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Fin and sperm whales residing or circulating in the Mediterranean Sea are exposed to biological and chemical hazard due to the increasing anthropogenic impact. In particular, most of the coastal areas bordering with the Sanctuary is heavily populated and full of commercial, touristic and military ports and industrial areas. As a consequence, a range of diverse human activities exerts several actual and potential threats to cetacean populations in the Sanctuary, including habitat degradation, urban, tourist, industrial, and agricultural development, intense maritime traffic, military exercises and oil and gas exploration, just to mention the most important ones.
This study provides background information on the occurrence and concentration of parasites and bacterial infections/communities as well a first investigation of heavy metals and organic pollutants in faecal samples from fin and sperm whale Mediterranean subpopulations within the Pelagos Sanctuary.
Here, a modified MINI-FLOTAC technique in combination with FILL-FLOTAC were used for parasitological detection of the cysts in the faecal samples of fin and sperm whales. Although this technique has never been used before for whale faecal samples, it has successfully been used in previous coprological surveys for the detection of gastrointestinal parasites in other marine animals as the loggerhead sea turtles (Caretta caretta)23,24. The MINI-FLOTAC can be considered as one of the most accurate methods for coprological diagnosis of endoparasite infections and cysts/eggs counting nowadays available in veterinary medicine25. It allowed an accurate and reliable detection of Blastocystis cysts in both fin and sperm faecal samples. Molecular analysis, sequencing and phylogenetic analysis confirmed the obtained results.
Blastocystis is a common intestinal protozoan parasite reported in several animals, e.g., humans, livestock, dogs, amphibians, reptiles, birds and even insects26,27,28. Although it possesses pathogenic potential, its virulence mechanisms in humans are still not well understood29. Blastocystis seems to be linked to Irritable Bowel Syndrome, i.e., a functional disorder mainly consisting in chronic or recurrent abdominal pain due to altered intestinal habits30. Studying the small subunit ribosomal RNA (SSU-rDNA) gene, several authors identified at least 22 different Blastocystis subtypes (ST) in a variety of animals, humans included, i.e., from ST1 to ST17, ST21, and ST23 to ST26 (Ref.26). To date, human Blastocystis isolates are classified into 10 ST (i.e., ST1-ST9 and ST12) that, with the only exception of ST9, have been identified also in other animals31. According to Parkar et al.32, Blastocystis has the potential to spread through human-to-human, animal-to-human, and human-to-animal contact.
Few similar parasitological investigations have been conducted in the past and are currently available in the literature. Hermosilla et al.33 detected three protozoan parasites (i.e., Giardia sp., Balantidium sp., Entamoeba sp.) and helminth parasites in individual faecal samples from wild fin (n. 10), sperm (n. 4), blue (Balaenoptera musculus; n. 2) and sei (Balaenoptera borealis; n. 1) Atlantic whale subpopulations from the Azores Islands, Portugal. Protozoan parasites (Giardia sp., Balantidium sp., Cistoisospora-like indet.) and helminth parasites were also found in individual faecal samples of wild sperm whales inhabiting Mediterranean Sea waters surrounding the Balearic Archipelago, Spain34. Out of these, three of herein detected parasites clearly bear anthropozoonotic potential, i.e., Anisakis, Balantidium and Giardia34.
In the present work, Blastocystis has been found in fin and sperm whale samples and, to the best of our knowledge, this is the first time that this protozoan genus is reported for any cetacean species. Therefore, this finding represents the first new host record for fin and sperm whales. Blastocystis ST3 was the only subtype found in fin and sperm whales. Molecular studies in human samples showed the occurrence of ST1–ST9, with ST3 as the most prevalent subtype35,36. Indeed, ST3 is the Blastocystis subtype with the highest prevalence in humans worldwide and probably represents the human species-specific ST (Ref.37). Consequently, animals harbouring ST3 may thus mirror environmental contamination by humans, confirming the zoonotic potential of animals for Blastocystis human infections. Unlike33,34, no eggs of helminths were found in our faecal samples.
Variations in parasites composition and prevalence might be related to several factors such as dietary differences, the parasite life cycle, the availability of hosts necessary to complete their life cycle, the interactions between parasite species, the host immune response, and the host population density23. Moreover, parasites can also spread in different way in animal populations in the wild, particularly when they act together with ecological, biological, and anthropogenic factors38.
The occurrence in whales of parasites with a zoonotic potential like Giardia or Balantidium, most probably due to coastal waters contaminated by sewage, agricultural and urban run-off, has been already reported elsewhere39,40,41,42,43. Furthermore, human excretions from increasing number of pleasure boats, fishing and whale watching boats could be an additional form of contamination. Finally, the intense maritime traffic in the Mediterranean Sea, the percentage of which is higher than in other oceans44, represents another source of contamination. In all cases, results highlight that human activities play an important role for the widespread of these pathogens.
No bacterial pathogen of human or terrestrial animal origin has been detected both by targeted PCR and by Illumina high throughput sequencing. This difference could be due to the lower survival rate of bacteria in the sea environment, compared to protozoan parasites45.
Previous works reported the occurrence of human pathogens in stranded common minke whale (Balaenoptera acutorostrata) from Philippines46 and killer whale respiratory microbiome in North Pacific47. Although the relatively low number of samples cannot exclude potential risk of transmission of human and zoonotic pathogenic bacteria to cetaceans in the surveyed area, our results suggest to focus on microbiological analyses to track potential internal waterborne pathogens to the ones able to form cysts (like parasites) or other forms of resistance (like spore-forming bacteria) that are more likely to survive for longer period in the seawater.
The dominance of Bacterioidetes and Firmicutes (common with other terrestrial mammals), the baleen-specific higher number of Spirochaetes and the lower of Proteobacteria characterized both species, as also reported elsewhere21. Moreover, differences of some taxa related with the diverse diet were confirmed: in the case of sperm whale, whose nutrition is based on cephalopods, a higher proportion of Synergistetes was observed in faecal samples, whereas faecal samples from fin whales had a higher level of Spirochaetes compared those from sperm whales. These findings are in agreement with Erwin et al.13. The Synergistetes phylum includes gram negative, anaerobic, rod-shaped bacteria, widely distributed in terrestrial and aquatic environments, including host-associated with mammals48. Within this phylum, Synergistaceae family and Pyramidobacter genus OTUs were particularly dominant among sperm whale microbiome (Fig. 4). However, no correlation with potential pathogenicity could be drawn from the presence of these specific OTUs, considering their ubiquity in oral and gut mucosa of marine and terrestrial animals, despite some of the genus belonging to Synergistaceae family (e.g. Cloacibacillus spp.) are considered opportunistic pathogens49. About potential health implication of Spirochaetes, similar conclusion than for Synergistetes could be drawn: Treponema sp., found as dominant genus in fin whales, were found in healthy baleen whales by Sanders et al.21 so as among more dynamical OTU in stranded right whales50Sphaerochaeta spp. associated with healthy cetacean monitored oral cavity microbiome14. Moreover, fin whale faeces also showed a higher proportion of taxa that are also enriched in terrestrial herbivores, like Lentisphaere, Verrucomicrobia, Actinobacteria and Tenericutes, as also reported elsewhere21. Although differences in species sampled and habitats compared to previous studies, we found confirmation of both species and diet-influenced gut microbiota composition. Notably Akkermansia (one of the dominant Verrucomicrobia OTUs) and Coriobacteriaceae (dominant family among Actinobacteria phylum) includes typical holobiont of terrestrial and marine mammals, but also some pathobiont, so far confirmed only for humans51. Due to the wide distribution of some of Synergistetes and Spirochaetes phyla, it is not possible to establish if their presence could be ascribed exclusively to an anthropogenic impact; however, it is worth of interest that some of the genus found in both whale species and belonging to these phyla include opportunistic pathogens whose virulence for marine mammals still need to be confirmed. Interestingly, some archaeal sequences related to the Thermoplasmatales order were also found. This confirms what already reported by Sanders et al.21, i.e., that archaea belonging to this order may have a role as methane producer from methylated amines in baleen gut, differently from methanogenic archaea belonging to other orders that typically colonize the gut of terrestrial mammals, including humans.
Therefore, the two sampled species harboured typical gut microbiome belonging to fin whale and sperm whale groups. These data extend the spectrum of surveyed whales gut microbiome to previously unsampled species and confirms that NGS analyses could be a useful tool to retrieve information on the health status of wild whales.
While the concentration of 16 U.S. EPA priority PAHs and of 29 PCBs, being always  More

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Spatial map of Chinese protected areas
The database of protected areas (PA) distribution in China and a digitized spatial map thereof were compiled from Zhao et al.40 and Zhang et al.41. In total, we obtained the information of 2622 protected areas in China, which also included marine reserves. In order to evaluate the representation of terrestrial protected areas, we excluded marine reserves from our analyses. We also excluded Taiwan because we did not have the spatial distribution data for nature reserves in Taiwan. Finally, we had the boundary information of 2572 protected areas covering about 15.2% land area in China.
Species’ range maps
Range maps of threatened vertebrates (birds, mammals, amphibians and reptiles) were obtained from the IUCN’s Red List42. Distribution data of threatened plants were compiled from Flora of China, Atlas of woody plants in China, provincial and local floras, checklists of nature reserves, various inventory reports across China and peer-reviewed papers. We obtained the information for critically endangered (CR), endangered (EN) and vulnerable (VU) species. The conservation status of vertebrates was obtained from IUCN Red List42, while that of plants was obtained from Qin et al.43. In total, we obtained the distribution information of 103 birds, 86 mammals, 134 amphibians, 50 reptiles, and 2983 plants in China (see Supplementary Data 1). We estimated the number of species in each PA by overlaying the map of PA with the species’ range maps in ArcGIS 10.2 (ESRI, Redlands, CA). In order to validate the distribution of species, we further verified the presence of species in respective PA by checking their inventory reports.
Human footprint data
In order to measure the extent of human pressure on the protected areas, we obtained the most comprehensive global map of human pressure i.e., human footprint (HFP) from https://wcshumanfootprint.org. The human footprint measures the cumulative impact of direct pressures on environment from human activities and is based on data from built environments, agricultural lands, pasture lands, human population density, night-time lights, railways, roads and navigable waterways44. It is one of the most complete and finest terrestrial datasets on cumulative human pressure on the environment. The human footprint maps of two time periods (1993 and 2009) are available at present. We downloaded the maps of both time periods at the spatial resolution of 1 km × 1 km to quantify the change in human pressure within Chinese protected areas over a 16-year period. It should, however, be noted that any point estimate of the change in HFP might include errors due to the resolution and reliability of the component layers. For example, one of the components of HFP is the night-time lights, which changed over time from incandescent to mercury vapor to light emitting diode. This means that the change in night light is due to more than development. As a result, the systemic bias in regional economy could likely cause low HFP in wealthy as compared to rural areas. While this issue does not invalidate our analyses, such comparisons should be applied with caution.
Climate data
In order to represent the climatic conditions of the past and the present, we obtained 20 years climate data comprising monthly minimum temperature, monthly maximum temperature and monthly precipitation for two time periods (past: 1961–1970 and present: 2010–2019). We used 10 years window for each time period to capture the variability in climatic conditions in order to prevent over- or underestimation of the past and present climate. In total, we obtained 12 months × 10 years × 3 variables × 2 time periods = 720 global raster layers from the Climate Research Unit (CRU TS v. 4.04) database (http://www.cru.uea.ac.uk/data) at the spatial resolution of 0.5° × 0.5°45. We then calculated the monthly mean values for the three variables for each time period separately. From these monthly mean values, we estimated mean annual temperature (MAT), mean temperature of warmest quarter (MTWQ), mean temperature of coldest quarter (MTCQ), mean annual precipitation (MAP), precipitation of driest quarter (PDQ) and precipitation of wettest quarter (PWQ) for each time period using biovars function in the R package ‘dismo’46. We then estimated the average value of climate variables in each PA using zonal.stats function in the R package ‘spatialEco’47. In order to reduce dimensionality and collinearity of the 6 climate variables, we performed principal component analysis (PCA) using prcomp function in R v4.0.248. Following Carroll et al.37, we used climate data for both past and present based on the first 2 PCA axes, which explained 89.4–89.8 % of the variance (Supplementary Tables 1-2). Additionally, we also estimated the change in mean annual temperature to identify PAs that have experienced climate warming higher than the Paris Agreement threshold of 1.5 °C. All the analyses were performed in R version 4.0.248.
Vulnerability mapping
We calculated three indices of vulnerability within each protected area: (i) species vulnerability, (ii) anthropogenic vulnerability, and (iii) climate vulnerability. In order to measure species vulnerability, we first assigned numerical value to each IUCN threat category using a geometric progression49,50. We gave scores of 2, 4, and 8 to species belonging to categories VU, EN, and CR, respectively. We, then, summed the score of all species in each PA and standardized the value to the range of 0–1 using minimum–maximum normalization. We performed these steps separately for birds, mammals, amphibians, reptiles and plants to calculate the vulnerability score of each group. We also calculated the cumulative score by combining the total scores of all five groups. Values close to 0 indicated low species vulnerability and values close to 1 indicated high species vulnerability. Although species diversity is highly correlated with the species vulnerability metric used herein (Pearson’s correlation coefficient = 0.94 − 0.99, p  More