Software for prioritizing conservation actions based on probabilistic information,Conservation Biology

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Software for prioritizing conservation actions based on probabilistic information
Conservation Biology ( IF 5.2 ) Pub Date : 2020-12-11 , DOI: 10.1111/cobi.13681
Matthew Watts _{1,

2,

3} , Carissa J Klein _{2,

3,

4} , Vivitskaia J D Tulloch _{2,

4,

5} , Silvia B Carvalho ₆ , Hugh P Possingham _{2,

3}

Affiliation

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species’ populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.

更新日期：2020-12-11

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