Biophysical correlates of relative abundances of marine megafauna at Ningaloo Reef, Western Australia

Abstract

Changes in the relative abundance of marine megafauna (whales, dolphins, sharks, turtles, manta rays, dugongs) from aerial survey sightings in the waters adjacent to Ningaloo Reef between June 2000 and April 2002 are described. Generalised linear models were used to explore relationships between different trophic guilds of animals (based on animal sighting biomass estimates) and biophysical features of the oceanscape that were likely to indicate foraging habitats (regions of primary/secondary production) including sea surface temperature (SST), SST gradient, chlorophyll-a (Chl-a), bathymetry (BTH) and bathymetry gradient (BTHg). Relative biomass of krill feeders (i.e. minke whales, whale sharks, manta rays) were related to SST, Chl-a and bathymetry (model [AICc] weight = 0.45) and the model combining these variables explained a relatively large amount (32.3%) of the variation in relative biomass. Relative biomass of fish/cephalopod feeders (dolphins, sharks) were weakly correlated with changes in SST, whereas that of other invertebrate/macroalgal feeders (turtles, dugong) was weakly correlated with changes in steepness of the shelf (bathymetry gradient). Our results indicate that biophysical variables describe only a small proportion of the variance in the relative abundance and biomass of marine megafauna at Ningaloo reef.

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

“…Rohner et al (2013) documented that 40, 30, and 24% of total variance could be explained for sightings of reef manta rays (Manta alfredi), giant manta rays (M. birostris) and whale sharks (Rhincodon typus), respectively, within the same study area and from dive logbook data. Low model deviance (8.4%) of turtles and dugongs has also been noted in similar analyses in which oceanic conditions as predictors of megafauna assemblages were used to model aerial survey data at Ningaloo reef, Western Australia (Sleeman et al, 2007). While they reported a weak correlation between bathymetry and relative abundance, where animals were more abundant when a steep change in depth contour occurred (Sleeman et al, 2007), depth was not a significant predictor in our study.…”

“…Low model deviance (8.4%) of turtles and dugongs has also been noted in similar analyses in which oceanic conditions as predictors of megafauna assemblages were used to model aerial survey data at Ningaloo reef, Western Australia (Sleeman et al, 2007). While they reported a weak correlation between bathymetry and relative abundance, where animals were more abundant when a steep change in depth contour occurred (Sleeman et al, 2007), depth was not a significant predictor in our study. The low total variance explained by our model may have been influenced by the multi-specific nature of our analysis (sightings of two turtle species merged), or a high degree of independent behavior exhibited by turtles, as has been demonstrated from satellite tagged turtles (e.g., Papi et al, 1997;Hatase et al, 2002Hatase et al, , 2006.…”

“…The low total variance explained by our model may have been influenced by the multi-specific nature of our analysis (sightings of two turtle species merged), or a high degree of independent behavior exhibited by turtles, as has been demonstrated from satellite tagged turtles (e.g., Papi et al, 1997;Hatase et al, 2002Hatase et al, , 2006. Using models to predict and explain the distribution of marine megafauna and how it correlates with oceanographic or bathymetric variables is difficult (Polovina et al, 2004;Piatt et al, 2006;Sleeman et al, 2007). Further complications arise as these models struggle to account for the complexity of animal behavior, particularly predators (Sleeman et al, 2007).…”

“…Using models to predict and explain the distribution of marine megafauna and how it correlates with oceanographic or bathymetric variables is difficult (Polovina et al, 2004;Piatt et al, 2006;Sleeman et al, 2007). Further complications arise as these models struggle to account for the complexity of animal behavior, particularly predators (Sleeman et al, 2007). Abundance and distribution of turtles is likely to be influenced by a complex suite of factors, including oceanographic, bathymetric, habitat requirements for food and shelter, deterrents (artificial lighting, heavy anthropogenic use, and natural predators) and behavioral factors (conspecific competition, age class instincts).…”

Particulate nutrient fluxes over a fringing coral reef: relevant scales of phytoplankton production and mechanisms of supply

“…Examination of satellite images shows that surface expression of increased phytoplankton in the autumn occurred over a vast (at least 100s of km 2 ) spatial scale, and corresponded well with increasing LC flow, placing reef-derived nutrients as an unlikely factor in the autumn bloom and firmly implicating the broad-scale influence of the accelerating LC in reeflevel nutrient fluxes as well as, perhaps, the seasonal dynamics of megafauna such as the whale shark. Interestingly, increased whale shark abundance has also been linked to years of stronger LC flow and phytoplankton biomass, not to upwelling (Wilson et al 2001, Sleeman et al 2007.…”