Data from: Dispersal and connectivity in increasingly extreme climatic conditions
Data files
Apr 23, 2024 version files 1.97 GB
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FloodDispersal.zip
1.97 GB
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README.md
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Abstract
While climate change has been shown to impact several life-history traits of wild-living animal populations, little is known about its effects on dispersal and connectivity.
Here, we capitalize on the highly variable flooding regime of the Okavango Delta to investigate impacts of changing environmental conditions on dispersal and connectivity of the endangered African wild dog (Lycaon pictus). Based on remote sensed flood extents observed over 20 years, we derive two extreme flood scenarios: a minimum and a maximum flood extent; representative of very dry and very wet environmental periods. These conditions are akin to those anticipated under increased climatic variability, as it is expected under climate change. Using a movement model parametrized with GPS data from dispersing individuals, we simulate 12,000 individual dispersal trajectories across the ecosystem under both scenarios and investigate patterns of connectivity.
Across the entire ecosystem, surface water coverage during maximum flood extent reduces dispersal success (i.e., the propensity of individuals to disperse between adjacent subpopulations) by 12% and increases dispersal durations by 17%. Locally, however, dispersal success diminishes by as much as 78%. Depending on the flood extent, alternative dispersal corridors emerge, some of which in the immediate vicinity of human-dominated landscapes. Notably, under maximum flood extent, the number of dispersing trajectories moving into human-dominated landscapes decreases by 41% at the Okavango Delta’s inflow, but increases by 126% at the Delta’s distal end. This may drive the amplification of human-wildlife conflict.
Whilst predicting the impacts of climate change on environmental conditions on-the-ground remains challenging, our results highlight that environmental change may have significant consequences for dispersal patterns and connectivity, and ultimately, population viability. Acknowledging and anticipating such impacts will be key to effective conservation strategies and to preserve vital dispersal corridors in light of climate change and other human-related landscape alterations.
https://doi.org/10.5061/dryad.z34tmpgnm
This repository contains all R-code and data to reproduce the analyses and visualizations from Hofmann et al., 2024. It is recommended to explore the data through the provided R-Scripts. A general design principle was to compartmentalize all analyses and simulations to reduce computational requirements. As such, there are often parent files (in .rds format) that provide overviews and bundle further data files as tidyverse data-tibbles. All R-codes are extensively documented, giving detailed insights into the processing and analytical steps.
Description of the File Structure
The file structure of the repository is as follows:
Description of Data Files
01_RawData
Raw data was further processed
FLOODMAPS/YYYY.MM.DD.tif- 0: Water
- 127: Cloud cover
- 255: Dryland
GLOBELAND/Water.tif- 0: Dryland
- 1: Water
MERIT/Rivers.tif- 0: Dryland
- 1: River (only rivers that are > 10 meters wide)
MODIS/Shrubs.tif- 0-100: Percentage cover by shrubs
- 200: Invalid / water
MODIS/Trees.tif- 0-100: Percentage cover by trees
- 200: Invalid / water
02_CleanData
Clean data was not further processed. Some of this data was obtained already cleaned from other data-sources (see below).
Shapefiles
Shapefiles are in EPSG:4326 projection and can be loaded using terra::vect().
Africa.shp:- ID: Running number to identify individual polygons
- CODE: Country code
- COUNTRY: Name of the country
AreasOfInterest.shp:- ID: Running number to identify individual polygons
- Name: Name of the area covered by each polygon
Cutlines.shp(generated from theSourceAreas.Rscript):- FID: Running number to identify individual cutlines
Faults.shp:- id: Running number to identify individual fault lines
- Name: Name of the fault line
KAZA.shp:- Name: Name of the area
MababeDepression.shp:- Name: Name of the area
MajorRivers.shp:- Name: Name of each river
MajorWaters.shp:- Name: Name of each water source
- Category: Category of the water source
Protected.shp:- Name: Name of the protected area
- IUCN: IUCN category of the protected area
- Country: Country in which the protected area lies
- Desig: Designation of the protected area (reclassified from the world database of protected areas into national park, protected area, forest reserve)
- Values: Numerical representation of the designation (3 = national park, 2 = protected area, 1 = forest reserve)
SourceAreas.shp(generated from theSourceAreas.Rscript):- ID: Running number to identify the source areas
- Type: Category of the source area
- Main: Areas within the main study area
- Buffer: Egression zone
Villages.shp:- name: name of the village
- place: category of the village (Village or City)
Raster data
Raster data is in EPSG:4326 projection and can be loaded using terra::rast().
DistanceToHumans.tif:- Values: Distance (in meters) to the nearest human influenced grid cell (roads, settlement, agriculture)
DistanceToWater.tif:- Values: Distance (in meters) to the nearest grid cell covered by water for a minimum, average, and maximum flood scenario.
HumanInfluence.tif:- Values: Relative strength of human influence (roads, settlements, and agriculture). The derivation of this layer is described by Hofmann et al., 2021.
ShrubCover.tif:- Values: Proportion of shrub cover (0 to 1) for a minimum, average, and maximum flood scenario.
TreeCover.tif:- Values: Proportion of tree cover (0 to 1) for a minimum, average, and maximum flood scenario.
WaterCover.tif:- Values: Proportion of water cover (0 or 1) for a minimum, average, and maximum flood scenario.
R-Data
R-Data files are in .rds format and can be loaded into R using readr::read_rds().
MovementModel.rds(i.e. the dispersal model):- Model of the class
glmmTMB, which requires theglmmTMBR-package to be installed and loaded. This model was used to predict selection scores. Covariates extracted during the simulation need to be scaled using the scaling parameters stored inScaling.rds.
- Model of the class
GammaDistribution.rds(used to propose random steps):- Shape: Estimated shape parameter of the step-length distribution
- Scale: Estimated scale parameter of the step-length distribution
Scaling.rds(used to scale extracted covariates):- center: value by which covariates are shifted
- scale: value by which covariates are scaled
03_Results
The following files will all be generated when running through the analyses. Intermediate results are stored in the 99_... folders, and only later consolidated into single files. The .rds files give overviews over the exact simulation parameters and associated files. For instance, the file DispersalSimulations.rds consolidates the individual simulations stored under 99_Simulations.
Overviews / Consolidated files:
DispersalSimulation.rds:- x: Simulated longitude
- y: Simulated latitude
- absta_: absolute turning angle (in radians)
- ta_: relative turning angle (in radians)
- sl_: step length (in meters)
- Timestamp: POSIXct timestamp
- BoundaryHit: Logical indicator if the simulated individual has hit a map boundary or not
- inactive: Logical indicator during what time a step is taken
- 0 = Inactive
- 1 = Active
- TrackID: Identifier of the simulated track
- SimID: Identifier of the simulation number
- FloodLevel: Flood scenario under which dispersal was simulated (either minimum or maximum flood, see
Water.tif) - SourceArea: Source area from which individuals were simulated (i.e., the id of the
SourceArea.shp) - StepNumber: Step number of the simulated step (1-2000)
Distance.rds:- FloodLevel: Flood scenario (either minimum or maximum flood, see
Water.tif) - SourceArea: Source area from which individuals were simulated (i.e., the id of the
SourceArea.shp) - Filename: Filepath to where the raster files are stored.
- Level: Whether the metric is computed for a specific source area (Local) or if it was compute across all source areas (Global).
- FloodLevel: Flood scenario (either minimum or maximum flood, see
DistanceAOI.rds:- FloodLevel: Flood scenario (either minimum or maximum flood, see
Water.tif) - Name: Name of the area of interest, see
AreasOfInterest.shp - Number: Density of trajectories within the area of interest
- SE: Standard error of the density of trajectories within the area of interest
- Percent: Percent change of the density from the minimum to maximum flood
- combined: Latex code for the number and standard error. This can be included in reports
- FloodLevel: Flood scenario (either minimum or maximum flood, see
HeatmapsBetweennessGlobal.rds:- Steps: Number of steps after which the metric was computed (500, 1000, or 2000)
- FloodLevel: Flood scenario (either minimum or maximum flood, see
Water.tif) - FilenameHeatmap: Filepath to where the raster file of the resulting heatmap is stored
- FilenameBetweenness: Filepath to where the raster file of the resulting betweenness map is stored
- Heatmap: Heatmap as raster file
- Betwenness: Betweenness map as raster file
HeatmapsBetweennessLocal.rds:- Steps: Number of steps after which the metric was computed (500, 1000, or 2000)
- SourceArea: Source area from which individuals were simulated (i.e., the id of the
SourceArea.shp) - FloodLevel: Flood scenario (either minimum or maximum flood, see
Water.tif) - FilenameHeatmap: Filepath to where the raster file of the resulting heatmap is stored
- FilenameBetweenness: Filepath to where the raster file of the resulting betweenness map is stored
- Heatmap: Heatmap as raster file
- Betwenness: Betweenness map as raster file
InterpatchConnectivityBootstrapped.rds:- SourceArea: Origin of the inter-patch connection (see
SourceAreas.shp) - CurrentArea: Target of the inter-patch connection (see
SourceAreas.shp) - FloodLevel: Considered flood level (min or max)
- Type: Type of the interpatch connection
- Dispersal = Movement between two main source areas
- Egression = Movement from a main source area to an egression zone
- DispersalSuccess: Number of simulated individuals moving between the specified areas
- SDDispersalSuccess: SD of simulated individuals moving between the specified areas
- DispersalDuration: Average minimum duration (in steps) it takes before simulated individuals successfully move from the source to the current area
- DispersalDuration: SD of the minimum duration (in steps) it takes before simulated individuals successfully move from the source to the current area
- SourceArea: Origin of the inter-patch connection (see
Unconsolidated files (these files should not be directly accessed):
99_Betweenness/Betweenness_Steps_FloodLevel.tif:- Values: Betweenness values calculated from simulated dispersers across all source areas
99_Betweenness/Betweenness_Steps_SourceArea_FloodLevel.tif:- Values: Betweenness values calculated from simulated dispersers separately for each source area
99_Betweenness/Distance_SourceArea_FloodLevel.tif:- Values: Number of simulated coordinates within 500 meters of a human influenced grid cell computed separately for each source area
99_Heatmaps/Heatmap_Steps_FloodLevel.tif:- Values: Traversal frequency calculated from simulated dispersers across all source areas
99_Heatmaps/Heatmap_Steps_SoufceArea_FloodLevel.tif- Values: Traversal frequency calculated from simulated dispersers separately for each source area
99_Simulations/FloodLevel_SourceArea_Replicate.rds- x: Simulated longitude
- y: Simulated latitude
- absta_: absolute turning angle (in radians)
- ta_: relative turning angle (in radians)
- sl_: step length (in meters)
- Timestamp: POSIXct timestamp
- BoundaryHit: Logical indicator if the simulated individual has hit a map boundary or not
- inactive: Logical indicator if the step is during wild dogs' active (1) or inactive (0) phase
- TrackID: Identifier of the simulated track
99_BetweennessGlobal.tif- Values: Betweenness across all source areas
99_BetweennessLocal.tif- Values: Betweenness for each source area separately
99_HeatmapsGlobal.tif- Values: Betweenness across all source areas
99_HeatmapsLocal.tif- Values: Heatmaps for each source area separately
Sharing/Access information
Links to other publicly accessible locations of the data:
- Description of the spatial data preparation: Hofmann et al., 2021
- Description of the dispersal model: Hofmann et al., 2023
