Abundance and Distribution of Cetaceans

Cetaceans are an important component of marine biodiversity in the North-East Atlantic Ocean, as is clearly shown by their distribution and density (Figure a). Data from the systematic large-scale surveys (e.g., Small Cetaceans in the European Atlantic and North Sea SCANS in 1994; SCANS-II in 2005, SCANS-III in 2016; Cetacean Offshore Distribution and Abundance in the European Atlantic CODA in 2007; etc.) have been analysed using models that relate the density of species to features of their habitat to produce maps of predicted density distribution in the North-East Atlantic Ocean. Cetaceans are distributed widely across this area and the relatively small amount of overlap in predicted high-use areas highlights how the different species utilise the marine area in different ways, at least in summer. These and other similar data have been analysed to provide abundance estimates, and trends when possible.

Results of these analyses show that whales, dolphins, and porpoises are abundant throughout the North-East Atlantic Ocean. Although 36 species of cetacean have been recorded within the Greater North Sea, Celtic Seas, and the Bay of Biscay and Iberian Coast (Waring et al., 2009; Evans 2020; https://www.ascobans.org/), many are widely dispersed oceanic species that are rarely seen in these waters and, thus, very difficult to monitor systematically, regularly, and frequently. For instance, the current European Red List (as of spring 2022) has a majority of cetacean species classified as data deficient (https://ec.europa.eu/environment/nature/conservation/species/redlist/mammals/status.htm). Accordingly, this Indicator Assessment is mainly dedicated to assessing those species for which consistently robust information on abundance and distribution is available.

The status of the following species is assessed: harbour porpoise (Phocoena phocoena). For this species an Indicator Assessment is possible because it hasdefined Assessment Units (AU) in the OSPAR Maritime Area and enough data to calculate at least three absolute abundance estimates. Coastal bottlenose dolphins (Tursiops truncatus) were assessed in five AUs, for which more than four estimates of abundance over a period of thirty years are available. No assessment could be made for the remaining AUs, because fewer than four abundance estimates during the last thirty years are available.

The status of the following species, for which AUs are defined but fewer than three estimates of abundance are available, is discussed: minke whale (Balaenoptera acutorostrata), fin whale (Balaenoptera physalus), offshore population of bottlenose dolphin , white-beaked dolphin (Lagenorhynchus albirostris), white-sided dolphin (Lagenorhynchus acutus), and short-beaked common dolphin (Delphinus delphis).
No assessment could be carried out on striped dolphin (Stenella coeruleoalba), Risso’s dolphin (Grampus griseus), long-finned pilot whale (Globicephala melas), sperm whale (Physeter macrocephalus), and beaked whales (as a combined species group, Ziphiidae) because there are no AUs defined for these species and fewer than three estimates of abundance are available. Their status with respect to distribution and abundance in the OSPAR Maritime Area is, however, discussed.

Killer whale (Orcinus orca) is not assessed, owing to a lack of additional data since the Pilot Assessment on Abundance and Distribution of killer whales in IA 2017. A review of killer whale populations in the North Atlantic identified three genetically distinguishable populations in the North-East Atlantic, each associated to a major fish stock (Jourdain et al., 2019). However, further photo-identification effort and genetic studies are required to delineate and define AUs.
Densities of killer whales are lower in OSPAR Regions II, III and IV where the indicator is common: few sightings of this species are registered as part of the large-scale surveys undertaken in these OSPAR Regions. In contrast, in Region I, the North Atlantic Sighting Surveys (NASS) estimated approximatively 15 000 killer whales (CV = 0,42) in 2001 (Jourdain et al., 2019). No abundance estimates for Regions II, III or IV are currently available, but small, localized populations have been identified (e.g. Gibraltar Strait, off the West Coast of the British Isles, Iberian Peninsula). Photographic capture-recapture studies are needed to obtain population size for these small, localized populations: large scale surveys have low coverage probability and are not designed to capture small-scale features in distribution and abundance, especially in areas of low abundance and / or patchy distribution (Hammond et al., 2014).

Human pressures

Cetaceans are subject to a range of human pressures, some of which have been shown to have negative impacts at the individual level, ranging from increased stress and higher energetic costs, through sub-lethal effects on reproduction and immune function, to mortality. Effects may manifest at population level (Hammond et al., 2008; MAGRAMA, 2012; JNCC, 2013; Pirotta et al., 2018; ICES 2019). An overview of drivers, pressures and activities impacting marine mammals is given in The Marine Mammals Thematic Assessment .

Historical data on abundance and distribution

There is very little information about historical distribution (pre-1980) for any species of cetacean in the area assessed and no information about historical abundance (save for catch statistics of whales; e.g., Brown 1976; Smith et al. 2012; https://iwc.int/whaling). Methods to estimate cetacean abundance were first developed in the 1980s (Hammond, 1986; Hiby and Hammond, 1989). Development in analytical and survey methods continues (e.g., Bravington et al., 2021). The most recent methods are developed during the SCANS surveys (Hammond et al., 2002, 2013, 2021).

Data on strandings have been recorded for more than 100 years, but these can provide only very limited information on the presence at sea of offshore species, and it is not possible to use these data to infer population trends without additional information on causes of death, search effort and drifting of dead animals (Authier et al., 2014; Ten Doeschate et al., 2018; ICES 2021). In some cases, strandings data can provide information about distribution and occurrence, such as with harbour porpoise, for which stranding numbers inclined steeply in the Southern North Sea due to a shift in distribution, with a relatively high level of strandings to date (IJsseldijk et al., 2020).

Quality Status Report 2010 and Intermediate Assessment 2017

No comparable quantitative assessment of the distribution and abundance of wide-ranging cetaceans was included in the Quality Status Report 2010. The report highlighted human pressures on marine mammals more generally, such as by-catch, increased shipping and bioaccumulation of persistent hazardous substances. The assessment is based on updated abundance estimates from the Intermediate Assessment 2017, and a modelling approach integrating the IA 2017 data with regional survey data for wide ranging species up to 2020.  For coastal bottlenose dolphin populations, the IA 2017 data were updated to 2020. After IA 2017 a threshold value for M4 abundance was approved and applied in the current assessment.  
Wide-ranging cetaceans are also assessed under the European Union Habitats Directive Article 17 reporting, latest in 2018 (https://www.eea.europa.eu/publications/state-of-nature-in-the-eu-2020/at_download/file; EEA, 2020).

Methods

The data used to infer distribution and to estimate abundance of wide-ranging species were mostly derived from dedicated aerial and shipboard surveys that used line-transect methodology to generate robust estimates of abundance: SCANS (Small Cetacean Abundance in the North Sea; Hammond et al., 2002), SCANS-II (Small Cetacean Abundance in the European Atlantic and North Sea; Hammond et al., 2013), CODA (Cetacean Offshore Distribution and Abundance in the European Atlantic; CODA, 2009) and SCANS-III (Hammond et al., 2021). Smaller-scale (mostly national) surveys have been conducted using the same or a similar methodology, such as in Belgium, Denmark, France, Germany, Ireland, the Netherlands, and Sweden (Scheidat et al., 2008; Viquerat et al., 2014; Gilles et al., 2016; Laran et al., 2017; Rogan et al., 2018). The large-scale surveys provide information on distribution and abundance over a large area but are infrequent, and only undertaken in summer. The smaller-scale surveys are undertaken more often, and sometimes reveal seasonal patterns at a local scale.

The international (SCANS, SCANS-II, SCANS-III and CODA) and national surveys (e.g., ObSERVE) were collated following a data call from OSPAR in 2021 (Figure a and Figure b). Data were collected on dedicated line-transect surveys (either aerial or ship-based) using a distance sampling protocol, that took place within the OSPAR Maritime Area between 2005 and 2020. The first SCANS survey in 1994 focused on the Greater North Sea subregion but also covering part of the Celtic Seas subregion (Figure b, leftmost panel). The second SCANS survey in 2005 extended into the Bay of Biscay and Iberian Coast subregion. In 2007, the CODA survey covered offshore waters of the Celtic Seas and Bay of Biscay and Iberian Coast subregion (Figure b, middle panel). SCANS-III in 2016 covered both inshore and offshore waters. The ObSERVE summer aerial survey covered waters around Ireland that same year (Figure b, rightmost panel). While the SCANS-II / CODA and SCANS-III / ObSERVE survey had a similar geographic coverage, the first SCANS 1994 survey was more limited in scope. The Greater North Sea subregion has been consistently surveyed by the three SCANS surveys.

The data used to assess abundance and distribution of coastal bottlenose dolphins are mostly derived from capture–recapture methods applied to photo-identification data available in each of the AUs for this species. In some AUs, data from ship-based surveys are available. Data are patchy, however, and in some AUs robust data are only available for a limited number of animals that use small (protected) areas within the AU. As a consequence, data derived from small-scale surveys that relate to a discrete group of animals may not always be a robust measure of abundance for the entire AU. In order to examine trends, only sites with at least four estimates calculated over a ten-year period were considered.

Species Distribution

Distribution maps were generated from a density surface model approach (Miller et al., 2013) fitted to the collated data by OSPAR. Environmental covariates used in modelling included physiographic predictors (e.g., bathymetry) and oceanographic variables (e.g., sea surface temperature; Table a). The model was used to predict the summer distribution between 2005 and 2020 for harbour porpoise, fin whale, minke whale and offshore bottlenose dolphin. Predicted distribution maps were averaged over the periods 2005 to 2009, and 2010 to 2020.

For other species, information on species distribution has been obtained from modelled density surfaces fitted to data collected during large-scale surveys (SCANS-II/CODA and SCANS-III): Lacey et al., (2021) fitted (i) a density surface model to the SCANS-II/CODA data and (ii) another model to the SCANS-III data to investigate the distribution and habitat of cetacean species (and change therein) in the North-East Atlantic. Sightings of these species during the SCANS-III surveys (summer 2016) are shown to illustrate recent distribution.

For coastal bottlenose dolphin, records of sightings and strandings were used to identify where populations existed historically.

Abundance

Abundance was estimated using line-transect distance sampling methods (conventional design-based (CDS) estimates; Hammond et al., 2013). Ship-based survey methods mostly used two observation teams on the same vessel so that animals missed on the transect line and any responsive movement could be accounted for in the analysis. However, for some species in some years, insufficient data were available for such extended analytical methods to be used. Several aerial surveys used tandem aircraft or the circle-back procedure for harbour porpoises to correct for animals missed on the transect line (Hiby and Lovell, 1998; Hiby, 1999); this was extended to dolphin species and minke whales in SCANS-III in 2016. For other species, conventional aerial survey methods were used, corrected for availability and observer bias where possible (Hammond et al., 2013, 2021).

The abundance of coastal bottlenose dolphin populations was estimated, where possible, using Photo-ID capture-recapture methods (Amstrup et al., 2010; e.g. Cheney et al. 2018) and ship-based surveys.

Metrics

Species Distribution

Density surface models have been used to predict the distribution of those species for which sufficient data are available from large-scale purpose-designed surveys.

A suite of physiographic and oceanographic covariates (Table a) was used to model, with data collated by OSPAR (Figure b), the distribution of cetaceans in a Density Surface Modelling framework (after Miller et al., 2013).

Table a: Assessment values for cetacean species. Source A: EMODnet DTM (https://www.emodnet-bathymetry.eu/). Slope, aspect and topography complexity index were derived from the bathymetry data. Source B: Copernicus database (https://resources.marine.copernicus.eu).

Source A: EMODnet DTM (https://www.emodnet-bathymetry.eu/). Slope, aspect and topography complexity index were derived from the bathymetry data.
Source B: Copernicus database (https://resources.marine.copernicus.eu).

The modelling framework uses generalised additive models to predict cetacean distribution from environmental covariates. This framework allowed to model non-linear relationships between cetacean habitat and the marine environment (Embling et al., 2010; Gilles et al., 2016; Formula a):
 

NB denotes the Negative Binomial distribution, ω is the overdispersion parameter, Effort is the prospected surface (including the effective strip width estimated by distance sampling) and µ is the linear predictor (Formula b):

where f(.) are cubic Bezier splines (Marx and Eilers 2010).

An extrapolation analysis in environmental space was undertaken to assess the robustness of predictions from this model-based approach (Bouchet et al., 2019). Environmental variables (Table a) define a new topology, or environmental space, which can be studied with geometric tools such as distances and polygons. In particular, an extrapolation analysis can be carried out in this environmental space to assess how the data collected thus far covers the whole environmental space available in the North-East Atlantic. This analysis informs on how pervasive extrapolation may be when predicting cetacean distribution and habitat from a model. Technically, extrapolation analysis compares convex polygons in environmental space: a first polygon is obtained with the effort data by taking all segments of effort and their associated environmental covariates (the calibration data). A second polygon is obtained from environmental data collected in the whole area for which a distribution map is desired. It is important to note that this area is larger than the geographic area covered by surveys (e.g., Figure a and Figure b). These environmental data are to be used to predict cetacean distribution using both the environmental data and the model calibrated with the survey effort data. Each prediction is thus defined by a set of coordinates (values) that locates it in environmental space: it is possible to assess for each prediction whether it falls within the convex hull defined by the calibration data (King & Zeng 2007, Authier et al., 2017, Bouchet et al., 2019). Extrapolation analysis thus allows to assess the robustness of model predictions by quantifying how much the latter are informed by these data.

Abundance

Abundance of animals per species AU has mostly been estimated using data collected from large-scale purpose-designed aerial or shipboard surveys using line-transect distance sampling methods (Buckland et al., 2001); these are known as conventional design-based estimates CDS (e.g., Hammond et al., 2013; 2021). When abundance estimates come from models fitted to these data to generate a density surface (Miller et al., 2013) from which abundance is derived; these are known as model-based estimates (e.g., Gilles et al., 2016). All the reported estimates of population size for the Indicator Assessment are design-based estimates, which are less model-dependent, but do not allow to map densities at a fine spatial scale.

Abundance of coastal bottlenose dolphins per population in an AU was estimated using capture–recapture methods applied to photo-identification data, and ship-based surveys.

Baselines

Although the baseline should derive from historical data, these data are not available for the vast majority of cetacean species (Bearzi and Reeves 2021). To assess change, a recent (modern) baseline must be used (Kopf et al., 2015), which should then be assessed as a “reference” situation, and possibly one that is already known to be degraded. The most useful baselines for wide-ranging cetacean species derive from the results of large-scale surveys such as SCANS for coastal species and SCANS-II/CODA for wider ranging species (e.g., CODA, 2009; Hammond et al., 2002, 2013).

For most species, only two abundance estimates are currently available so a robust assessment of a trend involving change from a baseline is not possible. For harbour porpoise and white-beaked dolphin in the Greater North Sea there are three estimates available.

Spatial Scope

Assessment Units (AUs) for assessing abundance and distribution were defined for a number of species (ICES, 2014b) in separate regions, or in the OSPAR Maritime Area. For harbour porpoise, several AUs have been reviewed and defined (Figure c; NAMMCO/IRM 2019). OSPAR’s Marine Mammal Expert Group (OMMEG) further reviewed these AUs in 2021 and merged the Celtic Sea and Irish Sea AUs into a single Irish and Celtic Seas AU. For bottlenose dolphin, eleven AUs have been defined for the relatively small coastal populations on the basis of a combination of spatial separation, lack of photo-ID matches and genetic differences (Evans and Teilmann, 2009; ICES, 2013; ICES 2014; IAMMWG 2015). (Figure d) and a single offshore AU for the relatively large and wide-ranging population(s) of bottlenose dolphin living offshore (Figure e).

Currently, a single AU covering all European Atlantic waters has been defined for minke whale (Figure f), short-beaked common dolphin (Figure f), and white-beaked dolphin and white-sided dolphin (Figure g). For fin whales, three AUs defined by the International Whaling Commission overlap with the OSPAR Maritime Area (Figure h). No AUs have been defined for other species included in this assessment.

Threshold Value

The assessment thresholds for populations of cetacean species are based on the generation criterion from the International Union for the Conservation of Nature (https://www.iucn.org/). In each AU, the (localized) population size of each species will be maintained at or above baseline levels (the first abundance estimate available or the closest to the date of the Habitats Directive (Council Directive 92 / 43 / EEC)), with (i) no absolute decrease of more than 30% and (ii) a rate of decrease no greater than 30% over three generations. The latter can be converted to threshold values that are an annual rate of decline in abundance that must not be exceeded without having to wait for three generations. The annual rates of decline, if sustained over three generations, will lead to 30% decline in abundance. Since generation time varies between species (Taylor et al., 2007), thresholds, which corresponds to average annual rate of decline in abundance that would result in an overall 30% decline if sustained over three generations, are species-specific and reported in Table b.

Table b: Threshold values for cetacean species

N/A: not available

Trend estimation

A trend is quantified as the percentage change (in abundance) over a specified time period (Link and Sauer 1997). To assess trends, at least three abundance estimates are required over a relevant time scale. Robust assessments will require additional abundance estimates. To assess trends, it is necessary to consider the statistical power to detect changes (Authier et al., 2020).
For coastal bottlenose dolphins, monitoring in several populations within AUs has been ongoing for long enough to enable an assessment of trends, while in others there are currently too few data available over a suitable time period. Current estimates of numbers of animals in a population are usually obtained by photo-ID, although in their absence, abundance estimates are derived from line-transect surveys. Where possible, annual data on the estimated number of animals per population are provided in this assessment. Consideration is also given to coastal bottlenose dolphin populations known to have disappeared from their former range.

Power Analysis

Prospective statistical power analyses (Hoenig and Heisey 2001) were performed for detecting a trend in short time series of abundance estimates for cetacean species using a statistical significance set to 0,2 (Authier et al., 2020). Power was increased (except type-I error rate; Authier et al., 2020) but remained low to detect a small decline.  The power to detect trends could be improved by increasing the frequency of the large-scale surveys (Authier et al., 2020).

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