Humpback whale abundance south of 60°S from three complete circumpolar sets of surveys

Austral summer estimates of abundance are obtained for humpback whales (Megaptera novaeangliae) in the Southern Ocean from the IWC’s IDCR and SOWER circumpolar programmes. These surveys have encircled the Antarctic three times: 1978/79–1983/84 (CPI), 1985/86–1990/91 (CPII) and 1991/92–2003/04 (CPIII), criss-crossing strata totalling respectively 64.3%, 79.5% and 99.7% of the open-ocean area south of 60°S. Humpback whales were absent from the Ross Sea, but were sighted in all other regions, and in particularly high densities around the Antarctic Peninsula, in Management Area IV and north of the Ross Sea. Abundance estimates are presented for each CP, for Management Areas, and for assumed summer feeding regions of each Breeding Stock. Abundance estimates are negatively biased because some whales on the trackline are missed and because some humpback whales are outside the survey region. Circumpolar estimates with approximate midpoints of 1980/81, 1987/88 and 1997/98 are 7,100 (CV = 0.36), 10,200 (CV = 0.30) and 41,500 (CV = 0.11). When these are adjusted simply for unsurveyed northern areas, the estimated annual rate of increase is 9.6% (95% CI 5.8–13.4%). All Breeding Stocks are estimated to be increasing but increase rates are significantly greater than zero only for those on the eastern and western coasts of Australia. Given the observed rates of increase, the current total Southern Hemisphere abundance is greater than 55,000, which is similar to the summed northern breeding ground estimates (~60,000 from 1999–2008). Some breeding ground abundance estimates are far greater, and others far lower, than the corresponding IDCR/SOWER estimates, in a pattern apparently related to the latitudinal position of the Antarctic Polar Front

Separating pygmy and Antarctic blue whales using ovarian corpora

Two Southern Hemisphere subspecies of blue whales exist: pygmy blue whales are shorter (≤ 79 ft, 24.2 m) and generally found north of 54°S in summer, while Antarctic (true) blue whales exceed 100 ft (30.5 m) and are found in more southerly waters. Abundance estimates of Antarctic blue whales rely on sightings south of 60°S but at-sea identification is difficult and these sightings may include some proportion of pygmy blue whales. Ovarian corpora (corpora lutea plus corpora albicantia) are permanent ovulation records that can be used to estimate this proportion. Pregnant females of the two subspecies may overlap at 72–79 ft (21.9–24.1 m), but pygmy blue whales at these lengths have high (> 4) corpora counts, contrasting with immature or newly mature Antarctic blue whales (0–3 corpora). Published papers yielded pairs of length-corpora data for 104 pygmy and 2,064 Antarctic region blue whales. The relationship between length and ovarian corpora counts is well fitted by logistic models (with negative binomial variability). A mixture model estimates that 0.4% (95% confidence interval 0.0–1.1%) of Antarctic region blue whales were pygmy blue whales, much lower than the “less than 7%” currently accepted by the IWC. If later ovarian corpora data (1947–51) are separately analysed, the estimated proportion is zero (95% CI = 0.0–0.5%), suggesting that the pygmy proportion in the Antarctic did not increase when Antarctic blue whales were greatly depleted. No support is found for Ichihara’s suggestion that high (>7) ovarian corpora counts in 78–81 ft Antarctic region catches were pygmy blue whales. These whales are instead explained by natural variability in Antarctic blue whales. These methods could be applied to blue whale males through the analysis of testes weight, and may hold promise in separating catches of other species with diminutive forms such as fin and minke whales

Analysis of simulated Antarctic minke surveys using the "standard" method and the "direct duplicate" method

Four new scenarios (sc33–sc36) of simulated survey data are analysed using the “standard” distance sampling method and the direct duplicate method of Palka (1995). The new scenarios were “blind” in that true simulated densities and the factors included were not revealed. Estimated densities were 0.029, 0.021, 0.082, and 0.063 whales per km 2 for the four scenarios using the “standard” method, and 0.029, 0.020, 0.081, and 0.062 whales per km 2 for the direct duplicate method. Some negative bias in estimates is expected from the standard method due to whales on the trackline being missed by the surveys. If true, the direct duplicate method then failed to correct for this bias for these scenarios, as resulting estimates were very similar to those obtained from the “standard” method

Comparison of Sower Cpiii abundance estimates For Humpback Whales in area Iv with Jarpa abundance estimates

Estimates of abundance for humpback whales from IDCR/SOWER surveys have been provided up to and including the 1997/98 survey (Branch and Butterworth, 2001). These surveys did not cover the majority of Area IV. Data on humpback whales in Area IV were extracted from DESS for the IDCR/SOWER surveys in CPIII. Two surveys were conducted in parts of this Area: 1994/95 from 40-80°E overlapping the 70-80°E section, and the 1998/99 survey from 80-130°E covering the remainder of Area IV. Estimates were obtained separately for these two surveys. In the 1994/95 survey, the EN and ES strata covered 60- 80°E, overlapping with Area IV from 70-80°E. The Prydz Bay stratum was entirely within Area IV. There were no humpback sightings in Prydz Bay. There were only four sightings in the eastern overlapping strata. Abundance estimates for EN and ES were 53 (CV=1.01) and 84 (CV=0.53) respectively. Assuming half of the abundance was in Area IV, the total abundance in Area IV would be 68 (CV=0.51)

Abundance estimates for Antarctic minke whales from three completed circumpolar sets of surveys, 1978/79 to 2003/04

Abundance estimates are provided for Antarctic minke whales from the ship-based IDCR-SOWER surveys using the standard distance sampling methodology applied in the past in the Scientific Committee. Agreed methods of pooling strata and of estimating mean school size have changed since the most recent published assessment of these surveys by Branch and Butterworth (2001a). The IDCR-SOWER surveys are grouped into three completed circumpolar sets of cruises: 1978/79–1983/84 (CPI), 1985/86–1990/91 (CPII) and 1991/92–2003/04 (CPIII), which respectively covered 64.3%, 79.5% and 99.7% of the ice-free area south of 60°S. Circumpolar abundance estimates are obtained by summing individual surveys in CPI and CPII (each covered one IWC Management Area), and by combining CPIII surveys (some overlapped) using the ‘survey-once’ method—by selecting the single survey offering the best or most recent coverage. When calibrated closing and independent observer mode estimates were inverse-variance weighted, circumpolar abundance estimates were 645,000 (CV = 0.143), 786,000 (CV = 0.094) and 338,000 (CV = 0.079) for CPI, CPII and CPIII respectively. These estimates are negatively biased because some Antarctic minke whales are north of 60°S and inside the pack ice during the surveys, and because some whales on the trackline are missed. After simple extrapolation to account for differences in the latitudes surveyed during each circumpolar set and for the increasing proportions of ‘like minke’ sightings, the ratio of estimates from the three CPs is 0.97:1.00:0.39, echoing previous findings of appreciably lower CPIII estimates. CPIII estimates for individual IWC Management Areas are similarly low, ranging within 18–52% of CPII estimates for Areas I–V, although 159% of CPII for Area VI. Explanations for the appreciably lower abundance estimates include a higher proportion of minke whales within the pack ice and a greater proportion of whales missed on the trackline, but any such hypothesis needs to be reconciled with higher abundance estimates in CPIII than in CPII for blue, humpback, fin, sperm and killer whales based on the same surveys

Abundance of Antarctic blue whales south of 60°S from three complete circumpolar sets of surveys

Sightings from the IDCR/SOWER austral summer surveys are analysed to provide abundance estimates for Antarctic (true) blue whales (Balaenoptera musculus intermedia) south of 60°S. The IDCR/SOWER ship-borne surveys have completely circled the Antarctic three times: 1978/79-1983/84 (CPI); 1985/86-1990/91 (CPII); and 1991/92-2003/04 (CPIII), covering strata totalling 64.3%, 79.5% and 99.7% of the ocean surface between the pack ice and 60°S. During the surveys, blue whale sightings were rare but were recorded in all regions. Raw sighting rates (schools per 1,000 n.mile of primary search effort) were 0.44 (CPI), 0.67 (CPII) and 1.48 (CPIII). Respective circumpolar abundance estimates were 453 (CV=0.40), 559 (CV=0.47) and 2,280 (CV=0.36), with corresponding mid-years of 1981, 1988 and 1998. The CPIII estimates are the most complete and recent for this subspecies. When adjusted for unsurveyed regions in a simple way, the estimated circumpolar rate of increase is 8.2% (95% CI=1.6–14.8%) per year; nevertheless, Antarctic blue whales still number far less than the estimated 202,000-311,000 that existed before exploitation. These abundance estimates are negatively biased because some Antarctic blue whales may have been north of 60°S or in the pack ice at the time of the surveys and because a small number of blue whales on the trackline were probably missed. Furthermore, a small proportion of pygmy blue whales, probably less than 1%, may have been included in the sightings

Female length at sexual maturity for pygmy and Antarctic blue whales based on Soviet ovarian corpora, 1961-72

Female blue whale ovarian corpora data were translated and encoded from the USSR’s Slava (1961/62–1965/66) and Sovietskaya Ukraina (1961/62–1971/72) expeditions. Complete ovarian data were available for 1,425 blue whales (1,272 pygmy, 153 Antarctic). Catches north of 52°S were assumed to be pygmy blue whales (Balaenoptera musculus brevicauda), while those south of 56°S were assumed to be Antarctic (true) blue whales (B. m. intermedia), although there was some evidence for a small proportion (<1%) of both Antarctic blue whales north of 52°S and pygmy blue whales south of 56°S. A small proportion of lengths were rounded to the nearest metre, and many whales shorter than 18.0 were recorded as 18.0 m or greater (whale stretching). A Bayesian logistic model fitted to the data provided estimates of L50 and L95 (the lengths at which 50% and 95% of females are sexually mature). For pygmy blue whales L50 was 19.2 m (95% interval 19.1–19.3 m) and L95 was 20.5 m (95% interval 20.4–20.7 m). These estimates are more precise than those from Japanese data because the Soviet vessels recorded 32 times more pygmy blue whales shorter than the legal minimum length (21.3 m). Among small areas, L50 varied from 18.4 to 19.9 m for pygmy blue whales; all estimates were much shorter than the 23.4 m from the Antarctic. The status of northern Indian Ocean pygmy blue whales is unclear: L50 for these blue whales was statistically significantly shorter than L50 for both the southern Indian Ocean and around Australia, but the magnitude of the differences was small: 0.5–0.6 m

Assessment of the East Greenland-Iceland fin whale population using a four-area model

The East Greenland-Iceland (EGI) fin whale population is modeled as four subpopulations with movement between the following areas: East Greenland (area 1), West Iceland (area 2), East Iceland (area 3) and the Far East (area 4). The model is sex- and age-structured, and is fitted to CPUE, sightings survey abundance, and mark-recapture data using both maximum likelihood and Bayesian approaches. Movement parameters are not differentiated by sex since the inclusion of sex-specific movement parameters did not improve the AIC. For the base case assessment scenario, best fits to the data were obtained when West Iceland and East Iceland are effectively fully mixed with a low level of interchange with East Greenland and little interchange with the Far East region. For the base case and all sensitivity tests, the overall recruited population is increasing and above 74% (base case 84%) of pre-exploitation abundance (K), and subpopulations in all areas are above 68% (base case 78%) of the individual K values. MSYR for the recruited population is 0.020 for the base case and 0.014 to 0.036 for the sensitivity tests. Projections for annual catches of 0, 100, and 200 whales taken from West Iceland indicate that only the last would result in abundance decreases compared to current levels. Under catch levels of 200 whales the probability of the total EGI population falling below 60% of pre-exploitation levels within the next 30 years was 5.7%, 7.3% and 11.5% for the 1+, recruited and mature components of the population, although there was a 51% probability of this occurring for the West Iceland mature component

Applying the direct duplicate method to simulated IDCR/SOWER survey data

The direct duplicate method (Palka, 1994) was applied to simulated IDCR/SOWER survey data. Estimates of whale density were generally negative biased, but less so than estimates obtained using the standard method. The mean bias across scenarios was -11% (range -31% to 8%) for the “2004” scenarios and -5% (range -19% to 10%) for the “2005” scenarios. Negative bias was more pronounced when a density gradient was present, when the detection function used to generate the simulated sightings excluded school size but included weather as a covariate, when errors in recorded school size were introduced, when weather and density were correlated, and when surveys were conducted in IO mode only. This method shows promise although further development is desirable to reduce the associated bias further, perhaps by including weather and school size as covariates

Applying Bayesian model selection to determine ecological covariates for recruitment and natural mortality in stock assessment

Incorporating ecological covariates into fishery stock assessments may improve estimates, but most covariates are estimated with error. Model selection criteria are often used to identify support for covariates, have some limitations and rely on assumptions that are often violated. For a more rigorous evaluation of ecological covariates, we used four popular selection criteria to identify covariates influencing natural mortality or recruitment in a Bayesian stock assessment of Pacific herring (Clupea pallasii) in Prince William Sound, Alaska. Within this framework, covariates were incorporated either as fixed effects or as latent variables (i.e. covariates have associated error). We found most support for pink salmon increasing natural mortality, which was selected by three of four criteria. There was ambiguous support for other fixed effects on natural mortality (walleye pollock and the North Pacific Gyre Oscillation) and recruitment (hatchery-released juvenile pink salmon and a 1989 regime shift). Generally, similar criteria values among covariates suggest no clear evidence for a consistent effect of any covariate. Models with covariates as latent variables were sensitive to prior specification and may provide potentially very different results. We recommend using multiple criteria and exploring different statistical assumptions about covariates for their use in stock assessment.publishedVersio