Oceanographic and atmospheric phenomena influence the abundance of whale sharks at Ningaloo Reef, Western Australia

Sleeman, Jai C.; Meekan, Mark G.; Fitzpatrick, Ben; Steinberg, Craig; Ancel, R.; Bradshaw, Corey J. A.

doi:10.1016/j.jembe.2009.10.015

Cited by 36 publications

(41 citation statements)

References 27 publications

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“…In other regions, such as the southern Gulf of Mexico and Djibouti, it has been suggested that regional upwelling along steep, shelf edges may also be the most important driver influencing the presence of whale shark aggregations (de la Parra Venegas et al , Rowat et al 2011. In Ningaloo Reef, Western Australia, whale shark abundances have been correlated with environmental variables, such as currents, water temperatures, and the Southern Oscillation Index, which has been suggested by the authors to also serve as a proxy for food availability (Taylor & Pearce 1999, Wilson et al 2001, Sleeman et al 2010. Whale shark distribution has been linked to nutrient-rich waters with elevated chlorophyll levels in other regions, including, but not limited to, Japan, Western Australia, India, and the Galapagos Islands (Iwasaki 1970, Compagno 1984, Arnbom & Papastavrou 1988, Taylor & Pearce 1999, Hsu et al 2007, Kumari & Raman 2010.…”

Section: Discussionmentioning

confidence: 99%

Feeding habitat of the whale shark Rhincodon typus in the northern Gulf of Mexico determined using species distribution modelling

McKinney¹,

Hoffmayer²,

Wu³

et al. 2012

Mar. Ecol. Prog. Ser.

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Whale shark Rhincodon typus is a globally distributed species, but there is a lack of knowledge pertaining to their biology, seasonal occurrence, and distribution in the northern Gulf of Mexico (NGOM). Understanding critical habitat for whale sharks is essential on both a regional and global basis for proper management because of their large migratory range. The goal of the present study was to describe the regional distribution of whale shark feeding aggregations in the NGOM by exploiting a presence-only dataset collected as a part of a volunteer sighting survey. Whale shark aggregations have been documented in large numbers in the NGOM since 2003, and species distribution models provide a unique approach to analyzing these presence data. We used maximum entropy and ecological niche factor analysis, 2 algorithms designed for predicting species distribution based only on presence data, to analyze data for the summer period in 2008 and 2009. Cohen's kappa (kappa) and the 'area under the receiver operating characteristic curve' (AUC) were used to evaluate model performance with an external testing dataset. Kappa values ranged from 0.28 to 0.69, and AUC values ranged from 0.73 to 0.80, indicating that the predicted distribution had a fair to substantial agreement with the testing data. Distance to continental shelf edge, distance to adjacent petroleum platforms, and chlorophyll a were the variables most strongly related to whale shark sightings, likely due to an association of these variables with high food availability. Suitable habitat was predicted along the continental shelf edge, with the most suitable habitat predicted south of the Mississippi River Delta. The spatial distribution of suitable habitat is dynamic; therefore, a multi-year study is underway to better delineate temporal trends in regional whale shark distribution and to identify consistent areas of high suitability. Presenceonly habitat models are a powerful tool for delineating important regional habitat for a vulnerable, highly migratory species.

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Section: Discussionmentioning

confidence: 99%

Feeding habitat of the whale shark Rhincodon typus in the northern Gulf of Mexico determined using species distribution modelling

McKinney¹,

Hoffmayer²,

Wu³

et al. 2012

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

show abstract

“…However, with the benefit of hindsight, the substantial variability in sighting data from this area in summer months (Cliff et al 2007) and seasonal changes in their oceanic distribution (Sequeira et al 2012) makes it difficult to attribute these earlier data to a genuine population decline. Even with comparatively robust datasets, such as for whale sharks at Ningaloo Reef in Australia, scientific consensus on population trends has proven difficult to attain due to differences in the underlying assumptions of the models, insufficient data to test which approach is more robust, and the potential influence of environmental variability (Wilson et al 2001, Meekan et al 2006, Bradshaw et al 2007, Holmberg et al 2008, 2009, Sleeman et al 2010a). In another planktivorous elasmobranch, the basking shark Cetorhinus maximus, a steep decline in catches in a target fishery off Achill Island in Ireland between 1947 and1975 was initially attributed to localised stock depletion (Parker & Stott 1965).…”

Section: Introductionmentioning

confidence: 99%

Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks

Rohner¹,

Pierce²,

Marshall³

et al. 2013

Mar. Ecol. Prog. Ser.

121

145

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Sightings of planktivorous elasmobranchs at their coastal aggregation sites are often linked to biological, environmental and temporal variables. Many large planktivorous elasmobranchs are also globally threatened species, so it is necessary to try and separate population trends from environmentally driven, short-term fluctuations. We investigated the influence of environmental variables on sightings of 3 species of planktivorous elasmobranchs off Praia do Tofo, Mozambique: the reef manta ray Manta alfredi, giant manta ray M. birostris and whale shark Rhincodon typus. We used 8-(2003We used 8-( to 2011We used 8-( ) and 6-yr (2005We used 8-( to 2011 logbook data for manta rays and whale sharks, respectively, and constructed a generalised linear model with animal sightings as the response. Predictors included temporal (year, month, time of day), biological (plankton categories), oceanographic (water temperature, time from high tide, current direction and strength and wave height) and celestial (moon illumination) indices. These predictors best fitted reef manta ray sightings, a coastal species with high residency, but less so for the wider-ranging giant manta rays and whale sharks. We found a significant decline in the standardised sightings time series for the reef manta ray (88%) and whale shark (79%), but not for the giant manta ray.

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“…This sighting may not only be linked to oceanographic processes like warming waters by climate-driven changes (Bigelow & Schroeder, 1953) but also to local productivity events that boost the abundance of zooplankton providing foraging opportunities (Sampson et al, 2010;Jaine et al, 2014) as these have been considered important causes for movement behavior of elasmobranchs (Dewar et al, 2008;Luiz et al, 2009;Sleeman et al, 2010). The concentration of chlorophyll-a has varied spatially and temporally in central Peru; in December 2015 the concentration was high (Imarpe, 2016).…”

Section: Discussionmentioning

confidence: 99%

Southernmost record of the Giant Manta Ray Mobula birostris (Walbaum, 1792) in the Eastern Pacific

Moreno

González-Pestana

2017

Mar Biodivers Rec

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Oceanographic and atmospheric phenomena influence the abundance of whale sharks at Ningaloo Reef, Western Australia

Cited by 36 publications

References 27 publications

Feeding habitat of the whale shark Rhincodon typus in the northern Gulf of Mexico determined using species distribution modelling

Feeding habitat of the whale shark Rhincodon typus in the northern Gulf of Mexico determined using species distribution modelling

Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks

Southernmost record of the Giant Manta Ray Mobula birostris (Walbaum, 1792) in the Eastern Pacific

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