Species distribution modelling of Bryde’s whales, humpback whales, southern right whales, and sperm whales in the southern African region to inform their conservation in expanding economies

In southern African waters, information about species distribution and habitat preferences of many cetacean species is limited, despite the recent economic growth that may affect them. We determined the relative importance of eight environmental variables (bathymetry, distance to shore, slope, chlorophyll-a, salinity, eastwards sea water velocity, northwards sea water velocity and sea surface temperature) as drivers of seasonal habitat preferences of Bryde’s whales (Balaenoptera brydei), humpback whales (Megaptera novaeangliae), southern right whales (Eubalaena australis) and sperm whales (Physeter macrocephalus). Using presence only data from multiple sources, we constructed predictive species distribution models (SDMs) consisting of ensembles of seven algorithms for these species during both summer and winter. Predicted distribution for all cetaceans was high in southern Africa and, in particular, within the South African Exclusive Economic Zone (EEZ). Predictive models indicated a more pronounced seasonal variation for humpback, sperm and southern right whales than for Bryde’s whales. Southern right whales occurred closer to shore during winter, humpback whales were more likely to occur along the east coast in winter and the west coast in summer, and sperm whales were more concentrated off the shelf in winter. Our study shows that ensemble models using historical, incidental and scientific data, in conjunction with modern environmental variables, can provide baseline knowledge on important environmental drivers of cetacean distribution for conservation purposes. Results of this study can further be used to help develop marine spatial plans and identify important marine mammal areas.

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Humpback whales Megaptera novaeangliae and southern right whales Eubalaena australis in Gerlache Strait, Antarctica

Polar Record ◽

10.1017/s0032247400022300 ◽

1988 ◽

Vol 24 (148) ◽

pp. 15-20 ◽

Cited By ~ 12

Author(s):

Gregory S. Stone ◽

William M. Hamner

Keyword(s):

Open Water ◽

Euphausia Superba ◽

Megaptera Novaeangliae ◽

Humpback Whales ◽

Southern Limit ◽

Right Whale ◽

Eubalaena Australis ◽

Right Whales ◽

Southern Right Whales ◽

The Right

AbstractDuring surveys conducted 2–20 April 1986 in Gerlache Strait, Antarctic Peninsula, 103 humpback whales Megaptera novaeangliae and eight right whales Eubalaena australis were sighted. The right whale sightings extend the southern limit of known distribution for the species. Humpback and right whale densities were respectively 0.22 (sd 0.23) and 0.01 (sd 0.06) whales per survey mile. Highest densities for both species were recorded inside bays, rather than in the relatively open water of Gerlache Strait. Both species were feeding on Antarctic krill Euphausia superba. Twenty-three humpback and four right whales were identified individually using photographs of natural features. Also included are sighting records of 18 southern bottlenose whales Hyperoodon planifrons.

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Species Distribution Models for sea pen corals in the Flemish Cap and Flemish Pass Area (NorthwestAtlantic Ocean)

10.7287/peerj.preprints.26687v1 ◽

2018 ◽

Author(s):

Mar Sacau Cuadrado ◽

Ana Garcia-Alegre Garralda ◽

Maria Grazia Pennino ◽

Francisco Javier Murillo Pérez ◽

Alberto Serrano López ◽

...

Keyword(s):

Species Distribution ◽

Environmental Variables ◽

Species Distribution Models ◽

Habitat Preferences ◽

Marine Spatial Planning ◽

Trawl Survey ◽

Distribution Models ◽

Sea Bottom ◽

Planning Framework

Species Distribution Models (SDMs) are widely used to identify species-environmentrelationships and predicting species occurrence and/or density at un-sampled locations.The SDMs implementation allows describing species geographical trends, toidentify spatial ontogenetic shifts of commercially exploited species and to assessthe effect of climate change on species distribution. Moreover, SDMs could bean essential tool to support the marine spatial planning framework providingessential and easy-to-use interpretation tools, such as predictive distributionmaps, with the final aim of improving management and conservation especially ofvulnerable species as sea pen corals. In this study, a 10-yr period (2007-2017) of a bottom trawl survey was used to estimateand predict the suitability habitat of sea pen species as a function of several environmental variables (i.e. bathymetry, sea bottom temperature, sea bottom salinity, slope, rugosity, aspectof the seabed, etc) in Flemish Cap and Flemish Pass (ATLAS Case Study No 11) using different SDM algorithms. Resultsshow that species exhibit specific habitat preferences and spatial patterns inresponse to environmental variables.

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Predicting suitable coastal habitat for sei whales, southern right whales and dolphins around the Falkland Islands

PLoS ONE ◽

10.1371/journal.pone.0244068 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0244068

Author(s):

Mick Baines ◽

Caroline R. Weir

Keyword(s):

Anthropogenic Activities ◽

Generalized Additive Models ◽

Habitat Preferences ◽

Additive Models ◽

Falkland Islands ◽

Coastal Habitat ◽

Distribution Models ◽

Cetacean Species ◽

Right Whales ◽

Southern Right Whales

Species distribution models (SDMs) are valuable tools for describing the occurrence of species and predicting suitable habitats. This study used generalized additive models (GAMs) and MaxEnt models to predict the relative densities of four cetacean species (sei whale Balaeanoptera borealis, southern right whale Eubalaena australis, Peale’s dolphin Lagenorhynchus australis, and Commerson’s dolphin Cephalorhynchus commersonii) in neritic waters (≤100 m depth) around the Falkland Islands, using boat survey data collected over three seasons (2017–2019). The model predictor variables (PVs) included remotely sensed environmental variables (sea surface temperature, SST, and chlorophyll-a concentration) and static geographical variables (e.g. water depth, distance to shore, slope). The GAM results explained 35 to 41% of the total deviance for sei whale, combined sei whales and unidentified large baleen whales, and Commerson’s dolphins, but only 17% of the deviance for Peale’s dolphins. The MaxEnt models for all species had low to moderate discriminatory power. The relative density of sei whales increased with SST in both models, and their predicted distribution was widespread across the inner shelf which is consistent with the use of Falklands’ waters as a coastal summer feeding ground. Peale’s dolphins and Commerson’s dolphins were largely sympatric across the study area. However, the relative densities of Commerson’s dolphins were generally predicted to be higher in nearshore, semi-enclosed, waters compared with Peale’s dolphins, suggesting some habitat partitioning. The models for southern right whales performed poorly and the results were not considered meaningful, perhaps due to this species exhibiting fewer strong habitat preferences around the Falklands. The modelling results are applicable to marine spatial planning to identify where the occurrence of cetacean species and anthropogenic activities may most overlap. Additionally, the results can inform the process of delineating a potential Key Biodiversity Area for sei whales in the Falkland Islands.

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Species Distribution Modeling And Assessment of Environmental Drivers Responsible To Distribution And Preferred Niche of Critically Endangered Endemic Ornamental Freshwater Fish Species of The Genus Sahyadria

10.21203/rs.3.rs-538109/v1 ◽

2021 ◽

Author(s):

Ajey Kumar Pathak ◽

Pushpendra Verma ◽

Rajesh Dayal ◽

Uttam Kumar Sarkar

Keyword(s):

Endangered Species ◽

Probability Distribution ◽

Fish Species ◽

Species Distribution ◽

Environmental Variables ◽

Environmental Drivers ◽

Distribution Model ◽

Ecological Niches ◽

Threatened And Endangered Species ◽

Annual Range

Abstract India has different bio-climatic zones that support diverse aquatic habitats rich in biodiversity. For effective conservation of endangered species in its habitat, it is essential to know the distribution of the fish species in the environmental range and for this species distribution models are the efficient and innovative tools. The present study used MaxEnt model to develop the probability distribution model highlighting the distribution of the fish species by analyzing the known occurrence records of Denison barb under genus Sahyadria (Sahyadria denisonii Day 1865 and Sahyadria chalakkudiensis (Menon, Rema Devi & Thobias 1999)) in relation to environmental variables typically incorporating seasonal and temporal variability. AUC of the models for Sahyadria species depicted good fitness. Both the species were found sensitive to 'Solar radiation', 'Temperature seasonality' and 'Temperature annual range' and these variables were assessed as the significant predictors. The sensitivity of distribution of both species to these environmental variables was found correlated with their breading and spawning seasons. 'Precipitation' was determined as a significant climatic envelop influencing distribution of the species associated with the river flow. The models showed that distribution of S. denisonii in the higher precipitation areas compared to S. chalakkudiensi. The probability distribution model with respect to distribution of both the species indicates a lineage barrier at Palghat Gap supporting the studies of earlier workers. At the latitudinal scale, prediction of the suitable ecological habitat provides the detailed insight into the distribution of all the genetic lineages of the genus Sahyadria. Evidently, the findings of this study can assist in determining ecological niches for other threatened and endangered species of other areas and may aid in field surveys as well as developing conservation plans.

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