera physalus) Mortality along the Italian
Coast between 1624 and 2021
Valerio Manfrini 1,*,†, Nino Pierantonio 2,*,†, Alessandro Giuliani 3, Federico De Pascalis 4, Nicola Maio 5,‡
and Annalaura Mancia 6,‡
Independent Researcher, 44022 Comacchio, FE, Italy
Tethys Research Institute, 20121 Milan, Italy
3 Environment and Health Department, Istituto Superiore di Sanità (National Institute of Health),
00161 Rome, Italy
4 BIO-AVM, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA),
40064 Ozzano dell’Emilia, Italy
5 Dipartimento di Biologia, Università degli Studi di Napoli Federico II, 80126 Naples, Italy
6 Dipartimento Scienze della Vita e Biotecnologie, Università di Ferrara, 44121 Ferrara, Italy
* Correspondence: valemanfro@gmail.com (V.M.); nino.pierantonio@protonmail.com (N.P.)
† These authors contributed equally to this work.
‡ These authors share the last authorship.
1
2
Citation: Manfrini, V.;
Pierantonio, N.; Giuliani, A.;
De Pascalis, F.; Maio, N.; Mancia, A.
Fin Whale (Balaenoptera physalus)
Mortality along the Italian Coast
between 1624 and 2021. Animals
2022, 12, 3111. https://doi.org/
10.3390/ani12223111
Academic Editor: Francesco
Filiciotto
Received: 30 September 2022
Accepted: 8 November 2022
Published: 10 November 2022
Publisher’s Note: MDPI stays neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Simple Summary: We present a comprehensive overview of fin whale historical and modern mortality events that occurred between 1624 and 2021 along the Italian coast, highlighting spatial and
temporal patterns and, where possible, the proximal causes of mortality. Emerging hot spot analysis
shows the spatial and temporal consistency of mortality events along the northern coast of the island
of Sardinia, the central coast of Tuscany and the Gulf of Trieste in the northern Adriatic Sea. The
coast of Liguria and the northern coast of Tuscany are sporadic hot spots, while the central coast of
Italy along the Tyrrhenian Sea as well as the coast of southern Sardinia and northern Sicily have
been identified as new hot spots of mortality events for the species. While the analysis of the temporal patterns suggests a steep increase in the number of mortality events starting in the second half
of the 1980s, we cannot exclude the possibility that this positive trend is the result of a strong observer bias. Conversely, recent mortality events seem to be consistent in number over the last six
decades and subject to year-round seasonality. Our results show that younger and immature individuals are the fin whales most affected by ship strikes. This study supports the implementation of
a conservation plan to ensure the survival of the species in the Mediterranean region.
Abstract: The Mediterranean Sea hosts a population of fin whale (Balaenoptera physalus), the only
species of Mysticete regularly occurring in the basin. Observed and inferred mortality suggests that
the population is likely declining. Accordingly, understanding the causes of mortality and assessing
the health status is pivotal to the survival of this endangered population. While such studies are
inherently difficult for a highly roaming species with a pelagic distribution, mortality events provide the opportunity to investigate biological and epidemiological traits linked to these events, and
evaluate the footprint of human activity, especially when long-term data series exist. We present a
comprehensive spatial–temporal overview of fin whale mortality events along the Italian coast encompassing four centuries (1624–2021). Time series analysis was used to highlight structural
changes in the evolution of mortality through time, while spatial–temporal patterns in the distribution of mortality events were assessed through emerging hot spot analysis methods. Recent mortality events (1964–2021) were further explored to evaluate, where possible, the primary causes of
mortality and to identify anthropogenic threats of conservation concerns. This long-term survey
offers the basis for an understanding of the health status of this B. physalus population and provides
much-needed information for developing an effective management and conservation plan for the
species in the region.
Animals 2022, 12, 3111. https://doi.org/10.3390/ani12223111
www.mdpi.com/journal/animals
Animals 2022, 12, 3111
2 of 22
Keywords: fin whale; Balaenoptera physalus; strandings; ship strikes; mortality events;
anthropogenic threats; Mediterranean Sea
1. Introduction
The Mediterranean Sea is a hot spot of biodiversity and is home to several species of
conservation concern [1]. In the basin, species and habitats alike are under ever-increasing
pressure derived from human activity with the potential to severely undermine their survival. While it is crucial to reduce the human footprint on the Mediterranean ecosystem
and manage threats, conservation efforts are often limited for highly mobile and cryptic
species, due to the lack of relevant information on abundance, distribution, and habitat
use. This is particularly true for cetaceans for which the collection of baseline information
is often hampered by the complex ecological traits of the target species. In this context,
historical and modern records of mortality events have proved particularly successful in
shedding light on several aspects of cetaceans’ biology and ecology, and supporting informed conservation and management decisions [2–8]. Post-mortem examinations of both
live- and dead-stranded cetaceans could, in fact, enable disease detection in otherwise inaccessible wild populations [9] and allow for health assessments aimed at understanding
the diseases, pathogens, and anthropogenic activities afflicting wild cetaceans [10–13].
Additionally, although the quality of the data derived from post-mortem examinations is
highly contingent upon the status of the specimen, and, therefore, often the causes of cetacean mortality remain largely unknown, the analysis of mortality events can lay the basis for developing quantitative frameworks for testing hypotheses regarding the correlates
of mortality [14–16]. Finally, studies of mortality events can be used to suggest recommendations for managing cetacean carcasses, identifying cost-effective disposal methods that
minimise costs, and maximise ecosystem functions and services [17].
The fin whale Balaenoptera physalus (Linnaeus, 1758) is the only Mysticete that regularly inhabits the Mediterranean Sea, where two distinct genetic lineages occur: true Mediterranean fin whales that reside year-round in the basin; and northeastern North Atlantic
(NENA) fin whales, which enter the basin through the Gibraltar Strait into the western
most sector of the Mediterranean Sea [18,19]. Fin whales aggregate during the summer in
the feeding grounds of the Pelagos Sanctuary for Mediterranean Marine Mammals [20]
and move out to winter-feeding and breeding grounds across the basin [21]. A direct connection between the winter and summer feeding grounds in the Strait of Sicily and the
northwestern Mediterranean, respectively, has been recently highlighted [22]. The extent
and timing of Mediterranean fin whale movements within the basin are still under debate,
with new evidence showing that relatively high numbers of fin whales extend their stay
in the northwestern Mediterranean well into the winter months [23].
A region-wide population estimate of fin whales in the Mediterranean was derived
from the 2018 ACCOBAMS Survey Initiative, resulting in a corrected estimate of 3280 individuals, all of them sighted in the western Mediterranean Sea [24]. The only earlier effort
that is comparable in methods and area with the 2018 monitoring was conducted in 1991
and produced an estimated abundance of 3583 individuals [25], 901 of which were feeding
in the Corsican-Ligurian-Provençal basin during the summer months [26]. A comparison
of these estimates suggests a putative population decrease of about 10% over the last 27
years [27].
The Mediterranean population of fin whales is currently classified as Endangered
according to IUCN Red List Criteria [27]. In addition, the species is listed in Appendix I
of CITES (Convention on International Trade in Endangered Species of Wild Fauna and
Flora). As a Mediterranean cetacean species, its conservation is also included in several
other international conventions: it is listed in Annex II of the SPAMI (Specially Protected
Areas of Mediterranean Importance or Barcelona Convention); in Appendix II of the Bern
Animals 2022, 12, 3111
3 of 22
Convention; in Annex IV of the EU Habitats Directive (Directive 43/92/EC); as ‘endangered migratory species’ in Appendix I and II of the Bonn Convention; and as protected
species in Annex 1 of the Agreement on the Conservation of Cetaceans of the Black Sea,
Mediterranean Sea and contiguous Atlantic Area (ACCOBAMS). In Italy, fin whales are
protected, like other cetaceans, by the Law on the Protection of Fauna (Law n. 157 of 11
February 1992).
Cetacean mortality events are relatively common in the Mediterranean Sea, with several cases of unusual die-off events occurring across the region [28–31] with the potential
to afflict the populations and species [22]. Specifically concerning Mediterranean fin
whales, evidence suggests that increased mortality is the result of exposure to direct and
indirect anthropogenic activities, among which ship strikes are considered the primary
cause [32–37]. However, recent studies have cautiously highlighted how dolphin morbillivirus (DMV) infections should be regarded as one of the main threats to Mediterranean
fin whales [30]. Additionally, exposure to chemical contaminants of different kinds (e.g.,
macro- and microplastics, POPs, trace metals) [38–41], pathogens [28,30], acoustic pollution [42] and entanglement in fishing gear [41] may impact the population. Habitat degradation, depletion of fish resources and other climate-induced changes in marine circulation regimes are also suggested as potential threats to the population [43].
In Italy, the first attempts to systematically collect data on cetacean mortality and
strandings were made by the World Wildlife Fund through the ‘Cetacean Project’ at the
end of the 1970s, followed by the efforts of the Centro Studi Cetacei (CSC), the first Italian
national stranding network, which was established in 1985 and started its nationwide activities in 1986 [44]. To date, the activities of CSC are concentrated in certain regions of the
Adriatic Sea.
Since 2006, the Interdisciplinary Center for Bioacoustics and Environmental Research
(Centro Interdisciplinare di Bioacustica e Ricerche Ambientali, CIBRA) of the University
of Pavia and the Museum of Natural History of Milan have added to the fundamental role
played by the CSC. They were charged by the Italian Ministry of the Environment and the
Protection of the Territory and the Sea (Ministero dell’Ambiente e della Tutela del Territorio e del Mare) to collect cetacean stranding data. As part of this assignment, the Italian
stranding network database (Banca dati spiaggiamenti, BDS—http://mammiferimarini.unipv.it/index_en.php (accessed on 8 November 2022)) was created.
In 2014, the national center for diagnostic investigations of stranded marine
mammals (Centro di referenza nazionale per le indagini diagnostiche sui mammiferi
marini spiaggiati, C.Re.Di.Ma.) was established. This institute cooperates with other entities of the national strandings network that includes the Cetacean stranding emergency
response team (CERT), the Mediterranean marine mammals tissue bank of the University
of Padua, and the BDS.
In this context, here we gather and report data on fin whale mortality along the Italian
coast between 1624 and 2021, highlighting spatial–temporal patterns of distribution of
mortality events. Furthermore, by investigating recent mortality events, we evaluate
whether significant differences exist in the biological characteristics of the specimens using data spanning 57 years (1964–2021).
2. Materials and Methods
2.1. Collection of Mortality Data
Information on mortality events was collated through a comprehensive content review of available published scientific literature, grey literature, historical accounts, and
available stranding databases and datasets including descriptions of mortality accounts.
For the latter, we specifically retrieved data from the BDS (last accessed on 20 September
2022), the Mediterranean Database of Cetacean Strandings (MEDACES—
http://medaces.uv.es/; last accessed on 20 September 2022), the database of the Centro
Studi Cetacei Onlus (GeoCetus—http://geocetus.it; last accessed on 20 September 2022)
Animals 2022, 12, 3111
4 of 22
[45], and the International Whaling Commission Global Ship Strikes Database (IWCGSSD—https://iwc.int/management-and-conservation/ship-strikes; last accessed on 20
September 2022). Finally, we accessed reports of CSC Onlus from 1981 to 2010 and the
Marine Mammals Sightings database of CIMA Foundation (MAMAS—https://mamas.cimafoundation.org/; last accessed on 20 September 2022).
Museum collections across Italy were also examined to obtain first-hand information
on previously unreported mortality events and to cross-check those events with missing
details, in particular for older accounts, and/or to resolve discrepancies between sources,
where possible. For each event, the following information was collated in full where available: date of the event, number of animals, sex, total body length (TBL), geographic location and coordinates, cause of death, status of the recovered carcass/animal, and other
relevant ancillary notes. Whenever possible TBL as available from the original sources
were cross-checked with museum collections to correct the estimates. This was carried out
by comparing condyle-premaxilla measurement to the TBL of specimens preserved in
museum collections [46–49]. From the date and the TBL we derived the season of the event
and the life stage, respectively. Seasons were defined as Spring (SP)—March to May, Summer (S)—June to August, Fall (F)—September to November and Winter (W)—December
to February.
Life stages were defined as fetus/calf/newborn (TBL< 10 m), immature (TBL = 10–15
m), sub-adult/adult/mature (TBL > 15 m) [50]. While different morphological features
would allow for more accurate identification of life stages and age classes [51], details on
these features were not always available for the collated mortality events, in particular for
older reports. Accordingly, to avoid bias introduced by potentially skewed data, in this
paper we only used TBL to derive individual life stages.
When the exact geographic coordinates of the mortality event were not available in
the original sources, they were approximated as accurately as possible based on the event
description and available graphical material.
The conservation status of each specimen was classified where possible as alive
(Code 1); dead, the specimen died a few hours after sighting/stranding (Code 2); moderate
decomposition (Code 3); advanced decomposition (Code 4); mummified/bones (Code 5);
and NA (not available/missing) [52].
All events for which discrepancies could not be resolved and, in general, could not
be attributed with certainty to fin whales, were discarded. The specimen recently identified as a hybrid between a fin whale and a blue whale [53] was included in the final dataset
based on the fact that the proportion of animals sampled for similar investigations across
the years is unknown and as a consequence, several other specimens identified as a fin
whale could in fact have been a hybrid animal that went unknown. Strandings of live
animals that were refloated or reached the open sea (i.e., did not die) were excluded from
the dataset. Animals that stranded alive but subsequently died were included with the
original date of the stranding.
Wherever possible, the causes of mortality were assessed and cross-checked amongst
sources and categorized into natural/biological and human-related. For historical data,
each event was classified as stranding (i.e., a category including all animals for which the
proximal cause of mortality was not identified), killing, by catch, ship strike, and floating
carcass. For modern data, each event was classified as ship strike, bycatch, and natural/biological (e.g., congenital disease).
The final dataset inclusive of all mortality categories was analyzed to assess spatial–
temporal patterns and to evaluate the biological characteristics of the specimens.
2.2. Statistical Analysis
2.2.1. Temporal Analysis
Before evaluating patterns in the time series, potential outliers were discarded from
the dataset. To assess the temporal evolution of annual mortality events along the Italian
Animals 2022, 12, 3111
5 of 22
coast, we performed structural-change analysis with the aim of highlighting break points
in the time series and suggesting changes in the pattern of mortality events over time.
Specifically, we used the strucchange package [54,55] for the software for statistical analysis
R [56] to assess deviations from stability in a classical linear regression model.
First, we used F statistics (Chow test statistics) to highlight the existence of at least
one break point in the time series and, therefore, the existence of at least two different time
segments. Secondly, we tested the time series for the existence of multiple break points,
where each potential break point is estimated alongside its lower and higher confidence
intervals [54,55].
For this analysis, when the day of the mortality event was missing, we used the 15th
day of the month or, if both the day and month were missing, the date of the 15th of June
of the specified year. This analysis was performed on the entire dataset.
2.2.2. Spatial Analysis
To visualize stretches of coastline with higher numbers of mortality events, a 50 km
by 50 km grid was overlaid on the Italian coastline, and for each cell of the grid, the number of mortality events was calculated. Each cell was then classified as ‘low’, ‘medium’, or
‘high’ mortality based on the percentile values of the number of mortality events per cell.
‘Low’ was assigned to values lower than the 25th percentile, ‘medium’ to values higher
than or equal to the 25th and smaller than the 75th percentile, and ‘high’ to values greater
than or equal to the 75th percentile.
2.2.3. Spatial–Temporal Analysis
Space-time cubes (STC) were created in ArcGIS Pro 2.5 (Esri Inc., 2020, Redlands,
California, USA) and were used to visualize and analyze spatial–temporal patterns of the
whole dataset of collated mortality events. Before evaluating potential patterns in the time
series, outliers were discarded from the dataset.
Data points were aggregated based on a 50 km spatial and 25 yr temporal scale, and
the count of points per bin was calculated. Mann-Kendall statistics for each independent
location were used to assess whether an increasing or decreasing trend was present in the
time series [57]. The result of the test was compared to the expected result of no trend over
time to determine if the observed result was statistically significant, based on the Z-score
(standard deviations) and p-values (statistical probabilities) for each space-time bin,
where a small p-value (<0.05) indicated that the trend was statistically significant. Hence,
the generated STC was analyzed in an Emerging Hot Spot Analysis framework through
Getis-Ord (Gi*) statistics [58,59].
The hot and cold spot trends detected by the Getis-Ord Gi* hot spot analysis were
evaluated with the Mann-Kendall test to determine whether trends were persistent, increasing, or decreasing over time, by comparing the Z-score of each bin in the STC to
neighbouring bins. A Z-score ≥ 1.96 or ≤ 1.96 signifies a statistically significant hot spot or
cold spot, respectively, at a significance level of p < 0.05. Under this approach, a hot spot
is therefore a section of the coastline with statistically significant clustering in both space
and time.
The output of the Getis-Ord (Gi*) statistics on the STC was then visualized as a twodimensional representation uniquely rendered into different categories, describing the
statistical significance of hot or cold spots and the trend of the location over time.
2.2.4. Correlation Analysis
The descriptive-observational character of the study orientated the statistical strategy
towards a correlative approach to assess the presence of significant correlations between
death cause, season, life stage, and other descriptors. Based on the results of the breakpoint analysis, a subsample of modern events was selected to evaluate temporal, spatial,
morphometric/biological and event-related descriptors. For each event, time—defined as
Animals 2022, 12, 3111
6 of 22
the number of consecutive days since the first modern record was registered—was log10transformed, and a time-index expressed as the rank order of time was generated and
included in the analysis alongside the variable season. Spatial variables included the latitude and longitude of each event and the region (i.e., the administrative region where each
event occurred). Morphometric/biological descriptors included TBL, life stage and sex.
Event-related descriptors were the cause of death, a categorical variable divided into
natural/biological, ship strike, and bycatch classes, and cause of death stemming from the
former by combining ship strike and bycatch classes into the single ‘human-related’ category. Missing data were reported in a ‘NA’ category for the above descriptors.
One derived categorical variable (cluster) was generated by the application of kmeans non-hierarchical clustering to Latitude/Longitude. The clustering was restricted to
the Tyrrhenian Sea to avoid potential bias introduced by the clustering procedure based
on Euclidean metrics not being able to account for the presence of barriers (e.g., the Italian
peninsula between the Tyrrhenian and Adriatic Seas). This variable has three categories
corresponding to the Liguria/Tuscany, Sardinia and southern Italy data-driven clusters,
respectively.
Correlation between fully numeric variables was computed by the Pearson correlation coefficient, while correlations between categorical variables were estimated by Chisquare metrics.
3. Results
Overall, 179 mortality events were found between 1624 and 2021 of which 125 were
categorized as ‘stranding/unknown’, 24 as ship strikes, 16 as killing events, 9 as biological/natural (after necropsies) and 5 as bycatch/entanglement events.
Three outlier years were highlighted in the time series of mortality events (1624, 1713
and 1728) (Figure S1 in Supplementary Materials) and therefore were removed from the
dataset for further spatial, temporal and spatial–temporal analysis.
3.1. Temporal Analysis
The breakpoint analysis highlighted the occurrence of only one structural change in
the time series, the year 1984 (LCI = 1964; UCI = 1988), and, consequently, an optimal twosegment partition in the reconstructed time series of mortality events (Figure 1). This
breakpoint shows that when looking at the overall evolution of fin whale mortality events
over the last five centuries, one significant change in the trend in mortality has occurred,
with an increase in the observation of mortality events. The pattern of cumulative mortality events through time (Figure 1) and the pattern of cumulative number of dead animals
strongly matches (Figure S2) because most of the mortality events involved a single animal. Figure 2 shows the annual number of dead fin whales between 1624 and 2021 and
highlights the relatively higher number of casualties around 1900 and in general in recent
years from 1980 onwards.
Animals 2022, 12, 3111
7 of 22
Figure 1. The cumulative number of mortality events between 1624 and 2021. Although the years
1624, 1713, and 1728 were highlighted as outliers in the time series and not included in the formal
analysis, they are included here to depict the entire dataset. Dashed red lines represent the breakpoint in the time series and the dashed orange lines represent the breakpoint Lower and Upper
Confidence Intervals.
Figure 2. The total number of dead fin whales by year between 1624 and 2021. Although the years
1624, 1713, and 1728 were highlighted as outliers in the time series and not included in the formal
analysis, they are shown here to depict the entire dataset. Dashed red lines represent the breakpoint
in the time series and the dashed orange lines represent the breakpoint Lower and Upper Confidence Intervals.
Based on the results of the breakpoint analysis, the data from 1964 onward are considered ‘modern’ data and included in the formal Correlation Analyses. Mortality events
before 1964 are considered ‘historical’ data. It should be noted that in this study, we used
the term stranding, including the definitions of strandling and beaching [52], to improve
readability. However, this term only indicates the consequence of death cause but not the
Animals 2022, 12, 3111
8 of 22
proximal cause of mortality, which in most cases remains unknown. Without post-mortem examinations on each specimen, we cannot establish with certainty the cause of death,
so it is more appropriate to indicate hypothetical causes of death. Summaries of collated
information for these two temporal datasets are provided in Tables 1 and 2, respectively
(for more details on collated information, see the full dataset in the Supplementary Materials).
Table 1. Summary of the main variables of historical mortality events (1624–1963). N/A Not available, (*) Here, the cause of death is known.
Variable
No. of animals
Sex
Life stage
Total length (m)
Cause of death
Hypothetical human-related
cause of death
Season
Region
Carcass/Body conservation status
Description
Male
Female
Unknown
Fetus/Calf/Newborn
Immature
Sub-adult/Adult/Mature
NA
Minimum
Maximum
Hypothetical
Unknown
Killing *
Bycatch
Ship strike
Autumn
Winter
NA
Summer
Spring
Liguria
Tuscany
Sardinia
Lazio
Campania
Puglia
Sicily
Calabria
Friuli-Venezia Giulia
Abruzzo
Alive (Code 1)/dead (Code 2)
NA
Value
67
12
10
45
8
16
29
14
5.50
21.50
19
48
16
2
1
16
16
14
14
7
17
15
10
7
5
5
3
2
2
1
2
65
%
17.91
14.92
67.16
11.94
23.88
43.28
20.89
28.35
71.64
84.21
10.52
5.26
23.88
20.89
10.44
25.37
22.38
14.92
10.44
7.46
4.47
2.98
1.49
2.98
97.01
Table 2. Summary of the main variables of modern mortality events (1964–2021). NA Not available,
(*) Estimated and measured lengths, (°) One animal was Code 5 (Mummified/Bones).
Variable
No. of animals
Sex
Description
Male
Female
Unknown
Value
112
27
38
47
%
24.10
33.92
41.96
Animals 2022, 12, 3111
9 of 22
Life stage
Total length (m) *
Cause of death
Hypothetical cause of death
Human related cause of death
Season
Region
Carcass/Body conservation °
Fetus/Calf/Newborn
Immature
Sub-adult/Adult/Mature
NA
Minimum
Maximum
Hypothetical
Unknown
Human-related
Natural/Biological
Ship strike
Bycatch/entanglement
Autumn
Spring
Summer
Winter
Tuscany
Sardinia
Liguria
Calabria
Campania
Sicily
Puglia
Lazio
Emilia-Romagna
Friuli-Venezia Giulia
Marche
Uncertain location
Advanced decomp. (Code 4)
NA
Fresh (Code 2)
Alive (Code 1)/dead (Code 2)
32
47
24
9
2.70
19.77
35
77
26
9
23
3
34
28
25
25
28
20
18
11
9
9
6
5
2
1
1
2
54
34
15
8
28.57
41.96
21.42
8.03
31.25
68.75
74.28
25.71
88.46
11.53
30.35
25.00
22.32
25.00
17.85
16.07
9.82
8.03
5.35
4.46
1.78
0.89
1.78
48.21
30.35
13.39
7.14
3.2. Spatial Analysis
Most of the mortality events observed in this study occurred in the western sector of
the Mediterranean Sea, and this agrees with current knowledge of a strong west-east longitudinal gradient in the occurrence and numbers of fin whales in the Mediterranean region. Sections of the Italian coastline with higher numbers of mortality events are shown
in Figure 3. Overall, a higher number of mortality events were recorded for the central
and northern coasts of Italy along the Tyrrhenian Sea, along the coast of the Liguria, Tuscany and northern Sardinia regions, along the Coast of Liguria on the Ligurian Sea and,
to a lesser extent, along the coast of Campania and in the area of the Strait of Messina in
the southern portion of the Tyrrhenian Sea. The Adriatic basin, where the species presence
is notably scant, shows relatively few mortality events, in particular along the central and
southern coast of Apulia and in the Gulf of Trieste.
Animals 2022, 12, 3111
10 of 22
Figure 3. The spatial distribution of fin whale mortality events recorded between 1624 and 2021.
Areas with a higher number of mortality events (in dark red) are observed primarily along the coast
of the Liguria, Tuscany and northern Sardinia regions and to a lesser extent, along the coast of Campania and in the area of the Strait of Messina. The Italian coastline is highlighted in red.
3.3. Spatial–Temporal Analysis
The analysis detected four emerging hot spot categories: ‘No pattern’; ‘New hot spot’,
defined as an area that has become a statistically significant hot spot in the last few time
passages; ‘consecutive hot spot’, defined as an area that was a statistically significant hot
spot for a considerable time frame before the final stage; and ‘sporadic hot spot’, defined
as an area that was an on-and-off hot spot throughout the entire period. No ‘Cold spots’
or ‘Decreasing cold spots’ emerged from the analysis. Figure 4 shows the above-mentioned areas overlaid with the Italian peninsula. Consecutive hot spots are evident along
the coast of Tuscany and in southern Italy along the northern coast of Sicily and the southern coast of Calabria, in the Strait of Messina area, as well as in the southwestern Gulf of
Taranto. New hot spots were highlighted along the coast of the Lazio and Campania regions.
Animals 2022, 12, 3111
11 of 22
Figure 4. Emerging hot spots obtained from all mortality events from data compiled between the
years 1624 and 2021 along the Italian coastline. The Italian coastline is highlighted in red.
3.4. Correlation Analysis
The distribution of modern mortality events (Figure 5) highlights two sharp changes
at years 1985 and 2015, respectively, while between 1985 and 2015, there is a relatively
stable number of mortality events. A Kruskal-Wallis rank sum test suggested a statistically
significant difference between the number of mortality events binned at 5-year intervals
for the periods 1970–1985, 1985–2015 and 2015–2021 (chi-squared = 8.4824, df = 2, p-value
= 0.01439). As expected from the distribution of mortality events (Figure 5), statistically
significant differences emerged between the periods 1970–1985 and 1985–2015 (two-sample Kolmogorov-Smirnov test: D = 1, p-value = 0.02381) and the periods 1985–2015 and
2015–2021 (two-sample Kolmogorov-Smirnov test: D = 1, p-value = 0.02381).
Animals 2022, 12, 3111
12 of 22
Figure 5. The frequency distribution of modern mortality events over time. Dashed red, blue, green
and purple lines show the average number of mortality events over the entire period (1964–2021;
the only 1964 record was excluded from the analysis due to lack of information), over the periods
1985–2015, 1970–1985 and 2015–2021, respectively.
As expected, all the temporal variables were highly correlated with Pearson r going
from r = 0.65 (p < 0.0001) in the case of Index and log time to practical identity (r = 0.99; p
< 0.0001) for Index and time.
Latitude and Longitude were moderately correlated (r = −0.54, p < 0.0001) in accordance with the Northwest to Southeast inclination of the Italian peninsula. The correlation
value corresponds to the cosine director of Italy with respect to the north-south meridian
axis and thus is an indirect indication of a sufficiently dense coverage of Italian seas by
the scored events.
The elevated number of records with ‘NA’ for cause of death (n = 77) drastically decreased the statistical power of the Chi-square test in the evaluation of the relation between life stage and death cause (Chi-square = 7.86, p = 0.097). However, we observed that
the main cause of calf mortality was biological (4 out of 7 corresponding to 57.14% of the
class), while ship strikes (14 out of 19) and bycatch (all three recorded events) affected
mainly immature animals.
In order to increase the power of the test, we evaluated the correlation between life
stages and a simplified biological/not biological binary classification of the cause of death,
with the latter reaching a marginal statistical significance: Chi-square= 6.19 (p = 0.045),
Fisher exact test = 0.040) (Table 3).
Table 3. Relative contingency table showing the expected increase in natural/biological causes of
death for calves and a higher number of human related causes of death for immature animals.
Death Cause/Life Stage Sub-Adult/Adult/Mature Immature Fetus/Calf/Newborn
Natural/Biological
3
2
4
Human related
6
17
3
A non-statistically significant correlation between death cause (in both multi-class
and binary versions) and season was observed; and a lack of statistically significant correlation was observed as well for season and life stage, and for season and region. However, in general, summer showed the lowest number of mortality events, while autumn
showed the highest. This trend is particularly evident for the Sardinia region, while the
opposite applies to the Liguria region, where the highest number of events occurred during the summer, as already shown by previous studies [32].
The spatial distribution of historical and modern mortality events from 1973 to 2021
is shown in Figure 6. It is evident that mortality events are distributed uniformly along
the Italian coast, with the highest occurrence along the western coast of Italy and along
the coasts of the islands of Sardinia and Sicily.
Animals 2022, 12, 3111
13 of 22
Figure 6. The overall distribution of both historical and modern mortality events along the Italian
coast.
In order to achieve a quantitative representation of the above condition, we performed a cluster analysis by using a non-hierarchical k-means procedure of the spatial
location of the events in the Latitude-Longitude space. For a consistent Euclidean space,
we limited ourselves to the western basin (Tyrrhenian Sea and Strait of Sicily), which can
be considered as unbiased by the spurious underestimation of distances between points
separated by land (e.g., Tuscany and Emilia-Romagna locations). The clustering procedure produced an optimal spatial classification into three classes with an R-square equal
to 0.84 (to be compared to an R-square = 0.68 in the case of a Gaussian distribution, with
a Pseudo-F value = 256.19, p < 0.0001). The resulting clusters (Figure 7) are the LiguriaTuscany (Cluster 1), Sardinia (Cluster 2), and a southern one (Cluster 3).
Animals 2022, 12, 3111
14 of 22
Figure 7. The spatial representation of the three clusters resulting from the clustering procedure.
Cluster 1 = Liguria-Tuscany cluster, Cluster 2 = Sardinia cluster and Cluster 3 = southern cluster.
It is worth noting that two northern Lazio events joined the Liguria-Tuscany cluster
and two southern Lazio locations went into the southern cluster.
The clusters did not significantly differ amongst each other either for the life stage or
the season of events or death cause, pointing to a common structure of the observed classes.
4. Discussion
Here we present a comprehensive dataset of fin whale mortality events that occurred
along the Italian coast over four centuries between 1624 and the end of 2021, and an associated geo-referenced database (Supplementary Materials). Such a dataset offers a basis
for further investigation into how mortality events of the Mediterranean sub-population
of fin whales are affected by a correlation between the effects of oceanic processes on the
distribution of specimens and the effects of anthropogenic activity.
The structural change analysis of the time series of mortality events highlights the
existence of one single breakpoint—i.e., a single point of significant change in the temporal pattern of mortality—the year 1984 (Cis = 1964–1988). Similar investigations of longtime series events [60] have shown that the sharp increase in mortality of fin whales
emerging in the mid-1980s is most likely artificial, caused by a strong contribution of observer bias in reporting. While the reporting of strandings or mortality events in the past
was highly biased towards populated and/or easily accessible places, modern national
stranding networks tend to distribute their efforts homogeneously across a given area.
The breakpoint year 1984 very closely matches the year of establishment of the CSC in
Animals 2022, 12, 3111
15 of 22
1985 that presented the basis for the first stranding network in Italy and, therefore, the
systematic collection of mortality-related data. Additionally, alongside the systematic effort of a dedicated network, the observed increase in events is likely due to an increase in
the human population and the generalized development of coastal areas through time
[61]. Finally, in Italy, a clear shift in public attitude towards stranded/dead whales
emerged in 1970s and 1980s [60], possibly due to public awareness and conservation efforts. This change in attitude, in turn, could have affected the likelihood of reporting a
stranding or the presence of a floating carcass.
When considering only modern mortality events over the last six decades (events
occurring after 1964), this study shows a regular trend of recorded mortality between 1985
and 2015 with an average of 10 dead animals per year (range = 11–21), and two transition
phases, one increasing and one decreasing, before 1985 and after 2015, respectively, with
a similar average number of events (4 and 3 animals; ranges = 3–4 and 2–5). The distribution of mortality events binned at five-year intervals for 1985–2016 years (Figure 5) is statistically different from the earlier and later periods. The lower number of mortality events
at the beginning of the time series could be explained by the lack of a centralized national
stranding network for the systematic collection of information on mortality. The decrease
in mortality observed after 2015 seems to be independent of a decrease in anthropogenicrelated mortality. Indeed, the last case of bycatch was reported in 1997, while during 2010–
2015, when mortality was still relatively high (16 events), only one ship strike was recorded compared to no ship strikes occurring between 2015 and 2020. While the causes of
the apparent decline remain unknown, a lack of notifications is unlikely, especially for
large cetaceans such as fin whales, in the age of social networks [62]. Ecosystem changes
are a plausible hypothesis as well as the influence of climatic variations. Indeed, both
large-scale climatic changes [16] and ecosystem changes [63] can explain the periodic variability of cetacean stranding and these aspects need further investigation. Moreover, cetacean strandings can be used as indicators of dynamics of cetacean populations at sea
[64] and recent studies have shown that temporal changes in stranding/mortality can be
due to changes in cetacean abundance and distribution [64]. Accordingly, a point to consider and that needs further investigation is the potential link between the observed decrease in mortality and the 10% decline of the Mediterranean population of fin whales,
inferred by comparing large-scale surveys conducted 27 years apart [22,24,25,27,65,66].
The analysis of modern events suggest little seasonal variation (Figure S4, Supplementary Materials) with a peak recorded in autumn and lowest mortality observed in
winter and summer. When considering the sole spatial level, most mortality events occurred along the western coast of Italy and in general across the western Italian seas, mirroring the at-sea distribution of fin whales that is primarily restricted to the western sector
of the Mediterranean basin [41,67,68]. The areas with higher recorded mortality numbers
are located along the coast of Liguria and Tuscany, Campania and Lazio, north of Sardinia
and the area of the Strait of Messina between Sicily and continental Italy. This partially
overlaps with current knowledge of fin whale distribution in the western Mediterranean
Sea [22,65,69]. While the effect of wind and surface currents on the distribution of carcasses at sea and on shore alike is unknown, we cannot exclude the possibility that the
differential distribution of mortality events compared to fine-scale distribution of animals
at sea could result from the effects of oceanic and atmospheric processes [6,16,70]. At the
same time, this differential distribution could suggest a shift in the at-sea distribution of
animals.
When considering spatial–temporal patterns, our analysis highlighted the presence
of several recurring hot spots (i.e., consecutive hot spots) along the central coast of Tuscany, in the Gulf of Trieste, north Adriatic Sea and along the northern coast of the island
of Sardinia. The recurring presence over time of mortality events in these areas could be
due to a regular presence of fin whales in the adjacent waters that could exploit them as
seasonal foraging grounds or migratory corridors. Alternatively, specific characteristics
in marine circulation patterns, coupled with the geomorphology of the sea floor, could
Animals 2022, 12, 3111
16 of 22
facilitate the beaching of fin whales in these areas. We also noted the emergence of mortality hot spots (i.e., new hot spots), along the entire coast of central and southern Italy,
including the northern coast of Sicily and the area of the Strait of Messina as well as the
southern coast of Sardinia. These newly established mortality hot spots could be the result
of a general shift in the distributional range of fin whales. Fin whales migrating from the
southern winter feeding grounds in the Strait of Sicily towards the summer feeding
grounds in the waters of the Pelagos Sanctuary navigate through bodies of water with
increasing volumes of maritime traffic [22], potentially resulting in higher collision rates
and therefore strandings in the southern Italian seas. However, the generalized decrease
in mortality associated with ship strike during the last decades suggests that this anthropogenic cause of mortality only marginally contributed to the observed increase in mortality in the southern Italian seas. Furthermore, it cannot be excluded that the spatial–
temporal emergence of new hot spot areas of mortality can be a direct consequence of
changes in the intensity and location of pressures [64]. Two areas of new hot spots in the
time series were also identified along the western-central and northwestern coast of Italy.
Fin whales are rarely present in the Adriatic Sea [41,71]. Therefore, we can attribute the
position of these modern mortality hot spots to the combined effect of currents, surface
winds, and coastal and bottom morphology that push carcasses into this area or direct
sick animals or animals with difficulties onto specific areas of the coast, which then strand,
dead or alive. Sporadic hot spots were located exclusively along the coast of Liguria and
the northern coast of Tuscany. While the Ligurian Sea and more generally the CorsoLiguro-Provençal basin is a well-known seasonal feeding ground for the species
[32,41,71,72], the relative paucity of strandings in this area can be a consequence of the
characteristics of marine circulation. This study does not show the emergence of any cold
spot, persistent or new, along the Italian coastline, which would indicate a decrease in the
numbers of recorded mortality events over time and space.
Most of the events were of single individuals, with only two cases of mass stranding
involving two animals each, both recorded in the second half of the 1800s. This agrees
with the non-gregarious nature of the species, with animals normally present singly, in
pairs or in small groups [73,74]. However, large temporary aggregations of fin whales
have been reported elsewhere [75]. Unusual mortality events were absent.
Mortality was higher for younger age classes, with mortality of immature individuals
predominantly occurring in spring, being 57.14% higher than for adults and other age
classes. This result agrees with previous studies highlighting that mortality for Mediterranean fin whales tends to be higher in the earlier stage of life (77%) and to decrease with
maturity [76].
It has been suggested that the Mediterranean Sea, compared to the oceans, might
have provided year-round resident fin whales an extended and more flexible calendar of
breeding and feeding opportunities [41], considering the minor predation pressure and a
high potential for socialization due to the relatively small size of the basin. However, increasing pressure from human activity such as vessel traffic, noise, chemical pollution,
and likely climate change, raises concern for the population’s survival.
Mortality appears to be in general high for the Mediterranean sub-population of fin
whales [41] and this is particularly concerning when considering the recent population
size estimates [24]. We did not find evidence of widespread systematic epidemics, compromised immune systems or generalized poor health conditions at the population level
over the study period. While full post-mortem examinations were not performed on all
recovered specimens between 1964 and 2021, evidence suggests that direct mortality derived from anthropogenic activity is likely to result in a decline of the overall population.
When looking at the entire dataset, only four confirmed bycatch events were recorded,
three of which occurred in the last six decades with the last one recorded in 1997. Direct
killings account for 15 of the recorded events. There was never a commercial whaling tradition within the Mediterranean Sea [60,71], and killing of live-stranded animals or ani-
Animals 2022, 12, 3111
17 of 22
mals in difficulty likely occurred opportunistically rather than being the result of a systematic whaling expedition. The majority of killing events in this study were reported in
the second half of the 1800s and the first half of the 1900s, with the last direct killing occurring in 1960 [67]. Additionally, a similar pattern of mortality due to direct killing
emerged for another large cetacean inhabiting the Mediterranean Sea, the sperm whale
Physeter macrocephalus (Linnaeus, 1758) [60]. The sudden stop in this practice, particularly
with large cetaceans, could be the result of education and public awareness campaigns, as
well as conservation efforts, which in turn have led to a change in attitude towards
stranded whales [77]. Amongst the anthropogenic causes of mortality, ship strikes were
the most common. The fin whale is the cetacean species that collides with vessels most
frequently [37]. The Mediterranean basin, in particular its northwestern sector, has been
identified as a collision hot spot for the species [32–36], where it is estimated that between
6% and 20% of Mediterranean fin whales show collision marks [35]. Our results agree with
existing knowledge and show that mortality associated with whale–vessel collisions is in
general high, and involves younger animals in particular [32]. While the dynamics of
whale–vessel collisions are not fully understood, several underlying factors have been
identified [78]. Specifically concerning the higher rate of collisions involving younger animals, the fact that they might spend more time at the surface than an adult increases the
risk of a collision [79]. Moreover, some authors describe the curiosity of younger animals
as a strong contributing factor to increased ship-strike mortality in the earlier life stages
[78]. However, the nature of fin whales as a flight species [80,81], i.e., a species that tends
to swim away from predators or more generally from an actual or perceived threat, provides more support for different hypotheses to explain the higher rate of ship strikes involving younger animals.
Our work shows that in the last decades there has been a putative decrease in whales
dying because of a collision. While carcasses of whales struck by vessels might not always
be recovered and, therefore, the observed decrease would be merely artificial, it is important to highlight that not only the immediate direct death of a whale following a collision is a matter of concern. Recent studies show that up to 20% of living Mediterranean
fin whales show signs of collision [35], with the resulting injury having the potential to
affect swimming and diving patterns, leading to altered feeding habits and potentially
harmful effects in the long term. While the energetic cost of human disturbance has been
assessed in several cases [82,83], similar studies are not yet available for animals that have
suffered collisions, and to date, it remains unclear whether the long-term effects of nonlethal collisions on Mediterranean fin whales should be considered a costly unnatural
threat rather than a short-term incident. Given the relatively high number of animals that
died from collisions in our study, we further stress the need to effectively mitigate the
threats posed by ship strikes and to reduce the risk of collision through appropriate management measures, in particular within the framework of the International Maritime Organization.
5. Conclusions
Investigations of time series of mortality events can complement existing approaches
to gain knowledge on the biology, ecology and conservation status of species, with the
benefit of providing a unique source of information for rare and difficult-to-access taxa. It
is crucial to highlight that the interpretation of the outcome of these types of studies, in
particular concerning the spatial and temporal patterns, must be taken with extreme caution. In the case of cetaceans, the lack of a robust probabilistic sampling of stranding
events, associated with different reporting efforts over time, could lead to biased results.
However, in this study, the descriptive analytical approach renders the results useful to
support informed management and conservation measures. Furthermore, this study
strongly supports the importance of a centralized national stranding network as well as
the creation and development of detailed datasets on species mortality. At the same time,
it also highlights that in Italy, despite four decades of systematic collection of mortality
Animals 2022, 12, 3111
18 of 22
data, often the proximal causes of mortality remain unknown. It thus emphasizes the need
to develop integrated and standardized data collection procedures which allow the assessment of the health status of the Mediterranean sub-population of fin whales.
Existing knowledge suggests that current mortality rates for Mediterranean fin
whales might be unsustainable, in particular in light of the recent abundance estimates
across the basin. Direct mortality associated with ship strikes remains high, and there is a
lack of information on the potential detrimental long-term population effects of non-lethal
whale–vessel collisions. Alongside ship strikes, the combined effects of high levels of
chemical pollution [38,84], the ingestion of high volumes of microplastics [85–87], and the
impact of underwater noise pollution [42] further stress the necessity of developing an
overarching conservation and management plan (CMP) for the species in the Mediterranean Sea. In this context, the International Whaling Commission and the Agreement on
the Conservation of Cetaceans of the Black Sea, Mediterranean Sea and contiguous Atlantic area are in the process of developing this much-needed CMP with the overall aim of
managing human activity afflicting Mediterranean fin whales and supporting their
favorable conservation status across the entire range of distribution within the region. Our
study provides relevant input to the development of this CMP.
Supplementary Materials: The following is available online at https://www.mdpi.com/article/10.3390/ani12223111/s1, Table S1: Dataset 1624–2021 (Excel file), Figures S1–S4.
Author Contributions: Conceptualization, V.M., N.P., N.M. and A.M.; methodology, V.M., N.P.
and A.G.; software, N.P. and A.G.; formal analysis, V.M., N.P., A.G., N.M. and A.M.; investigation,
V.M., N.P., F.D.P. and N.M.; data curation, V.M., N.P., F.D.P. and N.M.; writing—original draft
preparation, V.M.; writing—review and editing, V.M., N.P., F.D.P., A.G., N.M. and A.M.; supervision, V.M. and A.M.; project administration, V.M. and A.M. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Collected data used for the analyses in this study are provided as
Supplementary Materials to this paper.
Acknowledgments: We are grateful to all directors of the museums, the managers of the bone collections, Francesco Pollaro (CSEM), and Lydia O’Loughlin (Ship Strikes and Strandings Data Manager) of the International Whaling Commission for their helpful assistance. Thanks to Sonja “Pine”
Eisfeld-Pierantonio for her help with English language and proof reading. Finally, we are grateful
to the reviewers for their thoughtful comments on the manuscript, which greatly improved the
quality of our work.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
2.
3.
4.
5.
Coll, M.; Piroddi, C.; Steenbeek, J.; Kaschner, K.; Ben Rais Lasram, F.; Aguzzi, J.; Ballesteros, E.; Bianchi, C.N.; Corbera, J.;
Dailianis, T.; et al. The Biodiversity of the Mediterranean Sea: Estimates, Patterns, and Threats. PLoS ONE 2010, 5, e11842.
https://doi.org/10.1371/journal.pone.0011842.
Pyenson, N.D. Carcasses on the coastline: Measuring the ecological fidelity of the cetacean stranding record in the eastern North
Pacific Ocean. Paleobiology 2010, 36, 453–480.
Pyenson, N.D. The high fidelity of the cetacean stranding record: Insights into measuring diversity by integrating taphonomy
and macroecology. Proc. R. Soc. B 2011, 278, 3608–3616.
Coombs, E.J.; Deaville, R.; Sabin, R.C.; Allan, L.; O’Connell, M.; Berrow, S.; Smith, B.; Brownlow, A.; Doeschate, M.T.; Penrose,
R.; et al. What can cetacean stranding records tell us? A study of UK and Irish cetacean diversity over the past 100 years. Mar.
Mam. Sci. 2019, 35, 1527–1555.
Mazzariol, S.; Siebert, U.; Scheinin, A.; Deaville, R.; Brownlow, A.; Uhart, M.; Marcondes, M.; Hernandez, G.; Stimmelmayr, R.;
Rowles, T.; et al. Summary of Unusual Cetaceans Strandings Events worldwide (2018-2020); SC-68B/E/09 Rev1; 2020.
Animals 2022, 12, 3111
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
19 of 22
IJsseldijka, L.L.; ten Doeschate, M.T.I.; Brownlow, A.; Davison, N.J.; Deaville, R.; Galatius, A.; Gilles, A.; Haelters, J.; Jepson,
P.D.; Keijl, G.O.; et al. Spatiotemporal mortality and demographic trends in a small cetacean: Strandings to inform conservation
management. Biol. Cons. 2020, 249, 108733.
Li, W.T.; Chou, L.S.; Chiou, H.Y.; Chen, I.H.; Yang, W.C. Analyzing 13 Years of Cetacean Strandings: Multiple Stressors to
Cetaceans in Taiwanese Waters and Their Implications for Conservation and Future Research. Front. Mar. Sci. 2021, 8, 606722.
https://doi.org/10.3389/fmars.2021.606722.
Betty, E.L.; Stockin, K.A.; Hinton, B.; Bollard, B.A.; Smith, A.N.H.; Orams, M.B.; Murphy, S. Age, growth, and sexual dimorphism of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii). J. Mammal. 2022, 103, 560–575.
Reddy, M.L.; Dierauf, L.A.; Gulland, F.M.D. Marine Mammals as Sentinels of Ocean Health in CRC Handbook of Marine Mammal
Medicine, 2nd ed.; Dierauf, L.A., Gulland, F.M.D., Eds.; CRC Press: Boca Raton, FL, USA, 2001.
Cornaglia, E.; Rebora, L.; Gili, C.; Di Guardo, G. Histopathological and immunohistochemical studies on cetaceans found
stranded on the coast of Italy between 1990 and 1997. J. Vet. Med. A Physiol. Pathol. Clin. Med. 2000, 47, 129–142.
Bonar, C.J.; Boede, E.O.; Hartmann, M.G.; Lowenstein-Whaley, J.; Mujica-Jorquera, E.; Parish, S.V.; Parish, J.V.; Garner, M.M.;
Stadler, C.K. A retrospective study of pathologic findings in the Amazon and Orinoco river dolphin (Inia geoffrensis) in captivity.
J. Zoo Wildl. Med. 2007, 38, 177–191.
Diaz-Delgado, J.; Fernandez, A.; Sierra, E.; Sacchini, S.; Andrada, M.; Vela, A.I.; Quesada-Canales, Ó.; Paz, Y.; Zucca, D.; Groch,
K.; et al. Pathologic findings and causes of death of stranded cetaceans in the Canary Islands (2006–2012). PLoS ONE 2018, 13,
e0204444.
Puig-Lozano, R.; Bernaldo, R.; De Quirós, Y.; Díaz-Delgado, J.; García-Ñlvarez, N.; Sierra, E.; De la Fuente, J.; Sacchini, S.; SuárezSantana, C.M.; Zucca, D.; et al. Retrospective study of foreign body-associated pathology in stranded cetaceans, Canary Islands
(2000-2015). Environ. Pollut. 2018, 43, 519–527.
Klinowska, M. Cetacean live strandings relate to geomagnetic topography. Aquat. Mamm. 1985, 1, 27–32.
Brabyn, M.W.; McLean, I.G. Oceanography and coastal topography of herd-stranding sites for whales in New Zealand. J. Mamm.
1992, 73, 469–476.
Evans, K.; Thresher, R.; Warneke, R.M.; Bradshaw, C.J.A.; Pook, M.; Thiele, D.; Hindell, M.A. Periodic variability in cetacean
strandings: Links to large-scale climate events. Biol. Lett. 2005, 1, 147–150.
Quaggiotto, M.M.; Sánchez-Zapata, J.A.; Bailey, D.M.; Payo-Payo, A.; Navarro, J.; Brownlow, A.; Deaville, R.; Lambertucci, S.A.;
Selva, N.; Cortés-Avizanda, A.; et al. Past, present and future of the ecosystem services provided by cetacean carcasses. Ecosyst.
Serv. 2022, 54, 101406.
Bérubé, M.; Aguilar, A.; Dendanto, D.; Larsen, F.; Notarbartolo di Sciara, G.; Sears, R.; Sigurjónsson, J.; Urban-R, J.; Palsbøll, P.J.
Population genetic structure of North Atlantic, Mediterranean Sea and Sea of Cortez fin whales, Balaenoptera physalus (Linnaeus 1758): Analysis of mitochondrial and nuclear loci. Mol. Ecol. 1998, 7, 585–599.
Palsbøll, P.J.; Bérubé, M.; Aguilar, A.; Notarbartolo Di Sciara, G.; Nielsen, R. Discerning between recurrent gene flow and recent
divergence under a finite-site mutation model applied to North Atlantic and Mediterranean Sea fin whale (Balaenoptera physalus) populations. Evolution 2004, 58, 670–675.
Notarbartolo di Sciara, G.; Agardy, T.; Hyrenbach, D.; Scovazzi, T.; Van Klaveren, P. The Pelagos sanctuary for 570 Mediterranean marine mammals. Aquat. Conserv. Mar. Freshw. Ecosyst. 2008, 18, 367–391.
Canese, S.; Cardinali, A.; Fortuna, C.; Giusti, M.; Lauriano, G.; Salvati, E.; Greco, S. The first identified winter feeding ground
of fin whales (Balaenoptera physalus) in the Mediterranean Sea. J. Mar. Biol. Assoc. U. K. 2006, 86, 903–907.
https://doi.org/10.1017/S0025315406013853.
Panigada, S.; Lauriano, G.; Donovan, G.; Pierantonio, N.; Cañadas, A.; Vázquez, J.A.; Burt, L. Estimating cetacean density and
abundance in the Central and Western Mediterranean Sea through aerial surveys: Implications for management. Deep. Sea Res.
Part II Top. Stud. Oceanogr. 2017, 141, 41–58.
Pintore, L.; Sciacca, V.; Viola, S.; Giacoma, C.; Papale, E.; Giorli, G. Fin Whale (Balaenoptera physalus) in the Ligurian Sea:
Preliminary Study on Acoustics Demonstrates Their Regular Occurrence in Autumn. J. Mar. Sci. Eng. 2021, 9, 966.
ACCOBAMS. Estimates of Abundance and Distribution of Cetaceans, Marine Mega-Fauna and Marine Litter in the Mediterranean Sea
from 2018-2019 Surveys; Panigada, S., Boisseau, O., Canadas, A., Lambert, C., Laran, S., McLanaghan, R., Moscrop, A., Eds.;
ACCOBAMS—ACCOBAMS Survey Initiative Project: Monaco, 2021; 177p.
Forcada, J.; Agiolar, A.; Hammond, P.; Pastor, X.; Aguilar, R. Distribution abundance of fin whales (Balaenoptera physalus) in the
western Mediterranean Sea during the summer. J. Zool. 1996, 238, 23–24.
Forcada, J.; Notarbartolo di Sciara, G.; Fabbri, F. Abundance of fin whales and striped dolphins summering in the Corso Ligurian basin. Mammalia 1995, 59, 127–140.
Panigada, S.; Gauffier, P.; Notarbartolo di Sciara, G. Balaenoptera physalus (Mediterranean subpopulation). In The IUCN Red
List of Threatened Species; 2021; e.T16208224A50387979. Available online: https://doi.org/10.2305/IUCN.UK.20213.RLTS.T16208224A50387979.en (accessed on 28 July 2022).
Mazzariol, S.; Marcer, F.; Mignone, W.; Serracca, L.; Goria, M.; Marsili, L.; Di Guardo, G.; Casalone, C. Dolphin morbillivirus
and Toxoplasma gondii coinfection in a Mediterranean fin whale (Balaenoptera physalus). BMC Vet. Res. 2012, 8, 20.
Casalone, C.; Mazzariol, S.; Pautasso, A.; Di Guardo, G.; Di Nocera, F.; Lucifora, G.; Ligios, C.; Franco, A.; Fichi, G.; Cocumelli,
C.; et al. Cetacean strandings in Italy: An unusual mortality event along the Tyrrhenian Sea coast in 2013. Dis. Aquat. Organ.
2014, 109, 81–86.
Animals 2022, 12, 3111
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
20 of 22
Mazzariol, S.; Centelleghe, C.; Beffagna, G.; Povinelli, M.; Terracciano, G.; Cocumelli, C.; Pintore, A.; Denurra, D.; Casalone, C.;
Pautasso, A.; et al. Mediterranean Fin Whales (Balaenoptera physalus) Threatened by Dolphin MorbilliVirus. Emerg. Infect. Dis.
2016, 22, 302–305.
Mira, F.; Rubio-Guerri, C.; Purpari, G.; Puleio, R.; Caracappa, G.; Gucciardi, F.; Russotto, L.; Loria, G.R.; Guercio, A. Circulation
of a novel strain of dolphin morbillivirus (DMV) in stranded cetaceans in the Mediterranean Sea. Sci. Rep. 2019 9, 9792.
Panigada, S.; Pesante, G.; Zanardelli, M.; Capoulade, F.; Gannier, A.; Weinrich, M.T. Mediterranean fin whales at risk from fatal
ship strikes. Mar. Pollut. Bull. 2006, 52, 1287–1298.
Peltier, H.; Beaufils, A.; Cesarini, C.; Dabin, W.; Dars, C.; Demaret, F.; Dhermain, F.; Doremus, G.; Labach, H.; Van Canneyt, O.;
et al. Monitoring of Marine Mammal Strandings Along French Coasts Reveals the Importance of Ship Strikes on Large Cetaceans: A Challenge for the European Marine Strategy Framework Directive. Front. Mar. Sci. 2019, 6, 486.
Ritter, F.; Panigada, S. Collisions of Vessels with Cetaceans-the Underestimated Threat World Seas: An Environmental Evaluation Volume
III: Ecological Issues and Environmental Impacts; Elsevier: Amsterdam, The Netherlands, 2019; pp. 531–547.
Panigada, S.; Azzellino, A.; Cubaynes, H.; Folegot, T.; Fretwell, P.; Jacob, T.; Lanfredi, C.; Leaper, R.; Ody, D.; Ratel, M. Proposal
to Develop and Evaluate Mitigation Strategies to Reduce the Risk of Ship Strikes to Fin and Sperm Whales in the Pelagos Sanctuary—Final
Report; Pelagos Secretariat—Convention No. 2018-04; 2020; 67p.
Winkler, C.; Panigada, S.; Murphy, S.; Ritter, F. Global Numbers of Ship Strikes: An Assessment of Collisions Between Vessels and
Cetaceans Using Available Data in the IWC Ship Strike Database; Report to the International Whaling Commission, IWC/68B/SC
HIM09 Rev1; 2020; 33p.
Sèbe, M.; Kontovas, C.A.; Pendleton, L.; Gourguet, S. Cost-effectiveness of measures to reduce ship strikes: A case study on
protecting the Mediterranean fin whale. Sci. Total Environ. 2022, 827, 154236.
Fossi, M.C.; Marsili, L.; Neri, G.; Natoli, A.; Politi, E.; Panigada, S. The use of a non-lethal tool for evaluating toxicological hazard
of organochlorine contaminants in Mediterranean cetaceans: New data 10 years after the first paper published in MPB. Mar.
Pollut. Bull. 2006, 46, 972–982.
Mancia, A.; Abelli, L.; Fossi, M.C.; Panti, C. Skin distress associated with xenobiotics exposure: An epigenetic study in the
Mediterranean fin whale (Balaenoptera physalus). Mar. Gen. 2021, 57, 100822.
Pinzone, M.; Budzinski, H.; Tasciotti, A.; Ody, D.; Lepoint, G.; Schnitzler, J.; Scholl, G.; Thomé, J.P.; Tapie, N.; Eppe, G.; et al.
POPs in free-ranging pilot whales, sperm whales and fin whales from the Mediterranean Sea: Influence of biological and ecological factors. Environ. Res. 2015, 142, 185–196.
Notarbartolo di Sciara, G.; Castellote, M.; Druon, J.N.; Panigada, S. Fin Whales, Balaenoptera physalus: At Home in a Changing
Mediterranean Sea? Adv. Mar. Biol. 2016, 75, 75–101.
Castellote, M.; Clark, C.W.; Lammersc, M.O. Acoustic and behavioural changes by fin whales (Balaenoptera physalus) in response to shipping and airgun noise. Biol. Conserv. 2012, 147, 115–122.
Gambaiani, D.; Mayol, P.; Isaac, S.; Simmonds, M. Potential impacts of climate change and greenhouse gas emissions on Mediterranean marine ecosystems and cetaceans. J. Mar. Biol. Assoc. UK 2009, 89, 179–201.
Borri, M. Una rete per i delfini. Cetacea Inf. 1995, IV, 42–44.
Di Lorenzo, A.; Olivieri, V.; Internullo, E.; Bortolotto, A.; Manfrini, V.; Guccione, S.; Piscione, I.; Di Nardo, W.; Tringali, M.
GeoCetus: Sistema informativo geografico per la gestione di una banca dati online degli spiaggiamenti di cetacei lungo le coste
italiane. Biol. Mar. Mediterr. 2013, 20, 256–257.
Tomilin, A.G. Cetacea. In Mammals of the U.S.S.R. and Adjacent Countries; Heptner, V.G., Ed.; Israel Program for Scientific Translations, 1967; Jerusalem, Israel, 1957; Volume 9, 756p.
Robineau, D. Cétacés de France. Faune de France Volum. 89; Féderation Française des Sociétés de Sciences Naturelle: Paris, France,
2005; 646p.
Pyenson, N.D.; Goldbogen, J.A.; Shadwick, R.E. Mandible allometry in extant and fossil Balaenopteridae (Cetacea: Mammalia):
The largest vertebrate skeletal element and its role in rorqual lunge feeding. Biol. J. Linn. Soc. 2013, 108, 586–599.
https://doi.org/10.1111/j.1095-8312.2012.02032.x
Mogoe, T.; Bando, T.; Maeda, H.; Kato, H.; Ohsumi, S. Biological Observations of fin Whales Sampled by JARPAII in the Antarctic;
SC/F14/J10; ICR: 2014.
Rossi, A.; Panigada, S.; Arrigoni, M.; Zanardelli, M.; Cimmino, C.; Marangi, L.; Manfredi, P.; Santangelo, G. Demography and
conservation of the Mediterranean fin whale (Balaenoptera physalus): What clues can be obtained from photo-identification data.
Theor. Biol. Forum 2014, 107, 123–142.
Ohsumi, S. Examination on age determination of the fin whale. Sci. Rep. Whales Res. Inst. 1964, 18, 49–88.
Geraci, J.R.; Lounsbury, V.J. Marine Mammals Ashore: A Field Guide for Strandings, 2nd ed.; Texas A&M University Sea Grant
Publication: College Station, TX, USA, 2005; Volume 1, p. 344.
Fioravanti, T.; Maio, N.; Latini, L.; Splendiani, A.; Guarino, F.M.; Mezzasalma, M.; Petraccioli, A.; Cozzi, B.; Mazzariol, S.;
Centelleghe, C.; et al. Nothing is as it seems: Genetic analyses on stranded fin whales unveil the presence of a fin-blue whale
hybrid in the Mediterranean Sea (Balaenopteridae). Eur. Zool. J. 2022, 89, 590–600. https://doi.org/10.1080/24750263.2022.2063426
Zeileis, A.; Leisch, F.; Hornik, K.; Kleiber, C. Strucchange: An R Package for Testing for Structural Change in Linear Regression
Models. J. Stat. Softw. 2002, 7, 1–38.
Zeileis, A.; Kleiber, C.; Krämer, W.; Hornik, K. Testing and Dating of Structural Changes in Practice. Comput. Stat. Data Anal.
2003, 44, 109–123.
Animals 2022, 12, 3111
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
21 of 22
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing, Vienna, Austria,
2021. Available online: https://www.R-project.org/ (accessed on).
Hamed, K.H. Exact distribution of the Mann-Kendall trend test statistic for persistent data. J. Hydrol. 2009, 365, 86–94.
Getis, A.; Ord, J.K. The Analysis of Spatial Association by Use of Distance Statistics. In Perspectives on Spatial Data Analysis.
Advances in Spatial Science; Anselin, L., Rey, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2010.
Ord, J.K.; Getis, A. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geogr. Anal. 1995, 27, 286–
306.
Bearzi, G.; Pierantonio, N.; Affronte, M.; Holcer, D.; Maio, N.; Notarbartolo di Sciara, G. Overview of sperm whale Physeter
microcephalus mortality events in the Adriatic Sea, 1555–2009. Mamm. Rev. 2011, 41, 276–293. https://doi.org/10.1111/j.13652907.2010.00171.x
UNEP/MAP. Mediterranean Strategy for Sustainable Development 2016–2025; Plan Bleu, Regional Activity Centre: Valbonne,
France, 2016.
Pace, D.S.; Giacomini, G.; Campana, I.; Paraboschi, M.; Pellegrino, G.; Silvestri, M.; Alessi, J.; Angeletti, D.; Cafaro, V.; Pavan,
G.; et al. An integrated approach for cetacean knowledge and conservation in the central Mediterranean Sea using research and
social media data sources. Aquatic. Conserv. Mar. Freshw. Ecosyst. 2019, 29, 1302–1323.
Moore, J.E.; Barlow, J.P. Declining Abundance of Beaked Whales (Family Ziphiidae) in the California Current Large Marine
Ecosystem. PLoS ONE 2013, 8, e52770.
Peltier, H.; Dabin, W.; Daniel, P.; Canneyt, OV.; Dorémus, G.; Huon, M.; Ridoux, V. The significance of stranding data as indicators of cetacean population at sea: Modelling the drift of cetacean carcasses. Ecol. Indic. 2012, 18, 278–290.
Panigada, S.; Lauriano, G.; Burt, L.; Pierantonio, N.; Donovan, G. Monitoring winter and summer abundance of cetaceans in the
Pelagos Sanctuary (northwestern Mediterranean Sea) through aerial surveys. PLoS ONE 2011, 6, e22878.
Panigada, S.; Araujo, H.; Belmont, J.; Cañadas, A.; David, L.; Di-Méglio, N.; Dorémus, G.; Gonzalvo, J.; Holčer, D.; Laran, S.; et
al. The ACCOBAMS Survey Initiative: The first synoptic survey of the Mediterranean Sea. In Proceedings of the World Marine
Mammal Conference, Barcelona, Spain, 9–12 December 2019.
Pierantonio, N.; Bearzi, G. Review of fin whale mortality events in the Adriatic Sea (1728–2012), with a description of a previously unreported killing. Mar. Biodiv. Rec. 2012, 5, e109.
Stephens, G.; Akkaya Bas, A.; Hardy, J.; Awbery, T.; Rudd, L.; Arac, N.; Lyne, P. Sightings and stranding reports of fin whales
(Balaenoptera physalus) in the Levantine Sea. J. Cetacean Res. Manag. 2021, 22, 55–59.
Sciacca, V.; Caruso, F.; Beranzoli, L.; Chierici, F.; De Domenico, E.; Embriaco, D.; Favali, P.; Giovanetti, G.; Larosa, G.; Marinaro,
G.; et al. Annual Acoustic Presence of Fin Whale (Balaenoptera physalus) Offshore Eastern Sicily, Central Mediterranean Sea.
PLoS ONE 2015, 10, e0141838. https://doi.org/10.1371/journal.pone.0141838.
Zellar, R.; Pulkkinen, A.; Moore, K.; Rousseaux, C.S.; Reeb, D. Oceanic and Atmospheric Correlations to Cetacean Mass Stranding Events in Cape Cod, Massachusetts, USA. Geophys. Res. Lett. 2021, 48, e2021GL093697.
Notarbartolo di Sciara, G.; Bearzi, G. Cetacean direct killing and live capture in the Mediterranean Sea. In Cetaceans in the Mediterranean and Black Seas: State of Knowledge and Conservation Strategies; Notarbartolo di Sciara, G., Ed.; ACCOBAMS: Monaco,
2002.
Druon, J.N.; Panigada, S.; David, L.; Gannier, A.; Mayol, P.; Arcangeli, A.; Cañadas, A.; Laran, S.; Di Méglio, N.; Gauffier, P.
Potential feeding habitat of fin whales in the western Mediterranean Sea: An environmental niche model. Mar. Ecol. Prog. Ser.
2012, 464, 289–306. https://doi.org/10.3354/meps09810.
Gambell, R. Fin Whale Balaenoptera physalus (Linnaeus, 1758). In Handbook of Marine Mammals. The Sirenians and Baleen Whales;
Ridgway, S.H., Harrison, R., Eds.; Academic Press: London, UK, 1985; Volume 3, pp. 171–192.
Zanardelli, M.; Airoldi, S.; Bérubé, M.; Borsani, J.F.; Di-Meglio, N.; Gannier, A.; Hammond, P.S.; Jahoda, M.; Lauriano, G.;
Notarbartolo di Sciara, G.; et al. Long-term photo-identification study of fin whales in the Pelagos Sanctuary (NW Mediterranean) as a baseline for targeted conservation and mitigation measures. Aquatic. Conserv. 2022, 32, 1457–1470.
Herr, H.; Viquerat, S.; Devas, F.; Lees, A.; Wells, L.; Gregory, B.; Giffords, T.; Beecham, D.; Meyer, B. Return of large fin whale
feeding aggregations to historical whaling grounds in the Southern Ocean. Sci. Rep. 2022, 12, 9458.
Arrigoni, M.; Manfredi, P.; Panigada, S.; Bramanti, L.; Santangelo, G. Life-history tables of the Mediterranean fin whale from
stranding data. Mar. Ecol. 2011, 32, 1–9.
Bearzi, G.; Pierantonio, N.; Bonizzoni, S.; Notarbartolo di Sciara, G.; Demma, M. Perception of a cetacean mass stranding in
Italy: The emergence of compassion. Aquat. Conserv. Mar. Freshw. Ecosyst. 2010, 20, 644–654.
Dolman, S.; Williams-Grey, V.; Asmutis-Silvia, R.; Isaac, S. Vessel Collisions and Cetaceans: What Happens If They Don’t Miss the
Boat; Science Report; Whale and Dolphin Conservation Society: Wiltshire, UK, 2006.
Laist, D.W.; Knowlton, A.R.; Mead, J.G.; Collet, A.S.; Podesta, M. Collisions between ships and whales. Mar. Mam. Sci. 2001, 17,
35–75.
Jefferson, T.A.; Stacey, P.J.; Baird, R.W. A review of Killer Whale interactions with other marine mammals: Predation to coexistence. Mamm. Rev. 1991, 21, 151–180.
Ford, J.K.B.; Reeves, R.R. Fight or flight: Antipredator strategies of baleen whales. Mamm. Rev. 2008, 38, 50–86.
Williams, R.; Lusseauc, D.; Hammond, P.H. Estimating relative energetic costs of human disturbance to killer whales (Orcinus
orca). Biol. Conserv. 2006, 133, 301–311.
Van der Hoop, J.; Corkeron, P.; Moore, M. Entanglement is a costly life-history stage in large whales. Ecol. Evol. 2017, 7, 92–106.
Animals 2022, 12, 3111
84.
85.
86.
87.
22 of 22
Fossi, M.C.; Casini, S.; Marsili, L. Potential toxicological hazard due to endocrine-disrupting chemicals on Mediterranean top
predators: State of art, gender differences and methodological tools. Environ. Res. 2007, 104, 174–182. https://doi.org/10.1016/j.envres.2006.06.014.
Fossi, M.C.; Panti, C.; Guerranti, C.; Coppola, D.; Giannetti, M.; Marsili, L.; Minutoli, R. Are baleen whales exposed to the threat
of microplastics? A case study of the Mediterranean fin whale (Balaenoptera physalus). Mar. Pollut. Bull. 2012, 64, 2374–2379.
https://doi.org/10.1016/j.marpolbul.2012.08.013.
Cózar, A.; Sanz-Martín, M.; Martí, E.; González-Gordillo, J.I.; Ubeda, B.; Gálvez, J.Á.; González-Gordillo, J.I.; Ubeda, B.; Gálvez,
J.Á.; Irigoien, X.; Duarte, C.M. Plastic Accumulation in the Mediterranean Sea. PLoS ONE 2015, 10, e0121762.
https://doi.org/10.1371/journal.pone.0121762
Fossi, M.C.; Marsili, L.; Baini, M.; Giannetti, M.; Coppola, D.; Guerranti, C.; Caliani, I.; Minutoli, R.; Lauriano, G.; Finoia, M.G.;
et al. Fin whales and microplastics: The Mediterranean Sea and the Sea of Cortez scenarios. Environ. Pollut. 2016, 209, 68–78.
https://doi.org/10.1016/j.envpol.2015.11.022.