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The article presents a study which focuses on the population estimates of the Southern Hemisphere breeding stock D humpback whale (Megaptera novaeangliae) from North West Cape, Western Australia. It mentions that a line intersect methodology was used to investigate the numbers of the whales. It adds that the computation of Pod abundance was made using a Horvitz-Thompson like estimator. Moreover, an increasing breeding stock D population was provided by the study, but further surveys are needed to confirm whether the population is increasing at its maximum rate.

Background: The use of multi-sensor tags is increasingly providing insights into the behavior of whales. However, due to limitations in tag attachment duration and the transmission bandwidth of the Argos system, little is known about fine-scale diving behavior over time or the reliability of assigning behavioral states based on horizontal movement data for whale species. How whales use the water column while migrating has not been closely examined, yet the strategy used is likely to influence the vulnerability of whales to ship strike. Here we present information from a rare week long multi-sensor tag deployment on a pygmy blue whale (Balaenoptera musculus brevicauda) that provided a great opportunity to examine the fine-scale diving behavior of a migrating whale and to compare the occurrence of feeding lunges with assigned behavioral states. Results: The depth of migratory dives was highly consistent over time and unrelated to local bathymetry. The mean depth of migratory dives (~13 m when corrected for the tag position on the whale) was just below the threshold depth (12 m) that blue whales are predicted to travel below to remove the influence of wave drag at the surface. The whale spent 94 % of observed time and completed 99 % of observed migratory dives within the range of large container ship drafts (<24 m). Areas of high residence identified using the horizontal movement data (FastLoc GPS) did not reflect where lunge feeding occurred. Conclusions: The lack of correspondence between areas of high residence inferred from horizontal movement data and the locations where the whale performed feeding lunges highlights the need for further research to determine whether movement models can accurately detect whale feeding areas or only areas of prey searching. While migrating, the whale made dives to a depth that is likely to allow it to avoid wave drag and maximize horizontal movement. Although this strategy may reduce energy expenditure during migration, it also placed the whale at greater risk of ship strike for a much longer period than currently thought. If other whales have similar diving behavior to this animal during migration, many whale species may spend much longer periods than currently estimated within the parts of the water column where the risk of ship strike is high. [ABSTRACT FROM AUTHOR]

Long-term data of underwater passive acoustic monitoring (PAM) collected from two sites of pygmy blue whale presence within Australia, the continental shelf off Portland (38.5° S, 141.2° E) and the Perth Canyon (32° S, 115° E) were analysed to compare the acoustic behaviour of eastern Indian Ocean pygmy blue (EIOPB) whales. Pygmy blue whale song detection was consistently higher at the Perth Canyon site than at the Portland sample site. Statistical analysis found there to be a significant difference in the production of song and phrase variants between sites (p < 0.01) with a shorter two-unit (P2) song variant being more common in the Perth Canyon area, while the traditional three-unit (P3) song variant was more frequent off Portland. This was supported by manual and feature space analysis techniques. Increasing song complexity was observed in the form of phrases with broken song units, a phenomenon that was first observed at the Portland site on isolated occasions but has occurred and proliferated in the Perth Canyon area from 2016 onwards. Analysis of environmental conditions indicated that increased background noise due to multiple EIOPB whales vocalising, as well as water depth, may influence song length. This was reflected by songs made up of shorter phrases dominating in higher background noise conditions and deeper water, while longer more complex phrase types dominate in quieter, shallower conditions. Further research is recommended to isolate any potential influence of environmental factors on song production. [ABSTRACT FROM AUTHOR]

Early 'Discovery mark' data together with recent photo-identification, acoustic, genetic and satellite-radio tag data revealed linkages between humpback whales migrating from breeding grounds (C) off East Africa and the Area III feeding area, from Western Australian breeding grounds (D) and the Antarctic Area IV feeding area and the East Australian breeding grounds (E1) and Antarctic Area V feeding area. These data also revealed low levels of intermingling between (E1) and (D) humpback whales in the Antarctic Area IV feeding area consistent with these being separate populations. Greenpeace photographed the ventral tail flukes of 30 individual humpback whales in the Antarctic Area IV feeding area (70°E-130°E) from 2 to 9 January 2008, between 62°47'S and 64°14'S latitude and 80°00'E and 112°57'E longitude. Comparisons of the Antarctic Area IV Greenpeace fluke catalogue (n = 30) with existing reconciled fluke catalogues from East Africa (n = 842), Western Australia (n = 1,558) and Eastern Australia (n = 1,964), yielded no photo-identification matches. An analysis of the frequencies of whales seen and not seen in Antarctica, East Africa, Western Australia and Eastern Australia relative to the frequencies expected to have been seen and not seen, based on the estimated population sizes and the sizes of the catalogues, provided evidence that the Antarctic whales photographed are from a different population to the East African and East Australian populations. There was weak evidence supporting the hypothesis that the Antarctic whales are from the Western Australian population but insufficient data were available to determine a clear outcome. A comparison of the Antarctic Area IV Greenpeace catalogue (n = 30) with other existing African, Indian Ocean, Western and Eastern Australian and/or Antarctic catalogues, together with increased sampling across the humpback whale feeding season in Antarctica and along the Western and Eastern Australian coastline during their winter migration, is likely to provide further evidence of the migratory destination of these humpback whales. It will also add to our limited knowledge of the extent of population overlap within the Antarctic Area III, IV and V feeding areas. [ABSTRACT FROM AUTHOR]

ABSTRACT Despite being identified as a driver of mobile predator aggregations (hotspots) in both marine and terrestrial environments, topographic complexity has long remained a challenging concept for scientists to visualise and a difficult parameter to estimate. It is only with the advent of high-speed computers and the recent popularisation of geographical information systems ( GIS) that terrain attributes have begun to be quantitatively measured in three-dimensional space and related to wildlife dynamics, making the well-established field of geomorphometry (or 'digital terrain modelling') a discipline of growing appeal to biologists. Although a diverse array of numerical metrics is now available to describe the shape, geometry and physical properties of natural habitats, few of these are known to, or adequately used by, ecologists. In this review, we examine the nature and usage of 56 geomorphometrics extracted from the ecological modelling literature over a period of 32 years (1979-2011). We show that, in studies of mobile predators, numerous topographic variables have largely been overlooked in favour of single basic metrics that do not, on their own, fully capture the complexity of continuous landscapes. Based on a simulation approach, we assess the redundancy and correlation structure of these metrics and demonstrate that a majority are highly collinear. We highlight a suite of 7-8 complementary metrics which best explain topographic patterns across a bathymetric grid of the west Australian seafloor, and contend that field and analytical protocols should prioritise variables of these types, particularly when the responses of predator populations to physical habitat features are of interest. We suggest that prominent structures such as canyons, seamounts or mountain chains can serve as useful proxies for predator hotspots, especially in remote locations where access to high-resolution biological data is often limited. [ABSTRACT FROM AUTHOR]

In understanding the impact of commercial whaling, it is important to estimate the mixing of low latitude breeding populations on Antarctic feeding grounds, particularly the endangered humpback whale populations of Oceania. This paper estimates the degree of genetic differentiation among the putative populations of Oceania (New Caledonia, Tonga, the Cook Islands and French Polynesia) and Australia (western Australia and eastern Australia) using ten microsatellite loci and mtDNA, assesses the power of the data for a mixed-stock analysis, determines ways to improve statistical power for future studies and estimates the population composition of Antarctic samples collected in 2010 south of New Zealand and eastern Australia. A large proportion of individuals could not be assigned to a population of origin (> 52%) using a posterior probability threshold of > 0.90. The mixed-stock analysis simulations however, produced accurate results with humpback whales reapportioned to their population of origin above the 90% threshold for western Australia, New Caledonia and Oceania grouped using a combined mtDNA and microsatellite dataset. Removing the Cook Islands, considered a transient region for humpback whales, from the simulation analysis increased the ability to reapportion Tonga from 86% to 89% and French Polynesia from 89% to 92%. Breeding ground sample size was found to be a factor influencing the accuracy of population reapportionment whereas increasing the mixture or feeding ground sample size improved the precision of results. The mixed-stock analysis of our Antarctic samples revealed substantial contributions from both eastern Australia (53.2%, 6.8% SE) and New Caledonia (43.7%, 5.5% SE) [with Oceania contributing 46.8% (5.9% SE)] but not western Australia. Despite the need for more samples to improve estimates of population allocation, our study strengthens the emerging genetic and non-genetic evidence that Antarctic waters south of New Zealand and eastern Australia are used by humpback whales from both eastern Australia and the more vulnerable breeding population of New Caledonia, representing Oceania. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection. [ABSTRACT FROM AUTHOR]