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NATURE PODCAST
09 March 2022
The AI that deciphers ancient Greek graffiti
An artificial intelligence that restores illegible inscriptions, and the project that’s reintroducing lost species in Argentina.
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In this episode:00:46 The AI helping historians read ancient textsResearchers have developed an artificial intelligence that can restore and date ancient Greek inscriptions. They hope that it will help historians by speeding up the process of reconstructing damaged texts. Research article: Assael et al.News and Views: AI minds the gap and fills in missing Greek inscriptionsVideo: The AI historian: A new tool to decipher ancient textsIthaca platform08:53 Research HighlightsPollinators prefer nectar with a pinch of salt, and measurements of a megacomet’s mighty size.Research Highlight: Even six-legged diners can’t resist sweet-and-salty snacksResearch Highlight: Huge comet is biggest of its kind11:10 Rewilding ArgentinaThis week Nature publishes a Comment article from a group who aim to reverse biodiversity loss by reintroducing species to areas where they are extinct. We speak to one of the Comment’s authors about the project and their hopes that it might kick start ecosystem restoration.Comment: Rewilding Argentina: lessons for the 2030 biodiversity targets21:02 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, giant bacteria that can be seen with the naked eye, and how record-breaking rainfall has caused major floods in Australia.Science: Largest bacterium ever discovered has an unexpectedly complex cellNew Scientist: Record flooding in Australia driven by La Niña and climate changeThe Conversation: The east coast rain seems endless. Where on Earth is all the water coming from?Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.
doi: https://doi.org/10.1038/d41586-022-00701-7
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Read the paper: Restoring and attributing ancient texts using deep neural networks
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AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22
The evolution, evolvability and engineering of gene regulatory DNA
Article 09 MAR 22
Gran Turismo champion, reimagined urine — the week in infographics
News 15 FEB 22
History
AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22
Police rely on radiocarbon dating to identify forged paintings
News 09 MAR 22
Restoring and attributing ancient texts using deep neural networks
Article 09 MAR 22
Ecology
Discovery of a Ni2+-dependent guanidine hydrolase in bacteria
Article 09 MAR 22
Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22
How itchy vicuñas remade a vast wilderness
Research Highlight 04 MAR 22
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Following the work in36, we construct an epidemiological model which tracks the disease dynamics and population of two species of hosts following the introduction of a pathogen. The native host (hereafter simply referred to as “type 1”) is vulnerable to the disease, but due to being well adapted to the native habitat has high fecundity when uninfected. The invasive host (hereafter referred to as “type 2”), has coevolved defenses to the pathogen that increase both its tolerance of and resistance to the disease, but is not inherently as well-adapted to the habitat in the absence of infection (i.e., its intrinsic rate of growth in the new habitat is lower than that of the native).Our initial conditions correspond to a population of uninfected type 1 hosts with a small number of both uninfected and infected type 2 hosts, representing an invasion by a novel competitor carrying a novel pathogen into the type 1 population. We consider a vector-borne pathogen, and make the simplifying assumption that there is an already abundant competent vector species in the habitat. (For this initial formulation, we considered a scenario of mosquito-borne infections in birds, such as avian malaria37 or West Nile virus38, to motivate concrete choices.)The model couples two biological dynamics: the daily vector-borne spread of the disease among hosts, and a yearly host breeding cycle. We simulate in discrete time-steps that represent days using an SIR model taking into account the interactions between the disease, the two species of host, and the vectors. The model also includes a passive death rate for hosts of vectors, which increases for hosts while infected. While the vectors are assumed to breed daily, the hosts reproduce as part of an assumed annual breeding season, every (t_c) time-steps (typically equal to 365). These dynamics were informed by considering an annually breeding bird population in a tropical environment, however, they are not meant to reflect the realism of any one biological system. They are chosen here merely to allow a clean interpretation of modeled scenarios. Future models should explore the impact of greater variety in the dynamics of possible vector and host reproductive patterns.Epidemiological modelThe model tracks eight variables corresponding to combinations of host species and vectors with their infection status. Hosts may be of type 1 or 2, and are either susceptible to the disease ((S_1, S_2)), currently infected ((I_1, I_2)), or recovered ((R_1, R_2)). We assume that recovery is complete and recovered individuals suffer no residual effects from their infection aside from a lifelong immunity to becoming reinfected. (We later set the recovery rate for host type 1 to 0, so (R_1 = 0) at all times, but leave it defined for the sake of generality.) For simplicity, we model using only one stage of infection in which individuals are both infectious and symptomatic. The model also tracks the status of the vector population, which may either be susceptible ((S_v)) or infected ((I_v)). We assume that vectors do not recover from the disease, but also suffer no negative effects from being infected, acting only as carriers.For convenience of notation, we denote the total number of hosts$$begin{aligned} H = S_1 + I_1 + R_1 + S_2 + I_2 + R_2 end{aligned}$$and the relative frequencies of infection within their respective population$$begin{aligned} F_1 = frac{I_1}{H}, F_2 = frac{I_2}{H},F_v = frac{I_v}{S_v+I_v} end{aligned}$$which allows some equations to be written more compactly. Table 1 shows a summary of these variables.Table 1 Variables.Full size tableThe model also has several constant parameters that affect the dynamics. (beta _j) determines the probability that hosts of type j become infected when bitten by a single infected vector. We typically set (beta _1 > beta _2), making type 2 hosts less likely to become infected.Likewise, (delta _j) determines the probability that a vector becomes infected when biting an infected host of type j.(b_j) determines the bite rate for vectors on host type j. We assume that each vector bites the same number of hosts per day, so each vector’s probability of becoming infected depends only on the frequency of infection among hosts, while each host will be bitten more if there are more vectors.(gamma _j) determines the proportion of infected hosts of type j that recover from the disease each day. We typically set (gamma _1 = 0 < gamma _2), meaning infected hosts of type 1 do not recover, while infected type 2 recover after an average of (1/gamma _2) days.(mu _{j-}) determines the daily death rate for uninfected hosts of type j and (mu _{j+}) determines the death rate for infected host of type j. We typically set (mu _{1-} = mu _{2-}< mu _{2+} < mu _{1+}), meaning uninfected hosts have the same death rate regardless of type, infected type 2 have a higher death rate than uninfected hosts, and infected type 1 have the highest. (Both susceptible and recovered hosts are considered to be uninfected.) Table 2 shows a summary of parameters related to the SIR dynamics.Equation 1 shows continuous ordinary differential equations approximating the dynamics. Note that the actual model instantiates these in discrete time-steps using the forward Euler method with (h = 1).$$ begin{aligned}&frac{dS_1}{dt} = - S_1 beta _1 b_1 I_v /H - S_1 mu _{1-} \&frac{dI_1}{dt} = S_1 beta _1 b_1 I_v /H - gamma _1 I_1 - I_1 mu _{1+} \&frac{dR_1}{dt} = I_1 gamma _1 - R_1 mu _{1-} \&frac{dS_2}{dt} = -S_2 beta _2 b_2 I_v /H - S_2 mu _{2-} \&frac{dI_2}{dt} = S_2 beta _2 b_2 I_v /H - I_2 gamma _2 - I_2 mu _{2+} \&frac{dR_2}{dt} = I_2 gamma _2 - R_2 mu _{2-}\&frac{dS_v}{dt} = alpha _v H -S_v delta _1 b_1 F_1 -S_v delta _2 b_2 F_2 -S_v mu _v\&frac{dI_v}{dt} = S_v delta _1 b_1 F_1 + S_v delta _2 b_2 F_2 - I_v mu _v\ end{aligned} $$ (1) Table 2 Parameters for SIR dynamics.Full size tableFollowing a standard SIR model, susceptible hosts can become infected, and infected hosts become recovered, but each equation also contains a negative term corresponding to deaths. Thus, the total population of hosts is strictly decreasing in this time-frame. We assume that the vectors breed on a much shorter timescale than hosts, so we include a term for their births here, while host births are implemented by a yearly breeding event. We assume no vertical disease transmission, so all new vectors begin in the susceptible category. We assume that the daily birthrate for each vector increases with access to hosts, and decreases with competition among other vectors for hosts and breeding sites, so we set it equal to (frac{alpha _v H}{S_v + I_v}), where (alpha _v) is a constant scaling factor. Since the birthrate for each vector contains the total number of vectors in its denominator, the total number of vector births in the population will simply be (alpha _v H).A population with a larger number of hosts will be able to sustain a larger number of vectors. For a population with a constant number of hosts, the equilibrium vector population will be proportional to the number hosts: aH where (a = frac{alpha _v}{mu _v}) is the equilibrium vector density (number of vectors per host). For instance if (a = 2), then in equilibrium there will be twice as many vectors as hosts. Given a fixed number of hosts, the population of vectors will asymptotically approach the equilibrium value. In practice the total number of hosts is constantly changing, so the population of vectors will chase after this moving equilibrium, though for our standard parameters (alpha _v) and (mu _v) are sufficiently large such that this will occur on a short timescale, and the population of vectors remains close to the current equilibrium value.Breeding eventTable 3 shows a summary of parameters related to the breeding event. Every (t_c) days (typically 365), a breeding event occurs according to the following process.Table 3 Parameters for breeding event.Full size tableLet$$begin{aligned}&Delta S_1 = t_c alpha _{1-}(S_1+R_1)+t_calpha _{1+} I_1 \&Delta S_2 = t_c alpha _{2-}(S_2+R_2)+t_calpha _{2+} I_2 \ end{aligned}$$be the number of new host offspring of each type born this generation. In order to maintain consistency of temporal units among the parameters, each birthrate parameter is multiplied by (t_c). Let H be the current total number of hosts. Let$$begin{aligned} c = {left{ begin{array}{ll} 0 &{} hbox {if } H ge kappa \ 1 &{} hbox {if } H + Delta S_1 + Delta S_2 le kappa \ frac{kappa -H}{Delta S_1 + Delta S_2} &{} hbox {otherwise} \ end{array}right. } end{aligned}$$be the proportion of offspring that survive to adulthood. (None, if the population is already above carrying capacity. All, if the difference between the reproducing population size and the carrying capacity exceeds the new births. If the population is approaching carrying capacity, juvenile mortality scales proportionally so that the population will hit carrying capacity but not exceed it.)Then$$begin{aligned}&S_1 + c Delta S_1 rightarrow S_1 \&S_2 + c Delta S_2 rightarrow S_2 \ end{aligned}$$We assume there is no vertical disease transmission, so all new hosts begin in the susceptible category. We assume that the host population is iteroparous, such that the new offspring and the existing adult population both carry over to the next generation. If the new population would exceed the carrying capacity, we assume the limited space or supplies reduces the number of successful offspring so that the population exactly reaches the carry capacity by reduction in juvenile survival rather than population-wide competition that could also reduce the adult population.The carrying capacity is therefore what drives the interspecific host competition. Because births of both species are summed and then normalized by the total number of births, the higher the birthrate of one host, the larger a fraction of the available space it will capture during the breeding event. Similarly, the lower the death-rate of a host, the less space it frees up for the next breeding event. Even if one host species would be able to sustain a stable population on its own, the presence of a more fit competitor can lead to the extinction of the less fit type by driving its effective birth rate down.Immune-reproductive trade-offs and boundary conditionsWe assume that host type 1 is evolutionarily stable in the absence of the disease; an uninfected monoculture population below the carrying capacity will have at least as many births as deaths each cycle. In a continuous version of this model where births and deaths happened simultaneously, this might be defined by (alpha _{1-} ge mu _{1-}) . However in our model, the population spends many days decreasing due to deaths before the next breeding event occurs. The population exponentially decays throughout the cycle, and then jumps up during the breeding event. The number of new host births is proportional to the number of hosts at the start of the breeding event, which will be the lowest value of any other time during the cycle. Thus, the birth rate needs to be high enough that the surviving hosts can compensate despite their diminished numbers. Taking this into account, we get the condition$$begin{aligned}&alpha _{1-} ge frac{1-(1- mu _{1-})^{t_c}}{(1-mu _{1-})^{t_c}} \ end{aligned}$$Which is a higher bound on (alpha _{1-}) than the simpler one above, but will be close to it if (mu _{1-}) and (t_c) are small.To implement the scenario in which type 2 has increased resistance and tolerance to the disease at the expense of overall fecundity, we implement the following boundary conditions:$$begin{aligned}&beta _1 > beta _2 \&0 = gamma _1< gamma _2 \&mu _{1-} = mu _{2-}< mu _{2+} < mu _{1+} \&alpha _{1-} > alpha _{2-} > alpha _{2+} > alpha _{1+} end{aligned}$$Type 2 hosts are less likely to contract the disease, and are able to recover from it, while type 1 lack the immunological strength to eradicate it completely. Additionally, while both types of host are weakened by the disease, type 2 suffer fewer negative effects. However, this stronger immune response comes at the cost of reducing their birth rate when compared to healthy type 1 hosts.Due to the heterogeneous population, there is ambiguity in defining (R_0) for the disease. The two types of host have different transmission rates and durations of infection, and will therefore be responsible for different amounts of disease spread. To resolve this, we define several related values. Let (R_0^j) be the (R_0) of the disease in a homogeneous population of type j hosts: the average number of hosts infected (indirectly, through vectors) from a single infected host in a population consisting entirely of type j hosts.$$begin{aligned}&R_0^1 = frac{delta _1 beta _1 a b_1^2}{mu _v mu _{1+}} \&R_0^2 = frac{delta _2 beta _2 a b_2^2}{mu _v (mu _{2+}+gamma _2)} end{aligned}$$We simplify the equation for (R_0^1) since (gamma _1 = 0). We define w to be the frequency of host type 1: (w := (S_1 + I_1)/H). Then (R_0) for the vectors is$$begin{aligned} R_0^v = R_0^1 w + R_0^2 (1-w) end{aligned}$$which will also be the effective (R_0) of the disease for the hosts in the mixed population.For simplicity of results, we restrict to the case where type 1 is more infectious overall than type 2, in particular (R_0^1 > R_0^2). This allows us to avoid edge cases in simulation outcomes which are beyond the scope of this paper. We intend to lift this restriction and study these outcomes in future work.NoteAlthough usual epidemiological model formulations can rely on the value 1 as the boundary condition for (R_0) to determine the epidemic potential of an outbreak, in this case we are calculating effective (R_0) in a dynamic host population, such that the decrease in disease spread due to saturation from recovered hosts and already infected hosts increases the actual thresholds. More accurate criteria require a technical and somewhat cumbersome analysis, which we leave for a future paper. More
Since the bone discontinuities noted in the three vertebrae analyzed show no clear sign of bone overgrowth, it is pivotal to rule out the possibility that we are dealing with preservation damages before proposing an accurate diagnosis for the lesions. The close-up view examination of the abnormalities shows that their edges have clear signs of smoothing and rounding (Fig. 1), which represent important evidence of osteoblastic activity18,19. Additionally, the similar color of the cortical damage and normal bone can be used as secondary evidence to rule out post-mortem processes as a possible origin of the alterations, since recent destructive processes are lighter than the rest of the bone19. Therefore, as taphonomic processes can be ruled out, the pointed evidence strongly suggests that the discontinuities observed are of pathological origin. More specifically, these breaks found in all three vertebrae are indicative of bone fracture.Based on fracture analysis criteria applied here20, which consider the location and morphological pattern of the fractures, we classified the fractures noted in all vertebrae as traumas belonging to Type A (vertebral body compression), Group A2 (split fractures), and subgroup A2.1 (sagittal split fracture). This diagnosis implies that the traumatic episode was likely caused by a compressive force on the vertebral column, which split the vertebral bodies in the sagittal plane. This type of injury is considered stable—i.e., the fracture does not have a tendency to displace after reduction—and neurological deficit is uncommon20,22,23. Although stable traumas cause only moderate pain, without generating significant movement limitations20, the Eremotherium individual here analyzed died with unhealed bones, as there is no evidence of callus formation.The absence of other skeletal signs that point to the presence of another type of disease concomitantly to the fractures allows us to reject the possibility that they have been generated as a result of a pre-existing disease (e.g., infection, neoplasm). We also consider that the vertebral injuries were not caused by repetitive force (stress fractures) because this type of injury is commonly characterized as a nondisplaced line or crack in the bone, called hairline fracture3. Those refer to situations where the broken bone fragments are not visibly out of alignment and exhibit very little relative displacement21. Although the Eremotherium vertebrae fractures’ can be described as nondisplaced, they also have a noticeable gap between their edges that is mostly narrow with wider parts in the middle, something found in split fractures20 but that is not characteristic of hairline fractures. Lastly, the subgroup C1.2.1 (rotational sagittal split fracture) might be a source of confusion due to similar morphological pattern with subgroup A2.1 (sagittal split fracture). However, in subgroup C1.2.1 there are compressive and rotational forces acting simultaneously, producing total separation into two parts20, which clearly did not occur in the vertebrae analyzed here.In humans, compression fractures are most commonly caused by osteoporosis, although infection, neoplasm and trauma can also be etiological factors23,24,25. However, as aforementioned, the absence of other pathological skeletal marks is an important characteristic to take note as it serves to disregard the possibility of the fractures’ genesis to be secondary to another pathology. As such, in this case, osteoporosis, infection and neoplasm are unlikely etiologies. On the other hand, a compression fracture in a healthy individual is commonly generated after a severe traumatic event such as a fall from great height23,26. This scenario seems to better explain the origin of the vertebral fractures in the case of the Eremotherium ground sloth herein studied.The three fractured vertebrae were recovered in the Toca das Onças site (Fig. 2), a small cave considered as one of the richest paleontological sites of the Brazilian Quaternary15. Two complete skeletons of Eremotherium laurillardi and fragments belonging to at least thirteen other individuals, together with several other bones assigned to different smaller species are known to this cave14. It comprises of a single dry chamber that can only be entered through vertical entrances approximately 4.5 m high (Figs. 2b–d and 3). Two different hypotheses concerning the depositional process of Toca da Onças were previously proposed: (1) the animals climbed down into the cave in search of water14; or (2) due to the vertical character of the cave entrance, it could have functioned as a natural trap where animals accidentally fell into the cave15.Figure 2Location map of the Toca das Onças site and images of the cave. (a) Detail of the location, (b) cave entrance area view, (c) view from inside the cave, (d) Cave entrance detail. Scale bars 10 m in (b) and 5 m in (c). This figure was generated by Adobe Photoshop CS6 software (https://www.adobe.com/br/products/photoshop.html).Full size imageFigure 3Schematic representation of the Toca das Onças site. (a) Ground plan of the cave illustrating its morphology and dimension, (b) Cross-section illustrating the abyss-shaped entrance.Full size imageThe first hypothesis would indicate that the animal fell into the cave during an attempt to climb down. However, there is no report in the literature indicating that Eremotherium laurillardi could have been a climbing animal. In addition, the vertical morphology of the cave entrance would be a limiting factor for climbing behavior (see Fig. 3).Therefore, based on the type of fracture (compression sagittal split fracture) observed in the three vertebrae of Eremotherium as well as the inferred origin mechanism (fall from a great height), the presence of the individual here analyzed in the fossil accumulation of Toca das Onças is more likely explained by the second hypothesis. This idea is not particularly new as ‘entrapment due to fall’ has been described as a fossil accumulation mode to several other caves worldwide (e.g.,27,28). However, the use of bones fractures as an indicator of fossil accumulation mode is an interesting novelty. Of course, a detailed taphonomic investigation in the Toca das Onças still needs to be conducted in order to accurately interpret the formation of this important Quaternary fossil accumulation from Brazil.In sum, we suggest that the animal accidentally fell into the cave, fractured at least three sequential vertebrae (12th, 13th thoracic vertebrae and 1st lumbar vertebra) after the impact on the ground, survived for a while, but succumbed trapped inside the cave without food and water (Fig. 4). Other animals found in the cave, but without signs of bone fracture, may have fallen and not fractured their bones or not survived after the fall, especially the smaller ones. Finally, the proposal of falls to explain the unusual record of giant ground sloth fossils preserving much of its skeleton in caves, as reported for Toca das Onças site, contrasts with the better-documented pattern of skeletal accumulation via hydraulic action.Figure 4Artistic reconstruction of the suggested fall of the individual Eremotherium laurillardi into the cave. Artwork by Júlia d’Oliveira.Full size image More
Our compiled dataset consisted of 1674 records of marine mammal records after removing duplicate reports. It included 660 reports of sightings, 59 reports of induced mortalities or hunting records, 240 reports of incidental mortalities, 632 unique stranding records (live / dead), and 83 records which could not be categorised because of incomplete information.SightingsA total of 660 opportunistic sightings (number of individuals, ni = 3299) were recorded throughout the Indian coastline between 1748 and 2017 (Fig. 1a, 2a, 3a). Sighting data on the east coast (species = 18, ni = 1105) was mostly restricted to Odisha and Tamil Nadu (representing 97% of total east coast sightings). On the west coast (ni = 1297), Maharashtra (ni = 549), Gujarat (ni = 248) and Karnataka (ni = 307) contributed to highest sighting records (representing 85% of total west coast sightings). Sightings from the islands also contributed to 24.85% of the dataset (Andaman & Nicobar Islands = 24.37%, Lakshadweep = 0.48%). Highest incidence of sightings was for DFP (ni = 1894) followed by dugongs (ni = 959), BW (ni = 58) and SBW (ni = 17).Figure 1Marine mammal records obtained from data compiled between years 1748 – 2017 along the east coast, west coast and the islands of India for the groups i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, given as color-coded stacked bars where (a) sighting records—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) stranding records—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue.Full size imageFigure 2Marine mammal records obtained every year from the data compiled between years 1748–2017 along Indian coastline given as cumulative numbers for each group i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, as color-coded stacked bars, where (a) sighting records—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) stranding records—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue.Full size imageFigure 3Bubble plots showing distribution of marine mammal records obtained from data compiled between years 1748–2017 along the Indian coastline for each group i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, as color-coded stacked bars, where (a) sighting—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) strandings—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue. Size of the bubble indicates number of individuals. These maps were created using ArcGIS 10.5 (https://desktop.arcgis.com/en/arcmap/10.3/map/working-with-layers/about-symbolizing-layers-to-represent-quantity.htm).Full size imageInduced mortalitiesA total of 59 incidences (ni = 102) were recorded of marine mammals being hunted/ captured between the years 1748–2017 (Fig. 1b, 2b, 3b). The total number of animals hunted/ captured deliberately is similar along east coast (ni = 33), west coast (ni = 29) and islands (ni = 36). Out of all marine mammal species, 90% of the animals hunted at the east coast were dugong D. dugon (ni = 30, all from Tamil Nadu). On the west coast, records of hunting incidences of finless porpoise Neophocaena phocaenoides were highest (79% of total records on west coast, Goa ni = 17, Kerala ni = 4, Karnataka and Maharashtra ni = 1). In the islands (i.e., Andaman and Nicobar Islands), 94% of the hunting records were of dugongs (ni = 34).Incidental mortalitiesA total of 240 net entanglements (ni = 1356) were reported along the Indian coast between the years 1748 and 2017 (Fig. 1c, 2c, 3c). Similar counts of individuals entangled along east (ni = 670) and west coast (ni = 654) were obtained with low reporting from the islands (ni = 26). Fourteen species were reported entangled from both east and west coast with only 4 species recorded from the islands. D. dugon was found to be most frequently entangled along the east coast (63 incidences, ni = 594, contributing to 56% of the total numbers on east coast), followed by Tursiops sp. (11 incidences, ni = 14, 9% of the east coast dataset). On the west coast, Tursiops sp. was the most frequently entangled (18 incidences, ni = 117, contributing to 18% of the west coast dataset), followed by N. phocaenoides (17 incidences, ni = 34, contributing to 17% of the dataset). The total number of DFP being entangled from west coast (ni = 623) were higher than east coast (ni = 68). More dugong individuals were entangled along east coast (i.e., from Tamil Nadu; ni = 594) as compared to the west coast (i.e., Gujarat; ni = 3) and Islands (i.e., Andaman and Nicobar; ni = 19). D. dugon was the most frequently entangled species in the islands (19 incidences, ni = 19, contributing to 79% of the total numbers in islands dataset) followed by false killer whale Pseudorca crassidens (3 incidences, ni = 5, contributing to 12% of the islands dataset). Very few BW or SBW (11 incidences, ni = 11) were recorded accidently entangled throughout the Indian coastline.StrandingsMarine mammals stranding reports consisted of 91.93% dead (ni = 581) and 8.07% live strandings (ni = 51) (Figs. 1d, 2d, 3d). Considering mass strandings as strandings with ni > 2 (excluding mother and calf;33,34), 8.5% of all reports were mass strandings (21 strandings, ni = 1054). Most of the records did not have information about the sex of the stranded animal (83%), the age class (88%) or the state of decomposition of the carcass (53%). Highest strandings were reported of dugongs (strandings = 190, ni = 228), followed by BW (strandings = 178, ni = = 190), DFP (strandings = 157, ni = = 552) and SBW (strandings = 47, individuals = 48). There were 54 incidences (ni = 54, 9% of total stranding data) where the animal was not identified reliably to include in either of the groups.Species composition and frequencies of strandings were different on east coast, west coast and in the islands (Fig. 1, Table 1). Twenty-two species were reported as stranded on the east coast with D. dugon as the most frequently stranded species (83 incidences, ni = 107, ~ 29% of all records), followed by Indo-Pacific humpback dolphin Sousa chinensis, (31 incidences, ni = 108, ~ 10% of all records). On the west coast, out of 20 species reported as stranded, Balaenoptera musculus was most frequent (28 incidences, ni = 29, ~ 12% of all records) followed by N. phocaenoides (23 incidences, ni = 39, ~ 10% of all records). In the islands, 13 species were reported as stranded, D. dugon (93 incidences, ni = 102, contributing to 77% of the total animals found on the islands) followed by strandings of sperm whale Physeter macrocephalus (8 incidences, ni = 8, contributing to 6% of the data; Table 1).
a. Baleen whales
Table 1 Number of stranding events reported for marine mammals between 1748–2017 in India from the east coast, the west coast and Lakshadweep and Andaman & Nicobar archipelagos.Full size tableA total of 178 BW strandings (ni = 190) were reported. Most species were unidentified (east coast ni= 27, west coast ni = 58, islands ni = 4; i.e., 47% of the data). Identified strandings comprised of 6 species (see Table 1), some of which were later found to be misidentification (no confirmed evidence for common Minke Whale Balaenoptera acutorostrata, Sei Whale Balaenoptera borealis and Fin Whale Balaenoptera physalus from Indian waters; MMRCNI, 2018). Higher number of strandings occurred on the west coast (ni = 126), as compared to east coast (ni = 60). The east and west coast reported all six species of BW, whereas only three species stranded on the islands. B. borealis (misidentified) was the most stranded species across the east coast (12 incidences, ni = 12, contributing to 11% of the data) whereas blue whale Balaenoptera musculus was the most frequent across the west coast (28 incidences, ni = 29, contributing to 11% of the data). Baleen whale strandings were rare in the islands (4 incidences, ni = 4).Forty-seven SBW strandings (ni = 48) were reported along the Indian coast. More SBW stranded on the east coast (ni = 23) as compared to the west coast (ni = 13) and the islands (ni = 12). P. macrocephalus was most frequently reported (70% of all SBW records, east coast ni = 20, west coast ni = 6, islands ni = 8).There were 157 strandings (ni =552) of DFP belonging to 14 species. Twenty-one of these events were mass strandings (ni > 2). The largest mass stranding event (ni = 147) occurred of short-finned pilot whale Globicephala macrorhynchus along the west coast (Tamil Nadu). Higher number of DFP strandings were recorded from east coast (ni = 418) as compared to west coast (ni = 83) and the islands (ni = 51; Table 1). East coast received a higher diversity of stranded DFP (number of species = 11) as compared to west coasts (number of species = 9) and the islands (number of species = 3). S. chinensis was the most frequently stranded species along the east coast (31 incidences, ni = 108, contributing to 33% of the data) whereas N. phocaenoides was the most frequent along the west coast (23 incidences, ni = 39, contributing to 37% of the data; Table 1).
d. Dugongs
The current distribution of dugongs in India is in the shallow coastal waters of Gujarat, Tamil Nadu and Andaman & Nicobar Islands37,38. There are 190 stranding events recorded between the years 1893 and 2017. The highest number of stranded dugongs were recorded from Tamil Nadu (ni = 107) closely followed by Andaman and Nicobar Islands (ni = 102) and few records from Gujarat (ni = 19).Temporal stranding patternsOur analysis of temporal trends for the last 42 years (1975–2017) showed that the mean number of strandings along the Indian coast was 11.25 ± SE 1.39 / year. The number of stranding reports show an increasing trend for two decades after 1975, dropping between 1995 and 2004. We observed a distinct rise in strandings post 2005 (18.23 ± SE 2.98 / year) with the highest reports from 2015–17 (27.66 ± SE 8.51/year) (Fig. 4).
a. Baleen whales
Figure 4A beanplot of decadal trends in marine mammal stranding in India from data compiled between years 1975–2017. Data prior to 1975 was discontinuous over the years to be considered for decadal trends. The data for last decade considered here includes only two years (2015–17) where increased reporting is evident. The bold horizontal lines indicate the mean number of strandings in each decade whereas the smaller horizontal lines indicate stranding numbers recorded for each year within the decade.Full size imageOn the west coast, mean stranding rate throughout the years (1975–2017) was 0.0010 ± SE 0.0014 strandings/km, and a steady rise was observed in rate of reported strandings after 2010. A seasonal trend was observed as well, with a peak in the month of September (sr = 0.0061 ± SE 0.0016 strandings/km), i.e., towards the end of monsoon season, and lowest strandings were recorded in the month of June (sr = 0.0016 ± SE 0.006 strandings/ km) (Fig. 5).Figure 5Temporal patterns (annual and monthly stranding rates / 100 km of coastline) in strandings of marine mammal records obtained from data compiled between years 1975–2017 along east and west coast of India for each group where (a) annual stranding rate and (b) monthly stranding rate for baleen whales (BW); (c) annual stranding rate and (d) monthly stranding rate for dolphins and finless porpoise (DFP); (e) annual stranding rate and (f) monthly stranding rate for sperm and beaked whales (SBW) and (g) annual stranding rate and (h) monthly stranding rate for dugongs.Full size imageThe mean stranding rate of BW on the east coast through 1975–2017 was 0.0013 ± SE 0.0017 strandings/km, but no specific trends were observed according to years or seasons. Stranding rates of BW did not differ between east and west coast (Mann–Whitney U test, U = 390, U standardized = -0.025, p value > 0.05).The stranding rates of SBW differed significantly along both the coasts (Mann Whitney U test, U = 192, U standardized = 0.0, p value More
