Species composition and turnover models provide robust approximations of biodiversity in marine conservation planning

Fabrice Stephenson,John R Leathwick,Shane Geange,Atte Moilanen,C Roland Pitcher,Carolyn J Lundquist

doi:10.1016/j.ocecoaman.2021.105855

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.

Highlights

Robust identification of priority areas for conservation is often hampered by a lack of comprehensive knowledge of biodiversity pat terns (Arponen et al, 2008; Ferrier et al, 2007; Hortal et al, 2015)
The full complement of biodiversity is typically not represented in marine conservation planning, despite the important roles that less common species can play in the stability and functioning of marine ecosystems (Ellingsen et al, 2007)
Protection of the top-ranked 20% of cells from this ranking would provide 20% or greater representation for 179 of the 217 demersal fish species (82%) (Fig. 3)

Summary

Introduction

Robust identification of priority areas for conservation is often hampered by a lack of comprehensive knowledge of biodiversity pat terns (Arponen et al, 2008; Ferrier et al, 2007; Hortal et al, 2015). More recent environ mental classification models use species observations to weight and/or transform environmental variables to maximise the correspondence between classification groups and spatial variation in species composi tion (e.g., Ferrier et al, 2007; Leathwick et al, 2011) These new ap proaches provide significant improvements over environment-alone approaches (Pitcher et al, 2012) and are more likely to capture infor mation across a full range of species, allowing for representation of both common and rare species when describing spatial variation in species composition and turnover (Stephenson et al, 2018; Sutcliffe et al, 2015)

Methods

Results

Conclusion