Body mass, muscle, blubber and visceral fat content and their seasonal, spatial and temporal variability in North Atlantic common minke whales

The exponent for predicting total body mass from length has been studied in many species and here data on common minke whales from areas in the North Atlantic is added from both Icelandic and Norwegian research catches. The exponent was found to be not significantly different from 3. In addition seasonal changes in body mass and in the parts of blubber, muscle and visceral fat are reported. The exponent for how blubber mass increases with length is lower than 3. In all cases a significant increase over the season was detected, in particular for the mature animals, and also in girth measurements, particularly at the posterior part of the body. Pregnant females had significantly more blubber than other whales. These results agree with studies on blubber thickness measurements and tissue energy content of Icelandic baleen whales and observed changes in the ecosystem around Iceland during the research period.publishedVersio

Oceanic drivers of sei whale distribution in the North Atlantic

NRM was supported by Colciencias (Departamento Administrativo de Ciencia, Tecnología e Innovación, Colombia), the University of St Andrews, and NAMMCO.This study investigated the oceanic drivers of sei whale (Balaenoptera borealis) distribution in the central and eastern North Atlantic, and explored how distribution may have changed over almost three decades. Cetacean sightings data were available from Icelandic, Faroese and Norwegian surveys conducted throughout the central and eastern North Atlantic during summer between 1987 and 2015. Effective strip half width was estimated from the data to take account of variation in detection probability. Spatially-referenced environmental variables used as predictors in generalised additive models of sei whale relative density included: relief-related variables seabed depth, slope and aspect; monthly-varying physical oceanographic variables sea surface temperature (SST), mixed layer depth, bottom temperature, salinity, and sea surface height anomaly (SSH); and monthly-varying biological oceanographic variables chlorophyll-a concentration and primary productivity. Preliminary analysis considered which month (March-August) in the dynamic oceanographic variables explained most variability in sei whale density. Models including all variables (“full models”) could only be run for 1998-2015 because data for several variables were missing in earlier years. “Simple models" including only relief-related variables and SST were therefore run for 1987-89, and also for 1998-2015 for comparison. The best-fitting full model for 1998-2015 retained the covariates depth, May SST, May bottom temperature, July salinity, July SSH and July primary productivity. Of these, depth, May SST and July SSH were the strongest predictors of sei whale density. In the simple models for both 1987-89 and 1998-2015, depth (especially), May SST and seabed slope were the strongest predictors of sei whale density. The highest densities of sei whales were predicted in the Irminger Sea and over the Charles-Gibbs Fracture Zone; a pattern driven by large negative SSH, deep water (>1500m) and polar-temperate SST (5-12oC). There was some inter-annual variability in predicted distribution and there appears to be a northward expansion in distribution consistent with prey species responding to ocean warming. The models could be used to predict future distribution of sei whales based on future environmental conditions predicted by climate models.Publisher PDFPeer reviewe

A note on the distribution and abundance of blue whales (<i>Balaenoptera musculus</i>) in the Central and Northeast North Atlantic

The distribution and abundance of blue whales (Balaenoptera musculus) was assessed from ship surveys conducted in the Central and Northeast Atlantic in 1987, 1989, 1995 and 2001. Blue whales were most commonly sighted off western Iceland, and to a lesser extent northeast of Iceland. They were very rare or absent in the Northeast Atlantic. Sightings were combined over all surveys to estimate the detection function using standard line transect methodology, with the addition of a covariate to account for differences between surveys. Total abundance was highest in 1995 (979, 95% CI 137-2,542) and lowest in 1987 (222, 95% CI 115-440). Uncertainty in species identity had little effect on estimates of abundance. There was a significant positive trend in abundance northeast of Iceland and in the total survey area

Minke whale abundance estimation from the NASS 1987 and 2001 aerial cue–counting surveys taking appropriate account of distance estimation errors

We estimate the abundance of minke whales (Balaenoptera acutorostrata) from the Icelandic coastal shelf aerial surveys carried out as part of the 1987 and 2001 North Atlantic Sightings Surveys (NASS). In the case of the 1987 survey, the probability of detecting animals at distance zero (g(0)) is very close to 1 but there is substantial random measurement error in estimating distances. To estimate abundance from these data, we use methods which assume g(0)=1 but which includea distance measurement error model. In the case of the 2001 survey, measurement errors were sufficiently small to be negligible, and we use double platform methods which estimate g(0) and assume no measurement error to estimate abundance. From the 1987 survey, we estimate abundance to be 24,532 animals, with 95% CI (13,399; 44,916). From the 2001 NASS survey data, minke whale abundance is estimated to be 43,633 animals, with 95% CI (30,148; 63,149).Publisher PDFPeer reviewe

Estimating distance sampling detection functions when distances are measured with errors

Distance sampling methods assume that distances are known but in practice there are often errors in measuring them. These can have substantial impact on the bias and precision of distance sampling estimators. In this paper we develop methods that accommodate both systematic and stochastic measurement errors. We use the methods to estimate detection probability in two surveys with substantial measurement error. The first is a shipboard line transect survey in the North Sea in which information on measurement error comes from photographically measured distances to a subset of detections. The second is an aerial cue-counting survey off Iceland in which information on measurement error comes from pairs of independently estimated distances to a subset of detections. Different methods are required for measurement error estimation in the two cases. We investigate by simulation the properties of the new estimators and compare them to conventional estimators. They are found to perform better than conventional estimators in the presence of measurement error, more so in the case of cue-counting and point transect estimators than line transect estimators. An appendix on the asymptotic distributions of conditional and full likelihood estimators is available online.</p

Combined line-transect and cue-count estimate of sperm whale abundance in the North Atlantic, from Icelandic NASS-2001 shipboard survey

Sperm whales (Physeter macrocephalus) pose a particular problem to shipboard surveys as they dive for extended periods and are therefore likely to be missed (not available) even if they are right under the track line. To address these problems the NAMMCO planning committee for the NASS 2001 survey drew up guidelines to be followed when sperm whales were sighted. This required every deep dive to be recorded and considered to be a cue, from which a cue-count estimate is calculated if the cue rate is known. For those whales that did not dive before coming abeam, a conventional line-transect estimate is calculated, which gives an instantaneous surface estimate from which a total estimate can be obtained if the proportion of the time spent at the surface is known. These estimates are compared and combined. Precise dive cycle information is missing for the mostly single all male sperm whales in this area but a preliminary estimate of 11,185 (cv 0.34) is obtained for the surveyed area with an assumed surface time of 20% and two deep dives per hour

Trends in the distribution and abundance of cetaceans from aerial surveys in Icelandic coastal waters, 1986-2001

Aerial surveys were carried out in coastal Icelandic waters 4 times between 1986 and 2001 as part of the North Atlantic Sightings Surveys. The surveys had nearly identical designs in 3 of the 4 years. The target species was the minke whale (Balaenoptera acutorostrata) but all species encountered were recorded. Sighting rate and density from line transect analysis were used as indices of relative abundance to monitor trends over the period, and abundance estimates corrected for perception biases were calculated for some species from the 2001 survey. More than 11 species were sighted, of which the most common were the minke whale, humpback whale (Megaptera novaeangliae), dolphins of genus Lagenorhychus, and the harbour porpoise (Phocoena phocoena). Minke whales anddolphins showed little change in distribution or abundance over the period. There were an estimated 31,653 (cv 0.30) dolphins in the survey area in 2001. Humpback whales increased rapidly at a rate of about 12%, with much of the increase occurring off eastern and northeastern Iceland. In 2001 there were an estimated 4,928 (cv 0.463) humpback whales in the survey area. The relative abundance of harbour porpoises decreased over the period, but estimates for this species were compromised by uncorrected perception biases and poor coverage. The ecological and historical significance of these findings with respect to previous whaling activities and present-day fisheries is discussed

Minke whale abundance estimation from the NASS 1987 and 2001 aerial cue–counting surveys taking appropriate account of distance estimation errors

We estimate the abundance of minke whales (Balaenoptera acutorostrata) from the Icelandic coastal shelf aerial surveys carried out as part of the 1987 and 2001 North Atlantic Sightings Surveys (NASS). In the case of the 1987 survey, the probability of detecting animals at distance zero (g(0)) is very close to 1 but there is substantial random measurement error in estimating distances. To estimate abundance from these data, we use methods which assume g(0)=1 but which includea distance measurement error model. In the case of the 2001 survey, measurement errors were sufficiently small to be negligible, and we use double platform methods which estimate g(0) and assume no measurement error to estimate abundance. From the 1987 survey, we estimate abundance to be 24,532 animals, with 95% CI (13,399; 44,916). From the 2001 NASS survey data, minke whale abundance is estimated to be 43,633 animals, with 95% CI (30,148; 63,149)