Planning iterative investment for landscape restoration: Choice of biodiversity indicator makes a difference

Natural regrowth vegetation offers a cost-effective means of restoring some degraded landscapes. Worldwide, policy responses to climate change are increasing the attractiveness of investment in regrowth protection or facilitation which with strategic planning could also deliver substantial dividends for biodiversity conservation. This study compares the performance of two commonly used indicators of biodiversity conservation priority, irreplaceability and complementarity, as tools to support planning for iterative investment to protect natural regrowth of Brigalow, an endangered ecological community in subtropical eastern Australia. Brigalow covered more than seven million hectares prior to clearing, it now persists ‘intact’ on less than a tenth of that area but there are significant areas of regrowth. Data on Brigalow regrowth derived from mapping and remote sensing identify 10,555 patches covering 280,000 hectares in total. Two different classifications are used to represent Brigalow biodiversity: a land-type classification of 16 ‘regional ecosystems’ mapped at 1:100,000 scale, and a landscape-scale classification of 40 biogeographic subregions that discriminate relatively uniform landscapes at about 1:500,000 scale. Conservation targets are expressed as the extent of regrowth needed to increase the extent of intact or ‘remnant’ areas of each biodiversity feature to either 5% or 10% of its former extent. In each case, irreplaceability and complementarity are positively correlated, and either metric type could be used to identify relatively large sets of high-priority patches. However, regional-scale restoration is likely to involve iterative investment and therefore to require discrimination of relatively small sets of patches of the highest priority for biodiversity conservation. Irreplaceability is not an ideal measure of biodiversity value when planning such iterative processes, simply because irreplaceability is uninformative for ranking ‘high-value’ patches; they all have the highest possible score. This study demonstrates the importance of considering quite fundamental points when choosing metrics for conservation planning, such as the frequency distribution of values they produce. Where planning aims to identify quite small sets of very high value features metrics that are most variable among the highest value patches, like the one used for complementarity in this study, will be more useful than metrics that are strongly bounded at higher values.