Modelling the spatial distribution of cetaceans in New Zealand waters

Abstract

Aim Cetaceans are inherently difficult to study due to their elusive, pelagic and often highly migratory nature. New Zealand waters are home to 50% of the world's cetacean species, but their spatial distributions are poorly known. Here, we model distributions of 30 cetacean taxa using an extensive at‐sea sightings dataset (n > 14,000) and high‐resolution (1 km2) environmental data layers. Location New Zealand's Exclusive Economic Zone (EEZ). Methods Two models were used to predict probability of species occurrence based on available sightings records. For taxa with <50 sightings (n = 15), Relative Environmental Suitability (RES), and for taxa with ≥50 sightings (n = 15), Boosted Regression Tree (BRT) models were used. Independently collected presence/absence data were used for further model evaluation for a subset of taxa. Results RES models for rarely sighted species showed reasonable fits to available sightings and stranding data based on literature and expert knowledge on the species' autecology. BRT models showed high predictive power for commonly sighted species (AUC: 0.79–0.99). Important variables for predicting the occurrence of cetacean taxa were temperature residuals, bathymetry, distance to the 500 m isobath, mixed layer depth and water turbidity. Cetacean distribution patterns varied from highly localised, nearshore (e.g., Hector's dolphin), to more ubiquitous (e.g., common dolphin) to primarily offshore species (e.g., blue whale). Cetacean richness based on stacked species occurrence layers illustrated patterns of fewer inshore taxa with localised richness hotspots, and higher offshore richness especially in locales of the Macquarie Ridge, Bounty Trough and Chatham Rise. Main conclusions Predicted spatial distributions fill a major knowledge gap towards informing future assessments and conservation planning for cetaceans in New Zealand's extensive EEZ. While sightings datasets were not spatially comprehensive for any taxa, these two best available approaches allow for predictive modelling of both more common, and of rarely sighted, cetacean species with limited available information.

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

“…Because of difficulties in correcting for differences in sampling methods, all catch records were converted into presence records (Elith et al, 2011;Stephenson et al, 2018). To minimize the effect of spatial bias in the occurrence data, species records were aggregated spatially to a 1 km 2 grid resolution (Aiello-Lammens et al, 2015;Stephenson et al, 2020b). Strandings and records without an approximate date reference (month) were removed.…”

“…BRT models were fitted with a Bernoulli error distribution, a tree complexity of 2, a learning rate of 0.01 (with parameters selected so as to fit trees for each bootstrapped model), a bag fraction of 0.7 and random 10-fold cross evaluation following recommendations from Leathwick et al (2006) and Elith et al (2008). The BRT method has been widely used in ecological applications and has performed well in previous studies of fish, zooplankton, and cetacean distributions in New Zealand (Leathwick et al, 2006;Compton et al, 2013;Pinkerton et al, 2020;Stephenson et al, 2020b).…”

“…By relating species' sightings to environmental predictor variables, the abundance or probability of taxa presence at a given location can be estimated along with a characterization of the environmental drivers of species distributions. These models are becoming increasingly popular for use on marine species spanning large geographic and bathymetric ranges and have been employed for a range of cetaceans (Stephenson et al, 2020b), seabirds (Cleasby et al, 2020), and cartilaginous fishes, including basking sharks in the Northeast Atlantic (Austin et al, 2019).…”

Passive Acoustic Monitoring Reveals Spatio-Temporal Distributions of Antarctic and Pygmy Blue Whales Around Central New Zealand

“…Anatomical differences exist between the two sub-species (Ichihara, 1966;Olson et al, 2015), but can be difficult to recognise in the field. As such, information about blue whales in New Zealand waters has often been conflated by the inclusion of both sub-species (see Stephenson et al, 2020). Identification challenges, in addition to access constraints imposed by their offshore location, have led to both sub-species being classified as "data deficient" at a national level, although PBWs are also listed as "resident native" (Baker et al, 2019) due to focused studies on this sub-species (Torres, 2013;Olson et al, 2015).…”

“…In this study, PAM was used to investigate the spatial and temporal distributions of PBWs and critically endangered ABWs around central New Zealand. The South Taranaki Bight (STB) in central New Zealand has been a focus for blue whale research (Barlow et al, 2018), and is predicted to be a region with high probability of occurrence of blue whales (see Stephenson et al, 2020). As acoustic recorders were deployed in a range of physical marine environments, propagation modelling was conducted to consider detections in range context and to enable comparisons among recording locations.…”