Incorporating climate change into conservation decision-making at site and population scales is challenging due to uncertainties associated with localized climate change impacts and population responses to multiple interacting impacts and adaptation strategies. We explore the use of spatially explicit population models to facilitate scenario analysis, a conservation planning approach for situations of high uncertainty. We developed dynamic, linked habitat suitability and metapopulation models using RAMAS GIS to consider management and monitoring options for a grassland reserve in Minnesota (USA) in order to support a hydrologically sensitive rare orchid (Cypripedium candidum). We evaluated 54 future scenarios combining changes in drought frequency, increased depth to water table, and multiple configurations of increased invasive species cover and management. Simulation results allowed us to prioritize adaptation strategies and monitoring guidelines to inform adaptive management for our model system. For example, preventing further spread of invasive species into the current C. candidum population is an important low-risk resilience strategy for this site. However, under more serious climate change scenarios, higher-risk strategies, such as protecting critical recharge areas, become essential. Additionally, allocating limited monitoring resources toward detecting changes in depth to water table and assessing C. candidum population responses to severe drought will more efficiently inform decisions about when to shift from low-risk resilience approaches to higher-risk resistance and facilitation strategies. Applying this scenario-based modeling approach to other high-priority populations will enable conservation decision-makers to develop sound, cost-effective, site-specific management and monitoring protocols despite the uncertainties of climate change.
Bibliographical noteFunding Information:
Funding for this project was generously provided by the US Forest Service , Northern Research Station. We are grateful to Dr. H. Reșit Ackçakaya for guidance in using RAMAS GIS, and to Dr. Aaron Rendahl for valuable feedback on statistical analyses. We also thank Drs. Laura Van Riper, Lynne Westphall, and Anthony Starfield for their thoughtful comments on an earlier draft of this article. C.candidum occurrence data used in the models were provided by the Division of Ecological and Water Resources, Minnesota Department of Natural Resources (DNR), and were current as of October 2012. These data are not based on an exhaustive inventory of the state, and the lack of data for any geographic area shall not be construed to mean that no C. candidum are present.
Copyright 2016 Elsevier B.V., All rights reserved.
- Cypripedium candidum
- Habitat suitability
- Metapopulation model
- Rare plants