Better indicators of persistence in gap-analysis frameworks needed

Misleading results from conventional gap analysis – Messages from the warming north

Heini Kujala, Miguel B. Araújo, Wilfried Thuiller and Mar Cabeza

Biological Conservation
Volume 144, Issue 10, October 2011, Pages 2450-2458

Gap analysis is a widely used method for assessing the representation of species in protected area (PA) networks. The process consists of mapping three data layers

1) Land cover

2) Predicted distributions of vertebrate species

3) Stewardship layer. 

This data is then assessed to determine how much of a target species’ habitat is in protected areas.  From this assessment, planning decisions can be made about whether further protection is needed.

There is a rider here. Representation does not imply persistence. In this paper the researchers investigated whether gap analysis may result in misleading conservation guidelines by comparing the representation to two indicators of persistence.

The analysis was done with Finnish breeding birds and identified conservation priorities based on current distribution patterns. The levels of representation identified by gap analysis were very good. The researchers then compared the gap analysis results with recent population trends and projected changes in potential suitable climate under different climate change scenarios for the year 2050. The results show that that although high latitude species are well represented in PAs, they are currently declining and are projected to lose climatic suitability in the near future. Low latitude species with poor representation in PAs have increasing population trends and are generally expected to expand their ranges into protected areas in the near future.

The researchers sign off with the following words “This study demonstrates with empirical data a mismatch between representation in PAs and population trends, resulting in misleading understanding of current PA effectiveness. The mismatch is linked to the latitude of species distributions and corresponds to expected future changes, indicating that the patterns are potentially driven by climate change. We therefore urge practitioners and researchers to include better indicators of persistence in gap-analysis frameworks even for short term assessments.”