Editor’s Choice 54:2 – Optimizing the use of species distribution maps for planning new protected areas

The Editor’s Choice for Issue 54:2 is written by Kulbhushansingh Suryawanshi, who is taking part in our Associate Editor mentoring opportunity. The article chosen by the Editors as this issue’s Editor’s choice article is ‘Limitations and trade-offs in the use of species distribution maps for protected area planning‘ by Moreno Di Marco and colleagues.

Protected Areas have been the ‘big idea’ of biodiversity conservation over the last one hundred years. The total area and the number of protected areas have increased dramatically from a handful in the 1900s to over 160 thousand covering over 28 million sq km today (Watson et al. 2014). However, they still only cover about 5.6% of the earth’s surface which is not sufficient to slow down the extinction crisis.

Di Marco et al. (2016) help conservation planners and practitioners by identifying the trade-off in use of species distribution maps and models for planning future protected areas. Distribution maps or range maps are used to determine species coverage within protected areas and to find where new protected areas need to be placed. However, they have certain limitations. Commission errors in species distribution maps are when species are absent within the marked region but thought to be present. To reduce the impact of commission errors practitioners either perform analysis at coarse grid resolution or use habitat suitability models. Coarse grid analysis is when the entire distribution region is divided into grids and the presence of the species is examined for each grid.

Using a global data-set Di Marco et al. help optimise the use of distribution maps of different resolutions and habitat suitability models. They find that spatial priority analysis studied at finer resolution (say 10 km) was efficient in identifying strategic areas for protected area expansions. But they create a higher perception of coverage than actual levels of coverage. So when using habitat distribution maps with fine scale resolution for protected area planning, we are likely to overestimate the species protection levels. Thus, risking a false sense of security in the minds of conservationists. At a coarser level (say 200 km) of analysis of distribution map, the perceived and actual coverage mapped each other better, but these analyses proposed a need for 4 times higher geographical coverage. Such high coverage will come at a great cost. High geographical coverage will require significantly more resources and commitment from governments to achieve.

Big Bend Wildlife Management Area
The Big Bend Wildlife Management Area (Florida, USA) protects 29,000 ha of marshland, forest and floodplain. This is a critically important habitat for both biodiversity conservation and ecosystem services provision. Photo credit: Dr Gwen Iacona.

Interestingly, when compared with the habitat suitability models, the spatial configuration of the proposed protected areas matched better at coarser resolution than at finer resolution. Habitat suitability models were also more reasonable in the match between perceived and actual levels of protection.

Di Marco et al. analysed these trade-offs using data on 1115 species of threatened mammals (99.5% of all threatened mammals). They propose using high analytical resolution and refined distribution models when planning protected areas for well-studied groups like mammals and birds at regional and global scales.

Setting up new protected areas is a challenging task. One of the important aspects of the process is to minimize costs of effectively protecting an area with choosing an area to maximise the biodiversity inside. Di Marco et al. have made a valuable contribution to this optimisation problem. This paper is a fine example where a global-scale analysis has very practical value for site-specific conservation challenges such as protected area planning. Di Marco et al. also pave the way for future work to analyse the trade-offs for less-studied groups of species such as amphibians, reptiles and insects.

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