# Data and Code for: 'Sea level rise causes shorebird population collapse before habitat drowns' [https://doi.org/10.5061/dryad.wm37pvmth](https://doi.org/10.5061/dryad.wm37pvmth) The repository contains three R files used to run the code, as well as a large number of data files that provide input data needed to initialize the model in R. ## Description of the data and file structure *Folder and file structure* There are 6 folders: * maps * water * mining * bmp * code * products *Elevation maps of three islands from LiDAR data in 2014.* The folder '**maps**' contains for each island several rasterfiles are available in GeoTiff format downloaded from [https://ahn.arcgisonline.nl/ahnviewer/](https://ahn.arcgisonline.nl/ahnviewer/). Each raster-file contains a 5x5km grid of 0.5x0.5m cells. X and Y coordinates are in RD Dutch Grid (m) and the elevation (m) is relative to Dutch Ordnance level (NAP). Each island encompasses multiple adjacent raster-files. o Ameland: M_02CN1.TIF,M_02CN2.TIF, M_02DN1.TIF o Schiermonnikoog: M_02GN2.TIF, M_02EZ2.TIF, M_02HN1.TIF, M_02FZ1.TIF, M_02HN2.TIF, M_02FZ2.TIF, M_03AZ1.TIF o Terschelling: M_01GZ2.TIF, M_01GZ1.TIF, M_01GN1.TIF, M_01DZ2.TIF *Wadden sea coastline:* The folder '**maps'** also contain files with the coordinates of the Wadden Sea coastline for each of the three study island, derived from above rasters (see Methods in paper). The X and Y coordinates are in RD Dutch Grid (m). o CoastSouth_Ameland.txt o CoastSouth_Schiermonnikoog.txt o CoastSouth_Terschelling.txt *North Sea coastline:* The folder maps also contain files with the coordinates of the North Sea coastline for each of the three study island, derived from above rasters (see Methods in paper). The X and Y coordinates are in RD Dutch Grid (m). o CoastNorth_Ameland.txt o CoastNorth_Schiermonnikoog.txt o CoastNorth_Terschelling.txt *Creeks:* The folder maps also contain files with the coordinates of the creeks on each of the three study island, derived from above rasters (see Methods in paper). The X and Y coordinates are in RD Dutch Grid (m). o creekonly_ameland.txt o creekonly_schiermonnikoog.txt o creekonly_terschelling.txt *10-minute water levels readings from 1981-2022 at local tidal station:* The folder '**water**' contains data downloaded from [https://waterinfo.rws.nl/. ](https://waterinfo.rws.nl/.)The variables are ID, date (YYYY/MM/DD), time (HH/MM/SS), and value (the water level in meters relative to Dutch Ordnance level (NAP in m) at 10 minute intervals from 1982-2022. Each island has a separate file based on the local tidal gauge station at Nes (Ameland island), Schiermonnikoog (Schiermonnikoog island) and West-Terschelling (Terschelling island). o water_ameland.txt o water_schiermonnikoog.txt o water_terschelling.txt *Daily high tide water levels:* The folder '**water**' contains data downloaded from [https://waterinfo.rws.nl/. ](https://waterinfo.rws.nl/.)The variables are date (YYYY-MM_DD), lowest (the water level at lowest high tide of that day relative to Dutch Ordnance level (NAP in meters)), highest (the water level at highest high tide of that day relative to Dutch Ordnance level (NAP in meters), daynr (the number of the day in the year, with 1st April set to 1), year (the year of measurement), mean.high.tide (the mean high tide of the year relative to Dutch Ordnance level (NAP in meters)), and MHT.start.year (the mean high tide of the start year of simulation (1986) relative to Dutch Ordnance level (NAP in meters)). Each island has a separate file based on the local tidal gauge station at Nes (Ameland island), Schiermonnikoog (Schiermonnikoog island) and West-Terschelling (Terschelling island). o HighTidesAmeland.txt o HighTidesSchiermonnikoog.txt o HighTidesTerschelling.txt *Modelled deep soil subsidence due to gas mining for 1986-2050 in a 250x250m grid:* The folder '**mining**' contains rasters on the deep soil subsidence due to gas mining on Ameland island. The variables are the X and Y coordinate in RD Dutch Grid (m) and then one column providing the value of cumulative deep soil subsidence due to local gas mining (in cm subsidence) for each year from 1986-2050 (columns J1986 to J2050). The three files represent a low , mid (base) and high projection of deep soil subsidence on Ameland island (see Methods in paper). o Base scenario: DeepSubsidence_Ameland_MeR2021 \_base.txt o Low (2.5%): DeepSubsidence_Ameland_MeR2021 \_low.txt o High (97.5%): DeepSubsidence_Ameland_MeR2021 \_high.txt *Bird distribution maps:* The folder '**bmp**' contains files with observed bird distribution maps. A list of coordinates (X and Y in RD Dutch Grid (m) ) representing the location of midpoints of oystercatchers territories as determined during standardized breeding monitoring program by Sovon Dutch Center for Field Ornithology for each of the study islands [https://sovon.nl/bmp](https://sovon.nl/bmp). The variable 'jaar' gives the year of census and the variable 'gebcode' the name of the (sub)area on each of the islands (each island is censused in smaller subareas). o BMP_Territories_Oystercatcher_Ameland.csv o BMP_Territories_Oystercatcher_Schier.csv o BMP_Territories_Oystercatcher_Terschelling.csv *Model files in R language (using version 4.2.1 [https://www.r-project.org/](https://www.r-project.org/)*)*:* The folder '**code**' containts three files used to run the model simulations' The parameter file contains all model parameters, model settings and names of input data files. The function file contains all R functions needed to run the codeThe RunCode file contains the R code needed to run the model and store the model output - FloodingParameters.R - FloodingFunction.R - FloodingRunCode.R *Examples of model generated output files during initialization* The folder '**products**' contains two types of files for each island that the R code can generate during initialization. As this initialization phase can be time consuming, we have included example files that can be used to run the simulations on the sea level rise scenarios: One set of files for each island describes the initialized location of each territory and their properties : - territories_loc6Ameland.txt - territories_loc6Terschelling.txt - territories_loc6Schiermonnikoog.txt Each row is a territory on the island that can be tracked over time. The variables describe the X and Y coordinates of each territory (in m RD grid; "x.midpoint" "y.midpoint"), whether the territory is occupied (1) or not (0; "occupied") at the start ("occupied"), the distance to the Wadden sea coast line in m ("dist.coast" ) and the distance to the nearest creek in m ( "dist.creek"), the identity (rownumber) of the territories adjacent to the territory ("nb1" "nb2" "nb3" "nb4" "nb5" "nb6"), and the identity of the gridcell that is the midpoint of the territory (id of files directly below. All the other columns are variables that are NA or 0 as they are only used during simulation and not used for initialization, and their meaning is explained in the annotated R code described above. The second set of files for each island describes the initialized elevation of each grid cell that is tracked over time and their properties : - gridcells_loc_start6AmelandD.txt - gridcells_loc_start6TerschellingD.txt - gridcells_loc_start6SchiermonnikoogD.txt Each row is a gridcelll on the island that can be tracked over time. The variables describe the X and Y and Z coordinates of each gridcell (in m RD grid; "x, y, z"). The z-value is the elevation in m above Dutch ordnance level at the start of the simulation (typically the year 1986). For each cell is denoted the territory to which it belong (territory.id) and distance to the mining location (dist.mine), nearest creek (dist.creek) and coastline (dist.coast, all in m) and the distance water has to travel through a creek when depositing sediment (creek.length (in m)). All the other columns are variables that are tracked during simulations and change each timestep and not used for initialization, and their meaning is explained in the annotated R code described above. To run the model and store the model results a folder needs to be created called '**output**' in the same directory as above maps. ## Sharing/Access information Data was derived from a variety of CC0 sources, described in full in Extended table 1 of the paper. ## Code/Software The R code that uses the input data deposited in this repository *(using version 4.2.1*) can be found in folder 'code'*:* - FloodingParameters.R - FloodingFunction.R - FloodingRunCode.R The parameter file contains all model parameters, model settings and names of input data files. The function file contains all R functions needed to run the code The RunCode file contains the R code needed to run the model and store the model output

Sea level rise causes habitat loss and is considered to be a key threat to coastal species globally. Sea level rise also reduces habitat quality, potentially threatening populations already before habitat drowns and is lost. The extent and timing of changes in habitat quality for wildlife actively adapting to sea level rise, and how this affects population numbers under different emission scenarios, is unknown. Here, we combine long-term field data with models of sea level rise, marsh geomorphology, adaptive behaviour, and population dynamics to show that habitat quality is already declining on three islands due to increased flooding of shorebird nests. Also, population collapses are projected well before habitat drowns. Habitat loss, a widely used proxy, thus severely underestimates population impacts of sea level rise and coastal species will suffer much sooner than previously thought. Despite shorebirds adapting by moving to higher grounds, sea level rise will result in up to 79% fewer birds in a century, eventually leading to extinction in their prime habitat. Local gas mining exacerbates matters, as deep soil subsidence makes habitat even more vulnerable to sea level rise, effectively halving the window of opportunity for conservation action. Climate change ultimately jeopardizes the biodiversity value of this UNESCO World Heritage Area, and nature management needs to take this long-term perspective on board by in the short-term, boosting the accretion of tidal marshes or developing flood-safe alternative habitat elsewhere.

See paper for a detailed description of the datasets.