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Prioritising weed management activities in a data deficient environment: the Pilbara islands, Western Australia
Along the Pilbara coast of Western Australia (WA) there are approximately 598 islands with a total area of around 500 km(2). Budget limitations and logistical complexities mean the management of these islands tends to be opportunistic. Until now there has been no review of the establishment and impa...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945735/ https://www.ncbi.nlm.nih.gov/pubmed/27441230 http://dx.doi.org/10.1016/j.heliyon.2015.e00044 |
Sumario: | Along the Pilbara coast of Western Australia (WA) there are approximately 598 islands with a total area of around 500 km(2). Budget limitations and logistical complexities mean the management of these islands tends to be opportunistic. Until now there has been no review of the establishment and impacts of weeds on Pilbara islands or any attempt to prioritise island weed management. In many instances only weed occurrence has been documented, creating a data deficient environment for management decision making. The purpose of this research was to develop a database of weed occurrences on WA islands and to create a prioritisation process that will generate a ranked list of island-weed combinations using currently available data. Here, we describe a model using the pairwise comparison formulae in the Analytical Hierarchy Process (AHP), four metrics describing the logistical difficulty of working on each island (island size, ruggedness, travel time, and tenure), and two well established measures of conservation value of an island (maximum representation and effective maximum rarity of eight features). We present the sensitivity of the island-weed rankings to changes in weights applied to each decision criteria using Kendall's tau statistics. We also present the top 20 ranked island-weed combinations for four modelling scenarios. Many conservation prioritisation tools exist. However, many of these tools require extrapolation to fill data gaps and require specific management objectives and dedicated budgets. To our knowledge, this study is one of a few attempts to prioritise conservation actions using data that are currently available in an environment where management may be opportunistic and spasmodic due to budgetary restrictions. |
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