Drivers of Spatial Distributions of Basking Shark (Cetorhinus maximus) in the Southwest Pacific

Abstract

Basking sharks (Cetorhinus maximus) were widely reported throughout New Zealand waters. Once commonly observed, and sometimes in large numbers, basking sharks are now infrequently reported. Basking shark observations are known to be highly variable across years, and their distribution and occurrence have been shown to be influenced by environmental predictors such as thermal fronts, chl-a concentration, and the abundance of prey (zooplankton). Little is known of basking sharks in the South Pacific and more information on distribution, habitat use, and migratory patterns is required to better understand the species’ regional ecology. Here, we used bootstrapped Habitat Suitability Models [HSM, ensembled from Boosted Regression Tree (BRT) and Random Forest (RF) models] to determine the drivers of basking shark distribution, predict habitat suitability and estimated uncertainty in the South Pacific for the first time. High−resolution environmental (1 km2 grid resolution) and biotic data, including inferred prey species, and all available basking shark records across New Zealand’s Exclusive Economic Zone (EEZ) were included in the ensemble HSMs. The most influential driver of modeled basking shark distribution was vertical flux of particulate organic matter at the seabed, which may indicate higher levels of primary production in the surface ocean and higher prey density in the mesopelagic zone and at the seafloor. The BRT and RF models had good predictive power (AUC and TSS > 0.7) and both models performed similarly with low variability in the model fit metrics. Areas of high basking shark habitat suitability included the east and west coasts of the South Island, Puysegur Ridge, and Auckland Island slope. The outputs produced here could be incorporated into future management framework for assessing threat and conservation needs (e.g., spatially explicit risk assessment) for this regionally protected species, as well as providing guidance for future research efforts (e.g., areas of interest for sampling).

Spatial Distribution, Temporal Changes, and Knowledge Gaps in Basking Shark (Cetorhinus maximus) Sightings in the California Current Ecosystem

“…Furthermore, as mentioned above, it is likely that basking sharks in the CCE also feed at depth, limiting the efficacy of surface chl-a as an indicator of shark presence and prey abundance. More dynamic variables that predict convergence zones would likely also better reflect the spatial distribution of basking shark prey (see Finucci et al, 2021). Similarly, our model did not account for offshore or non-environmental cues that may drive basking sharks to coastal waters.…”

“…), along visible tidal lines that mark the front boundary (Baduini, 1995;Sims and Quayle, 1998;Miller et al, 2015). Basking shark coastal hotspots have been recorded primarily in the northern hemisphere and in New Zealand (Francis and Duffy, 2002;Finucci et al, 2021), though their seasonality varies by location. In regions in the Northern Atlantic, the Eastern Coast of North America, and in New Zealand, hotspots consistently form in late April through early September (i.e., Sims et al, 2003;Speedie et al, 2009;Lieber et al, 2013Lieber et al, , 2020Siders et al, 2013;Hoogenboom et al, 2015;Gore et al, 2018;Finucci et al, 2021).…”

“…Basking shark coastal hotspots have been recorded primarily in the northern hemisphere and in New Zealand (Francis and Duffy, 2002;Finucci et al, 2021), though their seasonality varies by location. In regions in the Northern Atlantic, the Eastern Coast of North America, and in New Zealand, hotspots consistently form in late April through early September (i.e., Sims et al, 2003;Speedie et al, 2009;Lieber et al, 2013Lieber et al, , 2020Siders et al, 2013;Hoogenboom et al, 2015;Gore et al, 2018;Finucci et al, 2021). Conversely, in the Eastern North Pacific (ENP), hotspots were observed in the mid-20th century during summer months along the southern coast of British Columbia in Canada, but during winter months in California's Morro and Monterey Bays (Squire, 1967(Squire, , 1990McFarlane et al, 2009).…”

Fine‐scale spatial and temporal distribution patterns of large marine predators in a biodiversity hotspot

“…SDM can identify relationships between species occurrences and environmental covariates to describe habitat preferences, then translate the output into geographical space to predict distribution within and beyond the study area (Elith & Leathwick, 2009). Some predator distribution studies include prey (biotic) data as covariates in SDMs (Finucci et al, 2021; Torres et al, 2008). However, as these data are often challenging to collect at the same temporal and spatial scale as the study species, most SDM studies include environmental covariates only (e.g.…”

“…However, as these data are often challenging to collect at the same temporal and spatial scale as the study species, most SDM studies include environmental covariates only (e.g. depth, salinity), with the assumption that environmental conditions drive resource distribution (Cañadas et al, 2002; Finucci et al, 2021; Skov et al, 2008; Virgili et al, 2021). Understanding fine‐scale details about the habitat in which the study species live and the drivers that may influence their distribution is near impossible for most ocean environments, but broad‐scale environmental variables can inform models, as seen in our study site.…”