Open AccessSurveillance for threatened and invasive species when uncertainty is severe
Author(s) -
Thompson Colin J.,
Can Rob M.,
Burgman Mark A.
Publication year - 2012
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/j.1472-4642.2012.00888.x
Subject(s) - threatened species , biosecurity , satisficing , robustness (evolution) , computer science , function (biology) , operations research , environmental resource management , risk analysis (engineering) , ecology , business , economics , engineering , biology , evolutionary biology , habitat , gene , biochemistry , artificial intelligence
Aim This study develops methods for efficient surveillance and monitoring systems to address a wide range of problems in biosecurity, ecology and conservation biology. It focuses especially on surveillance systems relevant for management that aims to reduce trade in threatened species and curb the spread of potential pests and diseases. Location Melbourne, Australia. Methods This paper develops different approaches to make decisions about the allocation of resources that aim to avoid unacceptable outcomes. The analysis solves for the optimal allocation of surveillance effort in each of two facilities as a function of the arrival rates of invasive species in two facilities (that is, when the arrival rates are known). However, when arrival rates are unknown, it is not possible to solve for this optimum. The analysis also provides a satisficing approach for situations in which arrival rates are unknown, in which a degree of tolerance for deviating from the optimal solution is specified. Results The study provides simple analytical solutions to these two problems, analogous to results developed earlier in operations research. The analysis is illustrated with an example of the inspection of quarantine facilities for pests and diseases associated with trade. Main conclusions The best surveillance strategy depends on the choice of an objective function and the attitude of the decision‐maker to the robustness of the decision. The example application could be adapted to many other environmental surveillance scenarios.