This is a model, written in R, to simulate the effects of offshore wind farms upon seabird foraging behaviour, and the ultimate consequences of this for adult and chick survival.
\r\n\r\nIt is called the \u201csimplified\u201d model to distinguish it from the displacement model that has already been developed by CEH which is more biologically realistic in a number of ways, but substantially more computationally-intensive to run.
\r\n\r\nThe model has been designed for use with four specific proposed offshore wind farms in the Forth-Tay region, and for use with five specific species (guillemot, kittiwake, puffin, gannet and razorbill). Much of the code can be readily-applied to different species, wind farms and SPAs, but some cannot \u2013 we have attempted to make the code as generic as possible, but it does not, and was not designed to, provide general code for modelling the effects of any wind farm in any geographical area. The model can be downloaded from the table below.
\r\n","format":"filtered_html","safe_value":"This is a model, written in R, to simulate the effects of offshore wind farms upon seabird foraging behaviour, and the ultimate consequences of this for adult and chick survival.
\nIt is called the \u201csimplified\u201d model to distinguish it from the displacement model that has already been developed by CEH which is more biologically realistic in a number of ways, but substantially more computationally-intensive to run.
\nThe model has been designed for use with four specific proposed offshore wind farms in the Forth-Tay region, and for use with five specific species (guillemot, kittiwake, puffin, gannet and razorbill). Much of the code can be readily-applied to different species, wind farms and SPAs, but some cannot \u2013 we have attempted to make the code as generic as possible, but it does not, and was not designed to, provide general code for modelling the effects of any wind farm in any geographical area. \u00a0The model can be downloaded from the table below.
\n"}]},"field_infomration_images":{"und":[{"fid":"417","uid":"21","filename":"guillemot_SNH.jpg","uri":"public://guillemot_SNH.jpg","filemime":"image/jpeg","filesize":"70998","status":"1","timestamp":"1458826230","type":"image","field_tags":[],"field_file_image_alt_text":{"und":[{"value":"Several Guillemots at a cliff edge","format":null,"safe_value":"Several Guillemots at a cliff edge"}]},"field_file_image_title_text":{"und":[{"value":"Guillemot - Copyright \u00a9 SNH","format":null,"safe_value":"Guillemot - Copyright \u00a9 SNH"}]},"_drafty_revision_requested":"FIELD_LOAD_CURRENT","rdf_mapping":[],"title":"Guillemot - Copyright \u00a9 SNH","alt":"Several Guillemots at a cliff edge","metadata":[],"width":"800","height":"532"}]},"field_infomration_data_link":{"und":[{"target_id":"13614"}]},"field_information_map_link":[],"field_theme":{"und":[{"tid":"956"},{"tid":"47"}]},"field_google_plus_gallery":[],"field_tags":{"und":[{"tid":"950"},{"tid":"951"},{"tid":"630"}]},"field_footer_text":{"und":[{"value":"Technical Details of the Model
\r\n\r\nThis simplified model includes three options for quantifying the magnitude of barrier effects. One option is to assume that they come from a normal distribution with mean 20 km and standard deviation 5 km; this corresponds to the way that barrier effects are specified in the full model. The remaining two options assume that the bird will incur an additional flight cost by flying around the wind farm rather than straight through it and quantify this additional cost by assuming that the wind farm is circular. The two options specify the radius of the circle in two ways:
\r\n\r\nInput Files
\r\n\r\nThe model requires six input files; one file to provide species-specific information about the spatial distribution of the species within the region of interest, one file to provide parameter values for the model, and four files to provide meta-information.
\r\n\r\nThe four meta-information files contain:
\r\n\r\ncolonies.csv : locations of each colony (with columns \u2018colony\u2019, \u2018longname\u2019, \u2018long\u2019, \u2018lat\u2019)
\r\n\r\ncolonysize.csv : size of each colony, in terms of number of pairs (with columns \u2018SPA\u2019, \u2018Colony\u2019, \u2018Guillemot\u2019, \u2018Razorbill\u2019, \u2018Kittiwake\u2019, \u2018Puffin\u2019, \u2018Gannet\u2019). Note that SPAs may contain more than one colony.
\r\n\r\nwfinfo.csv : locations, on the 0.5 x 0.5 km GEBCO grid, that are contained within the core and buffer regions for each wind farm. Columns are: \u2018WFcode\u2019, \u2018Longitude\u2019, \u2018Latitude\u2019. Ten options are currently used for WFcode: \u2018IC-footprint\u2019 (Inchcape footprint), \u2018IC-buffer\u2019 (Inchcape buffer), and so on for NnG, R3A, R3B and R3AB.
\r\n\r\nwfstats.csv : summaries of the relationship between each wind farm and each SPA. Columns are: SPA, WindFarm, Dist, AngleMid, large.anglerange, large.radius, large.MAD, small.anglerange, small.radius, small.MAD. \u2018Dist\u2019 is the distance, in km, between the central colony of the SPA and the centre of the wind farm. The remaining columns refer to the sets of bearings from the SPA that would include the wind farm.
\r\n\r\nThe parameter.csv file contains parameter values to use in running the displacement model \u2013 an example is provided. Information is required for all rows and all species. The \u2018Version\u2019 column is used when performing sensitivity analysis, and should be left blank otherwise.
\r\n\r\n| \r\n\t\t\t Input file \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Specific to a particular set of colonies? \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Specific to a particular set of wind farms? \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Specific to a particular species or set of species? \r\n\t\t\t | \r\n\t\t
|---|---|---|---|
| \r\n\t\t\t colonies.csv \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t
| \r\n\t\t\t colonysize.csv \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t
| \r\n\t\t\t wfinfo.csv \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t
| \r\n\t\t\t wfstats.csv \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t
| \r\n\t\t\t parameters.csv \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t
| \r\n\t\t\t spatial.csv \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t \r\n\t\t\t | \r\n\t\t\t\r\n\t\t\t Yes \r\n\t\t\t | \r\n\t\t
Model Functions
\r\n\r\nThree functions are used to directly run the model and process the output; the actual calculations are performed by a number of additional functions, but these are not called directly by the user.
\r\n\r\n\u201cvsdm.runmodel\u201d runs the model for a single set of parameter values. The arguments are:
\r\n\r\ndat : spatial distribution of the species
\r\n\r\nspecies : species name
\r\n\r\nmeta : meta information about SPAs and wind farms
\r\n\r\npars.full : parameter values
\r\n\r\npars.version : which version of the parameters to use (default is to use pars.version=0, which corresponds to none of the alternative versions being used)
\r\n\r\nppop : which proportion of the population to use for simulation (default is to use ppop = 1, which corresponds to 100% of the population)
\r\n\r\nmin.prey.adjust : ratio to use for adjusting minimum prey values
\r\n\r\nseedval.location : random seeds to use for simulating locations; set automatically if not specified
\r\n\r\nseedval.behaviour : random seed to use for simulation foraging behaviour; set automatically if not specified
\r\n\r\n\u201cvsdm.sensitivity\u201d runs the model for all parameter sets listed in the \u2018parameters.csv\u2019 file. The arguments are identical to those for \u201cvsdm.runmodel\u201d except that \u2018pars.version\u2019 is no longer an option (since the model is now run for all versions).
\r\n\r\n\u201cgetsummary.zones\u201d summarizes the output from \u201cvsdm.sensitivity\u201d in a format that can be used to create a CSV file (spreadsheet). The sole argument is \u2018out\u2019, the output from running \"vsdm.sensitivity\".
\r\n\r\nExample
\r\n\r\nThe following contains the R code used to run the analysis for kittiwake.
\r\n\r\nsetwd(\"~/Work/Project/CEH/Displacement/SimplifiedModel/CurrentModel/\")
\r\n\r\nsource(\"Simplified-Displacement-Model-Version17.R\")
\r\n\r\npars.full <- (read.csv(\"Parameters/parameters-gps.csv\"))[,3:9]
\r\n\r\nmeta <- list(wf = read.csv(\"Meta/wfinfo.csv\"), colony = read.csv(\"Meta/colonies.csv\"), colonysize = read.csv(\"Meta/colonysize.csv\"), wfstats = read.csv(\"Meta/wfstats.csv\"))
\r\n\r\nlevels(meta$colony$colony) <- c(\"ForthIslands\", \"Boddam\", \"FAR\", \"Fowlsheugh\", \"ForthIslands\", \"StAbbsHead\", \"TPL\")
\r\n\r\nlevels(meta$wfstats$SPA)[3] <- \"ForthIslands\"
\r\n\r\n## Species-specific part :
\r\n\r\ndat <- read.csv(\"/tmpshare/adam/projects/seabirds/Displacement/out-sixth/kittiwake.csv\")
\r\n\r\nlevels(dat$Site)[3] <- \"ForthIslands\"
\r\n\r\nout.kittiwake <- vsdm.sensitivity(dat, species = \"Kittiwake\", meta = meta, pars.full = pars.full, ppop = 1/3)
\r\n\r\nwrite.csv(getsummary.zones(out.kittiwake), file=\"Output/sensitivity-kittiwake.csv\", quote=FALSE, row.names=FALSE)
\r\n","format":"full_html","safe_value":"Technical Details of the Model
\nThis simplified model includes three options for quantifying the magnitude of barrier effects. One option is to assume that they come from a normal distribution with mean 20 km and standard deviation 5 km; this corresponds to the way that barrier effects are specified in the full model. The remaining two options assume that the bird will incur an additional flight cost by flying around the wind farm rather than straight through it and quantify this additional cost by assuming that the wind farm is circular. The two options specify the radius of the circle in two ways:
\nInput Files
\nThe model requires six input files; one file to provide species-specific information about the spatial distribution of the species within the region of interest, one file to provide parameter values for the model, and four files to provide meta-information.
\nThe four meta-information files contain:
\ncolonies.csv : locations of each colony (with columns \u2018colony\u2019, \u2018longname\u2019, \u2018long\u2019, \u2018lat\u2019)
\ncolonysize.csv : size of each colony, in terms of number of pairs (with columns \u2018SPA\u2019, \u2018Colony\u2019, \u2018Guillemot\u2019, \u2018Razorbill\u2019, \u2018Kittiwake\u2019, \u2018Puffin\u2019, \u2018Gannet\u2019). Note that SPAs may contain more than one colony.
\nwfinfo.csv : locations, on the 0.5 x 0.5 km GEBCO grid, that are contained within the core and buffer regions for each wind farm. Columns are:\u00a0 \u2018WFcode\u2019, \u2018Longitude\u2019, \u2018Latitude\u2019. Ten options are currently used for WFcode: \u2018IC-footprint\u2019 (Inchcape footprint), \u2018IC-buffer\u2019 (Inchcape buffer), and so on for NnG, R3A, R3B and R3AB.
\nwfstats.csv :\u00a0 summaries of the relationship between each wind farm and each SPA.\u00a0 Columns are: SPA, WindFarm, Dist, AngleMid, large.anglerange, large.radius, large.MAD, small.anglerange, small.radius, small.MAD. \u2018Dist\u2019 is the distance, in km, between the central colony of the SPA and the centre of the wind farm. The remaining columns refer to the sets of bearings from the SPA that would include the wind farm.
\nThe parameter.csv file contains parameter values to use in running the displacement model \u2013 an example is provided. Information is required for all rows and all species. The \u2018Version\u2019 column is used when performing sensitivity analysis, and should be left blank otherwise.
\n| \n Input file \n | \n\n Specific to a particular set of colonies? \n | \n\n Specific to a particular set of wind farms? \n | \n\n Specific to a particular species or set of species? \n | \n
|---|---|---|---|
| \n colonies.csv \n | \n\n Yes \n | \n\n \u00a0 \n | \n\n \u00a0 \n | \n
| \n colonysize.csv \n | \n\n Yes \n | \n\n \u00a0 \n | \n\n \u00a0 \n | \n
| \n wfinfo.csv \n | \n\n \u00a0 \n | \n\n Yes \n | \n\n \u00a0 \n | \n
| \n wfstats.csv \n | \n\n Yes \n | \n\n Yes \n | \n\n \u00a0 \n | \n
| \n parameters.csv \n | \n\n \u00a0 \n | \n\n \u00a0 \n | \n\n Yes \n | \n
| \n spatial.csv \n | \n\n Yes \n | \n\n \u00a0 \n | \n\n Yes \n | \n
Model Functions
\nThree functions are used to directly run the model and process the output; the actual calculations are performed by a number of additional functions, but these are not called directly by the user.
\n\u201cvsdm.runmodel\u201d runs the model for a single set of parameter values. The arguments are:
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 dat : spatial distribution of the species
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 species : species name
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 meta : meta information about SPAs and wind farms
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pars.full : parameter values
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pars.version : which version of the parameters to use (default is to use pars.version=0, which corresponds to\u00a0none of the alternative versions being used)
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ppop : which proportion of the population to use for simulation (default is to use ppop = 1, which corresponds to 100% of the population)
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0min.prey.adjust : ratio to use for adjusting minimum prey values
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 seedval.location : random seeds to use for simulating locations; set automatically if not specified
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 seedval.behaviour : random seed to use for simulation foraging behaviour; set automatically if not specified
\n\u201cvsdm.sensitivity\u201d runs the model for all parameter sets listed in the \u2018parameters.csv\u2019 file. The arguments are identical to those for \u201cvsdm.runmodel\u201d except that \u2018pars.version\u2019 is no longer an option (since the model is now run for all versions).
\n\u201cgetsummary.zones\u201d summarizes the output from \u201cvsdm.sensitivity\u201d in a format that can be used to create a CSV file (spreadsheet). The sole argument is \u2018out\u2019, the output from running \"vsdm.sensitivity\".
\nExample
\nThe following contains the R code used to run the analysis for kittiwake.
\nsetwd(\"~/Work/Project/CEH/Displacement/SimplifiedModel/CurrentModel/\")
\nsource(\"Simplified-Displacement-Model-Version17.R\")
\npars.full <- (read.csv(\"Parameters/parameters-gps.csv\"))[,3:9]
\nmeta <- list(wf = read.csv(\"Meta/wfinfo.csv\"), colony = read.csv(\"Meta/colonies.csv\"), colonysize = read.csv(\"Meta/colonysize.csv\"), wfstats = read.csv(\"Meta/wfstats.csv\"))
\nlevels(meta$colony$colony) <- c(\"ForthIslands\", \"Boddam\", \"FAR\", \"Fowlsheugh\", \"ForthIslands\", \"StAbbsHead\", \"TPL\")
\nlevels(meta$wfstats$SPA)[3] <- \"ForthIslands\"
\n## Species-specific part :
\ndat <- read.csv(\"/tmpshare/adam/projects/seabirds/Displacement/out-sixth/kittiwake.csv\")
\nlevels(dat$Site)[3] <- \"ForthIslands\"
\nout.kittiwake <- vsdm.sensitivity(dat, species = \"Kittiwake\", meta = meta, pars.full = pars.full,\u00a0 ppop = 1/3)
\n\u00a0write.csv(getsummary.zones(out.kittiwake), file=\"Output/sensitivity-kittiwake.csv\", quote=FALSE, row.names=FALSE)
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