Species composition and turnover models provide robust approximations of biodiversity in marine conservation planning. (15th October 2021)

• Record Type: Journal Article
• Authors: Stephenson, Fabrice; Leathwick, John R.; Geange, Shane; Moilanen, Atte; Pitcher, C. Roland; Lundquist, Carolyn J.
• Is Part Of: Ocean & coastal management. Volume 212(2021)
• Publication Date: 2021-10-15
• Subjects: Community classification -- Demersal fish -- Gradient forest model -- Marine spatial planning -- New Zealand -- Species distributions -- Zonation; Marine resources -- Management -- Periodicals; Coastal zone management -- Periodicals; Coastal ecology -- Periodicals; Ressources marines -- Périodiques; Littoral -- Aménagement -- Périodiques; Écologie littorale -- Périodiques; Coastal ecology; Coastal zone management; Marine resources -- Management; Periodicals; Electronic journals; 551.46
• DOI: 10.1016/j.ocecoaman.2021.105855
• Languages: English
• ISSNs: 0964-5691

Abstract

Abstract: Robust design of Marine Protected Areas in ocean environments is often challenging due to inadequate knowledge of biodiversity patterns, reflecting difficulties in the prediction of species distributions from sampling data that are often sparse or inadequate. Models that combine species and environmental data, such as Gradient Forests (GF), provide one analytical approach to this problem, efficiently combining available information to produce spatial models of species turnover throughout an area of interest. Spatial estimates of species turnover can then be classified to estimate spatial patterns in species composition; however, the performance of GF-based classifications within a conservation planning context has not previously been evaluated. Here we assess the utility for conservation planning (using the software Zonation ) of a GF-based hierarchical classification that summarises spatial patterns in demersal fish composition in the oceans around New Zealand. Progressively more complex Zonation analyses assessed the effects of (i) varying the number of classification groups, (ii) adding information describing species turnover, and (iii) adding information describing spatial variation in demersal fish species richness. The best-performing GF-based conservation ranking used layers describing the distributions of 30 classification groups, demersal fish species turnover between these groups, and species richness. Conservation outcomes from this ranking were only marginally less efficient than those from a more conventional ranking that used 217 individual species distribution layers (7% less efficient). This indicates that GF-based classifications may provide a practical alternative for marine conservation planning. Additional advantages arise from the greater ease with which a single classification layer summarising complex biodiversity patterns can facilitate decision-making in participatory stakeholder processes. Graphical abstract: Image 1 Highlights: Gradient Forest-based environmental classifications described spatial patterns of demersal fish composition, turnover and richness. The efficacy of Gradient Forest-based classifications for conservation planning were compared to 'traditional' estimates of individual species' distributions. A single Gradient Forest-based classification was only marginally less efficient in a conservation planning analysis than a more conventional approach (7% less efficient). Gradient Forest-based classifications summarise complex biodiversity patterns and provide a practical alternative for marine conservation planning.