The influence of planning unit characteristics on the efficiency and spatial pattern of systematic conservation planning assessments

Abstract

Systematic conservation planning is a widely used approach for designing protected area systems and ecological networks. This generally involves dividing the planning region into a series of planning units and using computer software to select portfolios of these units that meet specified conservation targets whilst minimising conservation costs. Previous research has shown that changing the size and shape of these planning units can alter the apparent spatial characteristics of the underlying data and thus influence conservation assessment results. However, this may be less problematic when using newer software that can account for additional constraints based on portfolio costs and fragmentation levels. Here we investigate these issues using a dataset from southern Africa and measure the extent to which changing planning unit shape, size and baseline affects the results of conservation planning assessments. We show that using hexagonal planning units instead of squares produces more efficient and less fragmented portfolios and that using larger planning units produces portfolios that are less efficient but more likely to identify the same priority areas. We also show that using real-world constraints in the analysis, based on reducing socio-economic costs and minimising fragmentation levels, reduces the influence of planning unit characteristics on the results and so argue that future studies should adopt a similar approach when investigating factors that influence conservation assessments.

Clear consideration of costs, condition and conservation benefits yields better planning outcomes

“…Where there is a high correlation between the selection frequencies of solutions, this indicates that the spatial priorities identified by the two approaches considered are broadly similar. We do not consider significance values, and report only on the correlation coefficients, as these are unaffected by spatial autocorrelation (Nhancale and Smith, 2011). The level of spatial agreement between the best solutions found using each approach was determined by calculating the Cohen's kappa coefficient of agreement (κ) between pairs of scenarios, using the 'psych' package (R Development Core Team, 2014).…”

Determining conservation priority areas for Palearctic passerine migrant birds in sub-Saharan Africa

“…the selected grid cells (Smith et al 2010, Nhancale andSmith 2011). To counteract the possible effects of selecting grid cells without consideration of the resulting fragmentation, MARXAN allows the calculation of the total cost for solutions that meet all the targets as the combined grid cell costs plus the boundary length cost.…”

“…We added the following four criteria not considered in Walther et al (2010): (1) inclusion of a boundary length modifier (BLM), which increases the clumping of selected sites by penalizing solutions with high fragmentation (Nhancale and Smith 2011); (2) exclusion of all sites with a high human footprint, i.e., cells that are affected greatly by humanity through a combination of high human population density, land transformation, human access such as roads and large rivers, and power infrastructure (Sanderson et al 2002); (3) use of different weights (called species penalty factors) to adjust for the threat status of each bird species (Loos 2011);and (4) inclusion of all sites that include protected areas (IUCN-UNEP 2009).…”

“…Therefore, highly fragmented solutions are often selected, with selected grid cells separated by large distances. To counteract such fragmentation, MARXAN implemented the BLM, which penalizes solutions with high fragmentation and encourages solutions with several grid cells in direct contact with each other, thus creating solutions with larger, connected areas containing several grid cells (Smith et al 2010, Nhancale andSmith 2011). Another possible critique of previously published solutions is that they may contain grid cells with high human impact, e.g., urban centers and other highly modified landscapes.…”

Identifying priority areas for national-level conservation to achieve Aichi Target 11: A case study of using terrestrial birds breeding in Japan

“…First, regionspecific social factors, such as local land use, strongly influence the outcomes of conservation planning (Klein et al 2008;Wilson et al 2010;Nhancale & Smith 2011). Second, more accurate and higher resolution data are often available at the national and regional scales, which could lead to more efficient conservation planning (Hamel et al 2013).…”