摘要
The stochLAB package is an adaptation of the R code developed by Masden (2015) to incorporate variability and uncertainty in the avian collision risk model originally developed by Band (2012). The package is for use by individuals modelling collision risk of seabirds at offshore wind farms. The primary functions take input information on the morphology, behaviour and densities of seabirds as well data pertaining to the proposed wind farm (i.e., turbine dimensions, speed and number).
These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, the package itself relies on generic biological and windfarm data and can be applied anywhere (i.e., in any marine environment) as long as the parameters are appropriate for the species and windfarms of interest.
Code developed under stochLAB substantially re-factored and re-structured Masden’s (heavily script-based) implementation into a user-friendly, streamlined, well documented and easily distributed tool. Furthermore, the package lays down the code infrastructure for easier incorporation of new functionality, e.g. extra parameter sampling features, model expansions, etc.
These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, the package itself relies on generic biological and windfarm data and can be applied anywhere (i.e., in any marine environment) as long as the parameters are appropriate for the species and windfarms of interest.
Code developed under stochLAB substantially re-factored and re-structured Masden’s (heavily script-based) implementation into a user-friendly, streamlined, well documented and easily distributed tool. Furthermore, the package lays down the code infrastructure for easier incorporation of new functionality, e.g. extra parameter sampling features, model expansions, etc.
| 源语言 | English |
|---|---|
| 输出媒体 | App development |
| 出版状态 | Published - 2022 |
联合国可持续发展目标
此成果有助于实现下列可持续发展目标:
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Affordable and clean energy
指纹
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