3D spatial conservation prioritisation: Accounting for depth in marine environments

Abstract

While marine environments are three‐dimensional (3D) in nature, current approaches and tools for planning and prioritising actions in the ocean are predominantly two dimensional. Here, we develop a novel 3D marine spatial conservation prioritisation approach, which explicitly accounts for the inherent vertical heterogeneity of the ocean. This enables both vertical and horizontal spatial prioritisation to be performed simultaneously. To our knowledge, this is the first endeavour to develop prioritisation of conservation actions in 3D. We applied the 3D spatial conservation prioritisation approach to the Mediterranean Sea as a case study. We first subdivided the Mediterranean Sea into 3D planning units by assigning them a z coordinate (representing depth). We further partitioned these 3D planning units vertically into three depth layers; this allowed us to quantify biodiversity (1,011 species and 19 geomorphic features) and the cost of conservation actions at different depths. We adapted the prioritisation software Marxan to identify 3D networks of sites where biodiversity conservation targets are achieved for the minimum cost. Using the 3D approach presented here, we identified networks of sites where conservation targets for all biodiversity features were achieved. Importantly, these networks included areas of the ocean where only particular depth layers along the water column were identified as priorities for conservation. The 3D approach also proved to be more cost‐efficient than the traditional 2D approach. Spatial priorities within the networks of sites selected were considerably different when comparing the 2D and 3D approaches. Prioritising in 3D allows conservation and marine spatial planners to target specific threats to specific conservation features, at specific depths in the ocean. This provides a platform to further integrate systematic conservation planning into the wider ongoing and future marine spatial planning and ocean zoning processes.

Bio‐physical models of marine environments reveal biases in the representation of protected areas

“…Future models could therefore be utilized in conjunction with a three‐dimensional modelling approach (i.e. Venegas‐Li, Levin, Possingham, & Kark, ) to better capture specific biodiversity features at depth for more efficient conservation prioritization. To enable a more accurate assessment of the NRSMPA, comprehensive, benthic habitat mapping, should be conducted to remove the need for environmental surrogates.…”

Incorporating climate velocity into the design of climate-smart networks of protected areas

“…Climate-velocity trajectories could be used to create a 42 climate-connectivity matrix and prioritize climate connections following a similar approach to that developed to 43 account for larval dispersal (Beger et al, 2010). Also, because we know that climate velocity magnitudes and 44 directions differ through the water column (Brito-Morales et al, 2020) this could be captured if protected-area 45 policy allowed for three-dimensional zoning in the ocean (Brito-Morales et al, 2018;Venegas-Li et al, 2018). 46…”

Costs are not necessarily correlated with threats in conservation landscapes

“…; Murdoch, Ranganathan, Polasky, & Regetz, ; Sala et al. ; Venegas‐Li, Levin, Possingham, & Kark, ). The assumption that costs are positively correlated with threats also influences important debates in conservation theory.…”

“…This assumption is often explicitly stated (e.g., Boyd, Epanchin-Niell, & Siikamäki, 2015;Butsic, Lewis, & Radeloff, 2013;Costello & Polasky, 2004;Devillers et al 2015;Merenlender, Newburn, Reed, & Rissman, 2009;Moore, Balmford, Allnutt, & Burgess, 2004;Newburn, Reed, Berck, & Merenlender, 2005;Visconti, Pressey, Segan, & Wintle, 2010) based on the argument that anthropogenic habitat transformation is most rapid and intense in economically profitable areas, such as those containing valuable natural resources (Costello & Polasky, 2004;Newburn et al, 2005;Visconti et al, 2010). Based on this assumption, many conservation planning exercises use metrics of threat as surrogates for conservation costs, thereby assuming that costs and threats have the same spatial distribution (Klein et al 2008;Murdoch, Ranganathan, Polasky, & Regetz, 2010;Sala et al 2002;Venegas-Li, Levin, Possingham, & Kark, 2018). The assumption that costs are positively correlated with threats also influences important debates in conservation theory.…”