DOI: 10.5061/dryad.q9c54
title: Data from: Consensus forecasting of intertidal seagrass habitat in the Wadden Sea

description: After the dramatic eutrophication-induced decline of intertidal seagrasses
 in the 1970s, the Wadden Sea has shown diverging developments. In the
 northern Wadden Sea, seagrass beds have expanded and become denser, while
 in the southern Wadden Sea, only small beds with low shoot densities are
 found. A lack of documentation of historical distributions hampers
 conservation management. Yet, the recovery in the northern Wadden Sea
 provides opportunity to construct robust habitat suitability models to
 support management. We tuned habitat distribution models based on 17 years
 of seagrass surveys in the northern Wadden Sea and high-resolution
 hydrodynamics and geomorphology for the entire Wadden Sea using five
 machine learning approaches. To obtain geographically transferable models,
 hyperparameters were tuned on the basis of prediction accuracy assessed by
 non-random, spatial cross-validation. The spatial cross-validation
 methodology was combined with a consensus modelling approach. The
 predicted suitability scores correlated amongst each other and with the
 hold-out observations in the training area indicating that the models
 converged and were transferable across space. Prediction accuracy was
 improved by averaging the predictions of the best models. We graphically
 examined the relationship between the consensus suitability score and
 independent presence-only data from outside the training area using the
 area-adjusted seagrass frequency per suitability class (continuous Boyce
 index). The Boyce index was positively correlated with the suitability
 score indicating the adequacy of the prediction methodology. We used the
 plot of the continuous Boyce index against habitat suitability score to
 demarcate three habitat classes – unsuitable, marginal and suitable – for
 the entire international Wadden Sea. This information is valuable for
 habitat conservation and restoration management. Divergence between
 predicted suitability and actual distributions from the recent past
 indicates that unaccounted factors limit seagrass development in the
 southern Wadden Sea. Synthesis and applications. Our methodology and data
 enabled us to produce a robust and validated consensus habitat suitability
 model. We identified highly suitable areas where intertidal seagrass
 meadows may establish and persist. Our work provides scientific
 underpinning for effective conservation planning in a dynamic landscape
 and sets monitoring priorities.
description: distance to land
 (geotif, epsg:3035)used for imputing
 sedimentdland.tifmean exposure time (geotif, epsg:3035)Exposure time of the intertidal flats: mean fraction of time (between 0 and 1) that the seafloor is exposed to air.exp.mean.tifintertidal regions (geotif, epsg:3035)Intertidal regions in the Wadden Sea: 1. Denmark; 2. Schleswig-Holstein; 3. Lower Saxony; 4. Netherlandsregion.tifseagrass occupation (geotif, epsg:3035)Proportion of the 17 years that a cell was occupied. These data cover Schleswig-Holstein.sg.prop.tifbottom shear stress (geotif, epsg:3035)mean bottom shear stress due to currentstaub.mean.tifbottom shear stress Q75 (geotif, epsg:3035)75th percentile of bottom shear stress due to currentstaub.Q75.tifslope of seaflooor (geotif, epsg:3035)slope.tiftidal basins (geotif, epsg:3035)39 tidal basin of the Wadden Sea. They are used to split the Schleswig-Holstein training area data for spatial cross-validation.TB.tiffunc_seagrass R script with libraries and helper functionsfunc_seagrass.R1_impute: R script for imputation of sediment data1_impute.R2_prepare: R-script spatial subsetting and transformation2_prepare.R3_train: R-script model trainingThis script trains the ML algorithms on the basis of spatial and random cross-validation. It applies the caret machinery.3_train.R4_gop: R-script for computing goodness of predictionGoodness of Prediction is computed for all models based on random and spatial cross-validation. The gop of the consensus model is also computed. The script outputs the predictions of the best (lowest RMSE) models and latex tables.4_gop.R5_ensemble: R-script for constructing the ensemble of predictions5_ensemble.R6_consensus: R script to produce the consensus predictionsThis script produces the consensus predictions (weighted average and weighted prediction variance) on the basis of the ensemble of predictions. It also adds the presence only data.6_consensus.R7_Boyce: R script for validation with presence-only dataArea-adjusted frequency of the presence of seagrass (“continuous Boyce index”) vs HSS (habitat suitability score) on the basis of a rolling mean.7_Boyce.RPresence only shapefileA shapefile (epsg:3035) with observations/occurrences of seagrass. It is presence-only because absences are not systematically scored.shapefile_presence_only.zipsessionInfoThe output of the R-function sessionInfo() which shows the used versions of R and packages.Consensus habitat suitability scores (geotif; epsg:3035)Consensus habitat suitability scores for littoral seagrass habitat in the Wadden Sea.WaddenSea_littoral_seagrass_habitat_consensus_score.tifSpread in the habitat suitability scores (geotif; epsg:3035)Spread in the habitat suitability scores for littoral seagrass habitat in the Wadden Sea.WaddenSea_littoral_seagrass_habitat_ensemble_spread.tifReclassified habitat suitability scores (geotif; epsg:3035)Reclassification (3 classes) of the consensus suitability scores for littoral seagrass habitat in the Wadden Sea.WaddenSea_littoral_seagrass_habitat_3classes.tif