DOI: 10.5061/dryad.msbcc2fxx
title: The role of dispersal limitation and reforestation in shaping the distributional shift of a forest herb under climate change

description: Aim: Forest herbs might be unable to track shifts in habitat suitability
 due to rapid climate change and habitat fragmentation. In this study, we
 quantified the role of dispersal limitation and the potential mitigating
 effect of large-scale reforestation on the redistribution of the
 herbaceous forest plant species Primula elatior under climate change.
 Location: Europe Methods: High resolution (100 m) landscape-scale and
 macro-climatic variables were combined to predict range-wide habitat
 suitability using Maxent. Dispersal limitation was modelled, based on
 isolation-by-resistance (IBR) principles through integration of circuit
 theory and genomic data, to assess patch accessibility and metapopulation
 stability under climate change. Large scale reforestation was evaluated as
 a potential mitigating strategy by incorporating a land-use change
 scenario into the distribution and dispersal models. Results:
 Landscape-scale variables contributed significantly to the distribution of
 P. elatior (78.33%) and to the accuracy of our model (AUC=0.81).
 Isolation-by-resistance (R²cond=0.92) was driven by land-use (45.5%),
 distance from rivers (36.4%) and elevation (18.2%). It was estimated that
 46.4±13.9% (mean±SD of climate change scenarios) of the total distribution
 area would be lost due to climate change by 2050 and an additional
 15.6±1.7% (mean±SD) of the distribution would not be accessible through
 migration. The median latitude of the patch distribution shifted
 183.2±34.8 km (mean±SD) northwards and 58.1±9.3 km (mean±SD) to more
 maritime regions. The patch accessibility was low in these regions and the
 metapopulation stability decreased considerably in the south of the
 distribution. Reforestation mitigated 54.1±18.2% (mean±SD) of the
 accessible distribution area loss and 49.5±4.2% (mean±SD) of the decrease
 in metapopulation stability. Main conclusion: To alleviate the loss of the
 accessible distribution area of P. elatior under climate change, it will
 be required to integrate climate mitigation strategies (RCP 2.6),
 range-wide afforestation, restoration of ecological connectivity and
 focused assisted migration to newly available habitat.
description: Data collection and
 processing <strong>Input_data_SDM.xlsx</strong> Training and validation data (occurrences and pseudo-absences) used for the species distribution modelling. The data processing methodology can be found in section “<i>Species occurrence data and pseudo-absence generation</i>” of the manuscript. Coordinates of the occurrences were removed due to contractual obligations. The coordinates can be requested at the agencies listed in table I of Appendix S1 of the manuscript and an overview of the coordinates can be found in Fig. 1 of Appendix S1. The corresponding raster stack values (explanatory variables) of occurrences and pseudo-absences are integrated in this dataset. <strong>PREDMEAN Raster tif files</strong> The methods regarding the projections in space and time of the species distribution models can be found in the section “<i>Model building and projections</i>” of the manuscript. Further info on the input data preparation can be found in Appendix S1-SIII and more in depth evaluation of the models can be found in Appendix SVI-SV. isolation_by_resistance.zip Methods corresponding to the isolation by resistance relationship can be found in the section “<i>Modelling dispersal and migration events to determine the accessible area</i>” of the manuscript. Detailed methods about the genome sequencing can be found in Appendix SVII (genetic distance) and detailed methods about the effective resistances can be found in Appendix SVIII. habitat_patch_shapefiles.zip Methods in regard to the habitat patch delineation can be found in the “<i>Model building and projections</i>” section of the manuscript, methods regarding the dispersal and migration effective resistances and potential can be found in the “<i>Modelling dispersal and migration events to determine the accessible area</i>” section of the manuscript, and methods regarding the proximity resistance index can be found in the “<i>Assessing shifts in patch distribution and configuration</i>” section of the manuscript. A more in detail description of the genetic optimization procedure and resulting statistics of dispersal potential thresholds can be found in Appendix SVII.
description: Datasets
 Input_data_SDM.xlsx Input_data_SDM.xlsx Training and evaluation data for the species distribution models 00_CURRENT_PREDMEAN.tif The current projected species distribution for <i>Primula elatior.</i> 01_FUT26_PREDMEAN.tif The projected species distribution for <i>Primula elatior </i>in 2050 under the RCP 2.6 scenario. 01b_FUT26_LUC_PREDMEAN.tif The projected species distribution for <i>Primula elatior </i>in 2050 under the RCP 2.6 scenario with projected large scale reforestation. 02_FUT45_PREDMEAN.tif The projected species distribution for <i>Primula elatior</i> in 2050 under the RCP 4.5 scenario. 02b_FUT45_LUC_PREDMEAN.tif The projected species distribution for <i>Primula elatior </i>in 2050 under the RCP 4.5 scenario with projected large scale reforestation. 03_FUT85_PREDMEAN.tif The projected species distribution for <i>Primula elatior</i> in 2050 under the RCP 8.5 scenario. 03b_FUT85_LUC_PREDMEAN.tif The projected species distribution for <i>Primula elatior </i>in 2050 under the RCP 8.5 scenario with projected large scale reforestation. isolation_by_resistance.zip genetic_samples_locations.shp Shapefile with the locations of the genetic samples isolation_by_resistance_data.txt text file with genetic distances, effective resistances and their derivatives between pairs of sampling locations isolation_by_resistance_NLS_model.rds The Nonlinear Least Squares model of the isolation by resistance relationship. The rds data format is a R storage data format that can be read with the readRDS(file = "isolation_by_resistance_NLS_model.rds") function. habitat_patch_shapefiles.zip 00_CURRENT.shp Shapefiles of the current projected habitat patch distribution of <i>Primula elatior</i>. 01_FUT26.shp Shapefiles of the projected habitat patch distribution of <i>Primula elatior</i> in 2050 under the RCP 2.6 scenario. 01b_FUT26_LUC.shp Shapefiles of the projected habitat patch distribution of <i>Primula elatior</i> in 2050 under the RCP 2.6 scenario with projected large scale reforestation. 02_FUT45.shp Shapefiles of the projected habitat patch distribution of <i>Primula elatior</i> in 2050 under the RCP 4.5 scenario. 02b_FUT45_LUC.shp Shapefiles of the projected habitat patch distribution of <i>Primula elatior</i> in 2050 under the RCP 4.5 scenario with projected large scale reforestation. 03_FUT85.shp Shapefiles of the projected habitat patch distribution of <i>Primula elatior</i> in 2050 under the RCP 8.5 scenario. 03b_FUT85_LUC.shp Shapefiles of the projected habitat patch distribution of <i>Primula elatior</i> in 2050 under the RCP 8.5 scenario with projected large scale reforestation. Metadata Input_data_SDM.xlsx Table 1: Field names and their description of the input data for the species distribution models. Coordinates for the occurrence data was removed due to contractual obligations. Field Description ID Occurrence ID. Concatenated field name based on the original dataset and occurrence number. aspect Raster value extract of aspect (exposure) carpinus Raster value extract of landscape-scale Carpinus tree density CLC Raster value extract corine land-use DEM Raster value extract of the digital elevation model in meters dry_season_18 Raster value extract of precipitation in the dry season (worldclim 18) fagus Raster value extract of landscape-scale Fagus tree density quercus Raster value extract of landscape-scale Quercus tree density River_log Raster value extract of logarithmic river distance seasonality_4 Raster value extract of temperature seasonality (worldclim 4) Soil_physical_PC1 Raster value extract of the first principal component of physical soil parameters Soil_physical_PC2 Raster value extract of the second principal component of physical soil parameters wet_month_13 Raster value extract of precipitation in the wetterst month (worldclim 13) WPI Raster value extract of the wetness proximity index longitude X coordinate in LAEA89 latitude Y coordinate in LAEA89 dataset Dataset name: training or evaluation / occurrence or pseudo-absence / repetition ID   Table 2: different datasets combined as determined by the dataset field. Each dataset name has 7 repetitions equal to the model repetition. dataset names Description 01_occurrence_training Spatially thinned <i>Primula elatior</i> occurrences from 2000-2020 02_pseudoabsence_training Spatially thinned background data (pseudo-absences) with approx. 200% quantity compared to occurrences 03_occurrence_evaluation Spatially thinned and geographically independent evaluation data of <i>Primula elatior</i> from 2000-2020 04_pseudoabsence_evaluation Spatially thinned background data (pseudo-absences) for evaluation with approx. 200% quantity compared to occurrences   PREDMEAN Raster tif files All raster predictins indicate the average occurrence probability ranging between 0 and 1 based on the 7 repetitions. isolation_by_resistance.zip Table 3: table of the isolation_by_resistance metadata. Source depicts the file that is described, field describes the column name and description describes the field name. Source Field Description isolation_by_resistance_data to ID of samples habitat patches dispersal target (bidirectional matrix) isolation_by_resistance_data from ID of samples habitat patches source target (bidirectional matrix) isolation_by_resistance_data NEI_dist Nei's genetic distance isolation_by_resistance_data resistance Effective resistance (based on the optimized resistance matrix) isolation_by_resistance_data inverse_standardised _genetic_distance inverse standardised genetic distance (transformed Nei's distance) isolation_by_resistance_data logaritmic_resistance Logarithmic effective resistance genetic_samples_locations POP_ID ID of samples genetic_samples_locations X LAEA89 coordinate in meters genetic_samples_locations Y LAEA89 coordinate in meters isolation_by_resistance_NLS _model.rds m Contains NLS model in rds R data format based on the isolation_by_resistance_data   habitat_patch_shapefiles.zip Table 4: Description of Primula elatior habitat patch metrics in the shapefiles. “Field name” is the ID of each field, “Unit” describes the unit of the metric and “description” describes the metric. . Field name Unit Description ID positive integer Patch ID  X meters (LAEA89) X coordinate of patch centroid  Y meters (LAEA89) Y coordinate of patch centroid  sea_dist meters Distance from the sea  DEM meters Elevation  aspect degrees Orientation of slope  area m² Patch area  area_ha hectares Patch area  NEAR_DIS meters Nearest neighbour distance between patches within a scenario  ERD_SS effective resistance Effective resistance of dispersal between nearest neightbour patches with short circuit methodology (no resistance within patch)  ERD_CEN effective resistance Effective resistance of dispersal from patch centroid to its nearest neighbour (with additional resistance within the patch)  ERD_WPD effective resistance Effective resistance of dispersal from patch centroid to patch edge (within-patch dispersal): res_cen - res_SS  M_DIS meters Nearest neighbour distance between current patches and projected patches in 2050  MCL_DIS meters Nearest neighbour distance between current source patches and projected patches in 2050 (source patches with a within-patch dispersal potential &gt; 50)  ER_M effective resistance Effective resistance of migration between current patches and its nearest neighbour projected future patch  ER_M_WL effective resistance Effective resistance of migration between current source patches and nearest neighbour projected future patches (source patches with a within-patch dispersal potential &gt; 50)  POT_DSS inverse standardised genetic distance Dispersal potential (predicted inverse standardized genetic distance)  POT_WPD inverse standardised genetic distance Within-patch dispersal potential  (predicted inverse standardized genetic distance)  POT_M inverse standardised genetic distance Migration potential from all patches (predicted inverse standardized genetic distance)  POT_M_WL inverse standardised genetic distance Migration potential from source patches (predicted inverse standardized genetic distance used for migration potential in the manuscript)  PRI sum(area/resistance) Proximity resistance index with a distance threshold of 5 km