Data

We used published age-specific corpora counts to quantify the rate of reproductive senescence in female toothed whales. In Cetaceans, reproductive history can be inferred from anatomical examination of the ovaries14. After ovulation, the Graafian follicle, in which the ovum is develops, degenerates first into a corpus luteum, and then into a corpus albicans (hereafter collectively corpora) which persists in the ovary39. Corpora counts have been used to infer ovulation rate and other reproductive characteristics in a variety of Cetacean species (e.g.30,39,40). Here we use corpora counts as a measure of ovarian activity and not to estimate pregnancy rates which may differ from the corpora count15,41,42. In earlier studies corpora albicans and corpora atretica may not always have been properly distinguished39, however, as we are measuring ovarian activity, not ovulations per se this will not bias our results.

We undertook a thorough literature search for age-specific corpora count data on all 72 recognised species of Odontocetes43. Our criteria for data inclusion were: each female studied had a count of corpora and an independent estimate of age; female age structure was well represented; and that the data is presented in a clear format to be accurately obtained. Independent estimates of age were based on examination of dentine cemental layers in all species except narwhals, where the racemization of aspartic acid in the eye was used44,45. Recent research has shown that beluga whales deposit growth layer groups annually46,47. We therefore use growth layer group counts as our estimate of beluga whale age- though we note that as PrR is calculated as a proportion systematic age, estimation errors (doubled or halved for example) would not affect our conclusions. Appropriate data was found for sixteen species: Atlantic white-sided dolphin Lagenorhynchus acutus48, Baird’s beaked whale Berardius bairdii49, beluga whale Delphinapterus leucas50, common bottlenose dolphin Tursiops truncatus51,52, false killer whale Pseudorca crassidens53, harbour porpoise Phocoena phocoena54, long-finned pilot whale Globicephala melas40, melon-headed whale Peponocephala electra55, narwhal Monodon monoceros45, Northern right-whale dolphin Lissodelphis borealis56, Pantropical spotted dolphin Stenella attenuata57, short-beaked common dolphin Delphinus delphis15, short-finned pilot whale Globicephala macrorhynchus30, sperm whale Physeter macrocephalus58, spinner dolphin Stenella longirostris59 and striped dolphin Stenella coeruleoalba57. Previous work in resident killer whales (Orcinus orca) has documented significant post-reproductive lifespans using long term individual based observations5,18,19,20 and the post reproductive period has been confirmed using non-invasive hormonal samples21. Currently however, to our knowledge there are no published corpora count data on killer whales of a sufficiently large sample size for a robust test of the rate of reproductive senescence and a calculation of physiological PrR (but see60). Killer whales are not, therefore, included in our analysis of ovarian activity. Data were restricted to include only data from the age of first ovulation, i.e. the age with the first non-zero corpora count. All analysis was performed in R61 with the ggplot2 package used for producing the figures62.

It is important to note that throughout this study we refer to species, but our data is only based (with one exception) on a single population. For one species, the common bottlenose dolphin, data were available from three geographically distinct populations which we analyse independently. Data are also available from two false killer whale populations53, however we only use data from one population (Japan) as the second population (South Africa) may have been reproductively compromised53.

Our analysis is based on the assumption that corpora counts are a reliable measure of ovarian activity across the lifespan, which is supported by detailed examination of ovaries across a range of cetacean species53,58,63. For some species of cetacean however, there is evidence to suggest corpora may regress, and not persist indefinitely15 and in some cases there may be multiple eggs released at a single ovulation event15. However, there is no evidence of age-related changes in either poly-ovulation or regression of corpora, which could otherwise affect our analysis of age-dependent changes in ovarian activity. Indeed, for three species (short- and long-finned pilot whales, false killer whales) both pregnancy and corpora data are available and in both cases changes in pregnancy rate show a strikingly similar age-related pattern to changes in corpora deposition (Fig. 1c,d; Supplementary 2), validating our approach that ovarian activity (corpora count) can be used as a reliable measure of fecundity. To our knowledge this is the first population level examination of the relationship between corpora count and pregnancy rate.

Quantifying reproductive senescence

A physiological decrease in fecundity with age in toothed whales will result in a lower rate of ovulation in older individuals. In populations with decreasing fecundity with age we therefore expect a second order relationship between ovarian activity and age, as older individuals are producing fewer new corpora per unit time. Reproductive senescence will be accompanied by a declining rate of ovarian activity with age. We fitted second order polynomials (which, inversed, decline in rate towards a peak) to each of the sixteen species to investigate this declining ovarian activity (e.g. Fig. 1). The change in ovarian activity with age is described by the slope of the fitted curve. A negative change in ovarian activity is an artefact of fitting a quadratic curve and was therefore treated as 0. We normalised both age and change in ovulation activity to between 0 and 1 to facilitate interspecies comparison.

We used AIC model comparison to investigate if the relationship between corpora count and age were best described by a 2nd order polynomial or linear relationship. A linear relationship is our null assumption as it suggests that there is no decline in physiological reproductive activity through life.

We found a relationship between corpora count and age in thirteen of the sixteen species (detailed fit information; Supplementary 1). For three species we found no correlation between the number of corpora and age (adj-R2 ≤ 0.1), suggesting that either the data are too sparse or that ovarian corpora are not a good measure of reproductive senescence in the species. These three species are: Atlantic white-sided dolphin (adj-R2 = −0.02), harbour porpoise (adj-R2 = 0.07) and the short-beaked common dolphin (adj-R2 = 0.10). No further analysis was performed on these species.

Calculating post-reproductive lifespans

For species with a decline in fecundity with age we then calculated their physiological post-reproductive representation (Phys-PrR). Post-reproductive representation is a population level measure describing the proportion of adult females years in the population that are being lived by post-reproductive females12. As our data are based on ovarian activity we measured the presence of physiologically post-reproductive females in the population (i.e. the proportion of females not ovulating).

The calculation of PrR is based on age-specific measures of survival and fecundity. We calculate age-specific survival from age-cohorts constructed from the original corpora data. Age-cohorts were constructed by making variable bin-widths starting at the oldest female in the study. We used these variable bin widths to construct monotonically decreasing age-cohorts, a pre-requisite for calculating survival from age-cohorts64. Bin widths were calculated in reverse: from the oldest individual. The oldest bin contains only the oldest female in the sample. The lower limit of the next bin was then selected to contain more than one whale, i.e. a greater number of females than the next oldest bin. This process continued until all females were assigned to a bin. In some cases, to fit the assumption of monotonically decreasing age cohorts the first age (youngest) bin for some species had to be smoothed to match the second youngest bin. This method will tend to underestimate late life survival, and therefore underestimate post-reproductive representation. Survival was then calculated from these age cohorts with survival assumed to be evenly spread through each age represented in a cohort. It should be noted that due to the low probability of sampling ‘rare’ ages of individuals, older whales are likely to be underrepresented in our data, further underestimating survival and the significance of the post-reproductive lifespan.

Calculating survival from age-cohort data assumes a stable population. If the population is not at equilibrium then calculation of survival, and therefore PrR, will be inaccurate. For example, in a growing population younger individuals will be overrepresented, underestimating late-life survival, and vice versa64. In the absence of detailed population growth parameters for most cetacean species, we model three population change scenarios in our calculation of Phys-PrR. Firstly, we assume a population at equilibrium, where population growth (r) = 064. Secondly we assume a population in serious decline, r = −0.1, where the total population shrinks by 10% each year. We model the largest possible population growth scenario for each species, up to r = 0.1, given the age-structure of the data64. These values are comparable to the estimated population growth rates of cetacean populations. For example, at the peak of the modern sperm whale fishery between 1945 and 1975 the best estimate of global sperm whale population decline averaged approximately 2.67% (r = −0.027) per year (calculated from13). In contrast, North Atlantic humpback whales (Megaptera novaeangliae) may be recovering from very severe whaling at annual growth rate of 0.073–0.08665.

We used our measure of age-specific ovarian activity as a measure of fecundity. PrR is the summed life-expectancy in years after 95% of population fecundity has been completed (age M). Age M is independent of population change and therefore remains unchanged in the different growth scenarios. Because our data begin from maturity, age B (usually the age at which 5% of lifetime fecundity has been realised) is equal to the first age present in our data. We calculated Phys-PrR for each population change scenario for all ten species with evidence of reproductive senescence.

The significance of our PrR values was calculated by simulating the life-history of individuals based on the real survival and fecundity data. We calculate the estimated Phys-PrR of 1000 populations of 1000 individuals with reproductive senescence equal to somatic senescence12. The reported p values are the number of these simulated populations with a higher Phys-PrR than the real Phys-PrR. Significance is reported as the result of a two-tailed test.

It should be noted that these calculations are based on a stable and representative age structure. For some species (notably in this study sperm whales13 and beluga whales66) hunting pressures may have changed the demographics, with a bias to removing large (old) individuals from the population. For these species, this will lead to an underestimation of the frequency of post-reproductive females in the population, and therefore an underestimation of Phys-PrR.

Phylogenetic ancestral state reconstruction

We combined the results of our Phys-PrR analysis with other published data on late-life reproduction to infer when post-reproductive lifespans have evolved in this clade using phylogenetic comparative methods. For this study we used a consensus tree created from the Bayesian posterior sample of 10,000 trees of the inferred phylogenetic relationships between cetacean species from the 10 k tree project67. This tree was pruned to leave only those species for which we have either physiological measures (n = 13) or other suitable records of reproduction in older females (n = 12; Fig. 3; Supplementary 4), resulting in a phylogenetic tree containing 25 species.

We used a continuous-time Markov chain method68 to model the evolution of post-reproductive lifespans as involving transitions between two states (post-reproductive lifespans present, and post-reproductive lifespans absent). This model has a single parameter, the instantaneous rate of change between these two states (transitions to and from post-reproductive lifespans are fixed to take the same value). We used the ancestral state estimation function in the R package “ape”69 in order to estimate the value for this rate parameter using maximum likelihood estimation. This approach allows us to infer the likely state of post-reproductive lifespans at ancestral nodes in the phylogeny given this model of evolution. These inferences are given as proportional probabilities (range: 0 to 1) and indicate whether ancestral species are likely to have had the trait under consideration.

Ethics statement

All data used in this study are from published corpora counts from dissection of whale corpses. The corpses from each study come from a variety of sources (Supplementary 6). Some are from accidental deaths; five species data are from mass stranding events and four from by-catch in fisheries. Other data are from deliberate killing of whales; two species data are from aboriginal subsistence hunts, one from historical commercial whaling (sperm whales) and six from drive hunts in Japan and the Faroe Islands. The authors wish to state, in the strongest terms, that we in no way condone whaling as a data collection method. The data used here are from historical sources, collected by scientists working alongside commercial operations and no data were used from scientific whaling. We emphasise that terminal sampling is not the best way to collect data on reproductive senescence in cetaceans. Short, but especially long-term detailed demographic studies give much richer data for studying the relative rates of reproductive senescence, social structures and post-reproductive lifespans (e.g. killer whales in the Salish Sea18,23,28,70). In the absence of such published data for cetaceans we have made use of this historical physiological data, but highlight the need for, and value of, detailed individual based longitudinal demographic data in the future.