Combined application of biophysical habitat mapping and systematic conservation planning to assess efficiency and representativeness of the existing High Seas MPA network in the Northeast Atlantic

ICES Journal of Marine Science ICES Journal of Marine Science (2015), 72(5), 1483– 1497. doi:10.1093/icesjms/fsv012 Original Article Jon L. Evans, Frances Peckett, and Kerry L. Howell* School of Marine Science and Engineering, Marine Institute, Plymouth University, Drake Circus, Plymouth PL4 8AA, UK *Corresponding author: tel: +44 1752 584544; fax: +44 1752 586101; e-mail: Evans, J. L., Peckett, F., and Howell, K. L. Combined application of biophysical habitat mapping and systematic conservation planning to assess efficiency and representativeness of the existing High Seas MPA network in the Northeast Atlantic. – ICES Journal of Marine Science, 72: 1483 – 1497. Received 8 January 2014; revised 8 January 2015; accepted 10 January 2015; advance access publication 23 February 2015. The High Seas are increasingly the subject of exploitation. Although Marine Protected Areas (MPAs) are seen as a useful tool in the sustainable management of the oceans, progress in the implementation of MPA networks in areas beyond national jurisdiction has been limited. Specifically, the criteria of “representativeness” has received little consideration. This study uses the systematic conservation planning software Marxan coupled with a biologically meaningful biophysical habitat map to investigate representative MPA network scenarios and to assess the efficiency and representativeness of the existing High Seas MPA network in the Northeast Atlantic. Habitat maps were created based on the layers of water mass structure and seabed topography resulting in 30 different habitats, in six distinct regions. Conservation targets were set at 10 and 30% representation of each habitat within the final network. Two portfolios were created. The first portfolio (P1) ignored the presence of the existing MPA network within the study area allowing a non-biased selection of planning units (PUs) or sites to be chosen. The second (P2) enforced the selection of areas within the existing MPA network. Efficiency was measured as the difference in the percentage area contained within the “best scenario” MPAs from the un-bias run (P1) compared with (P2). Representativety of the existing network was assessed through the investigation of the properties of PUs included within MPAs in the “best scenario” Marxan output of P2. The results suggest that the current MPA network is neither efficient nor representative. There were clear differences in the spatial distribution of PUs selected in P1 compared with P2. The area required to be protected to achieve that the representation of 10 and 30% of each habitat was 8 – 10 and 1– 4% higher, respectively, in P2 compared with P1. Abyssal areas in all regions are underrepresented within the current MPA network. Keywords: biophysical, deep-sea, habitat classification, Marine Protected Areas, Marxan. Introduction Background The deep-sea is vast, and although it represents 63% of the Earth’s surface (Smith et al., 2008) a mere 0.0001% of it has been the focus of biological, scientific investigation (Benn et al., 2010). In the past, the remoteness and inaccessibility of the deep seabed has protected it from exploitation but has also severely limited research (Benn et al., 2010). However, there is increasing evidence that human activities are causing serious damage in the High Seas [or areas beyond national jurisdiction (ABNJ); McClain and Hardy, 2010; Ramirez-Llodra et al., 2011; Pham et al., 2014]. ABNJ include all areas outside of the 200 nautical mile economically exclusive zones (EEZs), set in place in 1994 by the 1982 United Nations Convention on the Law of the Sea (UNCLOS) which is the main comprehensive, legal framework relating to the High Seas (O’Leary et al., 2012). As technology improves, previously inaccessible areas containing economically viable resources are being exploited (McClain # 2015 International Council for the Exploration of the Sea. Published by Oxford University Press. All rights reserved. For Permissions, please email: Combined application of biophysical habitat mapping and systematic conservation planning to assess efficiency and representativeness of the existing High Seas MPA network in the Northeast Atlantic 1484 and Hardy, 2010; Ramirez-Llodra et al., 2011). Knowledge of slow growth rates and extreme longevity of many deep-sea species and awareness of the damage fishing and mining practices can inflict (Laffoley, 2005) suggests that any marine habitats at risk from anthropogenic activities require protection. Conservation tools Marine Protected Areas Mapping biodiversity using surrogates To design a representative system of MPAs that will facilitate conservation of deep-sea biodiversity, a number of issues need to be resolved. Gaining support for MPAs in data poor areas, such as ABNJ, is necessary. The paucity of data in the deep-sea will not be solved soon and will always lag behind exploration (O’Leary et al., 2012). This is fuelling a growing body of research that uses various surrogates to represent biological diversity, and more recently the use of predictive modelling to map the distributions of habitats (Howell et al., 2011; Ross and Howell, 2012). The use of biophysical surrogates as indicators of benthic habitats has become an established method of habitat mapping (Roff and Taylor, 2000; Harris and Whiteway, 2009; Howell, 2010). These surrogates are often organized into a hierarchical classification system, the examples of which include the European Nature Information System (EUNIS; Davies et al., 2004) and the Global Open Oceans and Deep-sea Seabed (GOODS UNESCO, 2009); for a detailed review, see Howell (2010). These habitat classification systems can then be used to assist the creation of habitat maps to convey spatial information that is relevant to the distribution of biodiversity (Metcalfe et al., 2012). A study by Roff et al. (2003) was one of the first to establish that by using simple mapping and Geographical Information System (GIS) techniques, a map of representative habitats can be created that follows a classification system. A GIS overlay approach, based on Boolean logic (Harris et al., 2008), using a geophysical (i.e. physiographic and oceanographic) framework identified different broad scale, natural regions termed “seascapes” for the entire Canadian coastline and Scotian Shelf. Following this initial example, a number of studies have emulated Roff et al. (2003), using a variety of spatially different surrogates to systematically map different regions around the world; the Australian continental shelf (Harris et al., 2008), Scottish continental shelf (Howell, 2010), and the global seabed (Greene et al., 1999; Agnostini et al., 2008; Harris and Whiteway, 2009). To be relevant and of use understanding the distribution of biodiversity and hence the design of MPAs, any surrogates must exert control on (i.e. be a surrogate for) the occurrence of species (Roff et al., 2003; Harris et al., 2008; Harris and Whiteway, 2009; Howell (...truncated)