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Marine biota are redistributing at a rapid pace in response to climate change and shifting seascapes. While changes in fish populations and community structure threaten the sustainability of fisheries, our capacity to adapt by tracking and projecting marine species remains a challenge due to data discontinuities in biological observations, lack of data availability, and mismatch between data and real species distributions. To assess the extent of this challenge, we review the global status and accessibility of ongoing scientific bottom trawl surveys. In total, we gathered metadata for 283,925 samples from 95 surveys conducted regularly from 2001 to 2019. We identified that 59% of the metadata collected are not publicly available, highlighting that the availability of data is the most important challenge to assess species redistributions under global climate change. Given that the primary purpose of surveys is to provide independent data to inform stock assessment of commercially important populations, we further highlight that single surveys do not cover the full range of the main commercial demersal fish species. An average of 18 surveys is needed to cover at least 50% of species ranges, demonstrating the importance of combining multiple surveys to evaluate species range shifts. We assess the potential for combining surveys to track transboundary species redistributions and show that differences in sampling schemes and inconsistency in sampling can be overcome with spatio‐temporal modeling to follow species density redistributions. In light of our global assessment, we establish a framework for improving the management and conservation of transboundary and migrating marine demersal species. We provide directions to improve data availability and encourage countries to share survey data, to assess species vulnerabilities, and to support management adaptation in a time of climate‐driven ocean changes.

AimProducing quantitative descriptions of large‐scale biodiversity patterns is challenging, particularly where biological sampling is sparse or inadequate. This issue is particularly problematic in marine environments, where sampling is both difficult and expensive, often resulting in patchy and/or uneven coverage. Here, we evaluate the ability of Gradient Forest (GF) modelling to describe broad‐scale marine biodiversity patterns, using a large dataset that also provided opportunity to investigate the effects of sample size on model stability.LocationNew Zealand's Extended Continental Shelf to depths of 2,000 m.MethodsGF models were used to analyse and predict spatial patterns of demersal fish species turnover (beta diversity) using an extensive demersal fish dataset (>27,000 research trawls) and high‐resolution environmental data layers (1 km2 grid resolution). GF models were fitted using various sized, mutually exclusive subsets of the demersal fish data to explore the effect of variation in numbers of training observations on model performance and stability. A final GF model using 13,917 samples was used to transform the environmental layers, which were then classified to produce 30 spatial groups; the ability of these groups to identify fish samples with similar composition was evaluated using independent sample data.ResultsModel fitting using varying sized subsets of the data indicated only minimal changes in model outcomes when using >7,000 observations. A multiscale spatial classification of marine environments created using results from a final GF model fitted using ~14,000 samples was highly effective at summarizing spatial variation in both fish assemblage composition and species turnover.Main conclusionsThe hierarchical nature of the classification supports its use at varying levels of classification detail, which is advantageous for conservation planning at differing spatial scales. This approach also facilitates the incorporation of information on intergroup similarities into conservation planning, allowing greater protection of distinctive groups likely to support unusual assemblages of species.

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.

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