The Lombard effect in humpback whales

2012 ◽  
Vol 131 (4) ◽  
pp. 3456-3456 ◽  
Author(s):  
Michael Noad ◽  
Rebecca Dunlop ◽  
Douglas Cato
Keyword(s):  
Humpback Whales ◽  
Lombard Effect

scholarly journals The Lombard effect in singing humpback whales: Source levels increase as ambient ocean noise levels increase

2020 ◽  
Vol 148 (2) ◽  
pp. 542-555 ◽  
Author(s):  
Regina A. Guazzo ◽  
Tyler A. Helble ◽  
Gabriela C. Alongi ◽  
Ian N. Durbach ◽  
Cameron R. Martin ◽  
...  
Keyword(s):  
Humpback Whales ◽  
Noise Levels ◽  
Lombard Effect ◽  
Ocean Noise

Modeling cetacean habitat use in an urban coastal area in southeastern Brazil

2020 ◽  
Vol 642 ◽  
pp. 227-240
Author(s):  
L Lodi ◽  
R Tardin ◽  
G Maricato
Keyword(s):  
Habitat Use ◽  
Protected Areas ◽  
Linear Models ◽  
Anthropogenic Activities ◽  
Buffer Zone ◽  
Humpback Whales ◽  
Southeastern Brazil ◽  
Major Influence ◽  
Cetacean Species ◽  
Few Data

Most studies of cetacean habitat use do not consider the influence of anthropogenic activities. We investigated the influence of environmental and anthropogenic variables on habitat use by humpback Megaptera novaeangliae and Bryde’s whales Balaenoptera brydei off the coast of the Brazilian city of Rio de Janeiro. Although there are 2 marine protected areas (MPAs) in this area, few data are available on cetacean habitat use or on the overlap of different cetacean species within these MPAs. Our aim was to evaluate the effectiveness of the MPAs and propose a buffer zone to better protect the biodiversity of the study area. We conducted systematic surveys and developed spatial eigenvector generalized linear models to characterize habitat use by the species in the study area. Habitat use by humpback whales was influenced only by depth, whereas for Bryde’s whales there was the additional influence of anthropogenic variables. For Bryde’s whales, which use the area for feeding, sea surface temperature and the distance to anchorages had a major influence on habitat use. We also showed that neither of the MPAs in the study area adequately protects the hotspots of either whale species. Most of the humpback whale grid cells with high sighting predictions were located within 2 km of the MPAs, while areas of high sighting prediction of Bryde’s whales were located up to 5 km from the MPAs, closer to beaches. Our findings provide important insights for the delimitation of protected areas and zoning of the MPAs.

scholarly journals Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

2021 ◽  
Vol 13 (11) ◽  
pp. 2074
Author(s):  
Ryan R. Reisinger ◽  
Ari S. Friedlaender ◽  
Alexandre N. Zerbini ◽  
Daniel M. Palacios ◽  
Virginia Andrews-Goff ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Predictive Models ◽  
Regional Variation ◽  
Large Scale ◽  
Predictive Performance ◽  
Humpback Whale ◽  
Machine Learning Algorithms ◽  
Humpback Whales ◽  
Environmental Covariates ◽  
Animal Habitat

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.

scholarly journals Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

2021 ◽  
Author(s):  
Paulino José García-Nieto ◽  
Esperanza García-Gonzalo ◽  
José Pablo Paredes-Sánchez
Keyword(s):  
Critical Temperature ◽  
Optimization Algorithm ◽  
Regression Models ◽  
Chemical Properties ◽  
Atomic Radius ◽  
Elastic Net ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Humpback Whales ◽  
Whale Optimization

AbstractThis study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

scholarly journals Interannual comparison of core taxa and community composition of the blow microbiota from East Australian humpback whales

2019 ◽  
Vol 95 (8) ◽  
Author(s):  
C Vendl ◽  
B C Ferrari ◽  
T Thomas ◽  
E Slavich ◽  
E Zhang ◽  
...  
Keyword(s):  
Multiple Stressors ◽  
Physiological State ◽  
Temporal Stability ◽  
Individual Variability ◽  
Habitat Destruction ◽  
Humpback Whales ◽  
Large Individual ◽  
Rrna Gene ◽  
Alpha And Beta Diversity ◽  
Transient Nature

ABSTRACT Cetacean represent vulnerable species impacted by multiple stressors, including reduction in prey species, habitat destruction, whaling and infectious disease. The composition of blow microbiota has been claimed to provide a promising tool for non-invasive health monitoring aiming to inform conservation management. Still, little is known about the temporal stability and composition of blow microbiota in whales. We used East Australian humpback whales (Megaptera novaeangliae) as a model species and collected blow and control samples in August 2016 and 2017 for an interannual comparison. We analysed the blow by barcode tag sequencing of the bacterial 16S rRNA gene. We found that the microbial communities in 2016 and 2017 were statistically similar regarding alpha and beta diversity but distinct to seawater. Zero-radius operational taxonomic units (zOTUs) shared by both groups accounted for about 50% of all zOTUs present. Still, the large individual variability in the blow microbiota resulted in a small number of core taxa (defined as present in at least 60% of whales). We conclude that the blow microbiota of humpback whales is either generally limited and of transient nature or the reduced airway microbiota is the symptom of a compromised physiological state potentially due to the challenges of the whales‘ annual migration.

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

scholarly journals Distribution of rorquals and Atlantic cod in relation to their prey in the Norwegian high Arctic

Polar Biology ◽  
2021 ◽  
Author(s):  
Hiroko K. Solvang ◽  
Tore Haug ◽  
Tor Knutsen ◽  
Harald Gjøsæter ◽  
Bjarte Bogstad ◽  
...  
Keyword(s):  
Prey Species ◽  
Barents Sea ◽  
Atlantic Cod ◽  
Spatial Association ◽  
High Arctic ◽  
Humpback Whales ◽  
Predatory Fish ◽  
Detection Range ◽  
Minke Whales ◽  
Acoustic Methods

AbstractRecent warming in the Barents Sea has led to changes in the spatial distribution of both zooplankton and fish, with boreal communities expanding northwards. A similar northward expansion has been observed in several rorqual species that migrate into northern waters to take advantage of high summer productivity, hence feeding opportunities. Based on ecosystem surveys conducted during August–September in 2014–2017, we investigated the spatial associations among the three rorqual species of blue, fin, and common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0-group fish, Atlantic cod, and capelin) in Arctic Ocean waters to the west and north of Svalbard. During the surveys, whale sightings were recorded by dedicated whale observers on the bridge of the vessel, whereas the distribution and abundance of cod and prey species were assessed using trawling and acoustic methods. Based on existing knowledge on the dive habits of these rorquals, we divided our analyses into two depth regions: the upper 200 m of the water column and waters below 200 m. Since humpback whales were absent in the area in 2016 and 2017, they were not included in the subsequent analyses of spatial association. No association or spatial overlap between fin and blue whales and any of the prey species investigated was found, while associations and overlaps were found between minke whales and zooplankton/0-group fish in the upper 200 m and between minke whales and Atlantic cod at depths below 200 m. A prey detection range of more than 10 km was suggested for minke whales in the upper water layers.

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