Irving_et_al_arroyo_toad

Species distribution model assessing impact of future climate-induced flow alteration on Arroyo Toad

input data downloads

all data

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/00_all_rasters_200m.tif

base raster

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/Elev.tif

delta FFM current rasters

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/Delta_Rasters.zip

delta FFM future scenario rasters -

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/futurerasters.zip

spatial data

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/SpatialData.zip

protected land and critical habitat

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/ProtectedCriticalLand.zip

data products

probability of occurrence (current)

https://sccwrp.maps.arcgis.com/home/item.html?id=0b84fcfdab284fa2bd1e1b5bb4cdb617

binary presence/absence

https://sccwrp.maps.arcgis.com/home/item.html?id=fb8fa42dde5e4549b6166871b61f03d2

probability of occurrence under future scenarios

https://sccwrp.maps.arcgis.com/home/item.html?id=ad4bdfc4cf1541d3956c2357bb16eaf4

Code

Functions

https://ftp.sccwrp.org/pub/download/PROJECTS/SDHydroVulnerabilityArroyoToad/Functions.R

Script 0: 00_gridded_200m_data_format.R

formatting environmental/physical data script to raster format

input data needed

landscape

Elev.tif Slope.tif AccFlow.tif MRVBF.tif VRM3.tif VRM18.tif

soil

AWC_r.tif PercentClay.tif PercentSand.tif PercentSilt.tif

remote sensing

TC_092014_RB9.tif TC_042014_RB9.tif

##FFM "Delta_Rasters/DS_Mag_50_Delta.tif" "Delta_Rasters/FA_Mag_Delta.tif" "Delta_Rasters/Peak_2_Delta.tif" "Delta_Rasters/Peak_5_Delta.tif" "Delta_Rasters/Peak_10_Delta.tif" "Delta_Rasters/Q99_Delta.tif" "Delta_Rasters/SP_Mag_Delta.tif" "Delta_Rasters/Wet_BFL_Mag_10_Delta.tif" "Delta_Rasters/Wet_BFL_Mag_50_Delta.tif"

writes stacked raster will all environmental data

00_all_rasters_200m.tif 00_final_raster_layer_names.RData ## and raster names

Script 1: 01_auto_gridded_model_no_climate.R

Random forest SDM. Automates - 1 RF model 2 Pseudo absences 3 Partial Plots 4 Proximity 5 Validation 6 Model coefficients 7 Relative importance

Runs 10 x models and saves files separately

input data needed

00_all_rasters_200m.tif ## environmental data ToadsObs_SnapToRaster.shp ## Toad observation data (can not share)

Saves raster stack as dataframe

01_RB9_grdded_data.RData

Script 2: 02_current_predictions.R

Predicts toad distribution under current conditions and combines probabilities & importance over models

Input data needed -

01_RB9_grdded_data.RData ## df of raster environmental data 00_final_raster_layer_names.RData ## raster layer names NHD_reaches_RB9_castreamclassification.shp ## NHD reaches for staudy area Elev.tif

Also, uses baseline scenario as current conditions

Saves

02_relative_importance_mean.csv ## mean relative importance 02_validation_values.csv ## mean validation values 02_Av_Probs_Current_RB9.csv ## mean probability of occurrence df 02_Arroyo_Toad_Prob_Occurrence_RB9.shp ## mean probability of occurrence spatial 02_Arroyo_Toad_Prob_Occurrence_RB9.tif ## mean probability of occurrence raster

Script 3: 03_future predictions.R

Predicts toad under future scenarios and combines probabilities over models

Input data needed future scenarios

Saves

03_Av_Probs_Future_RB9_extremes_New.csv ## probabilities for all scenarios

Script 4: 04_physcial_factors_of_CURRENT_predictions.R

Calulates occurrences predicted on protected land and critical habitat

Input data - All predictions from Script 02 02_Av_Probs_Current_RB9.csv ## mean probability of occurrence df 02_Arroyo_Toad_Prob_Occurrence_RB9.shp ## mean probability of occurrence spatial 02_Arroyo_Toad_Prob_Occurrence_RB9.tif ## mean probability of occurrence raster

Protected Land/Habitat

Pendleton_95.shp ## Camp pendleton CPAD_2023a_Holdings.shp ## Protected Land ArroyoToadFinalCriticalHabitatUSFWSds129.shp ## Critical habitat

spatial data

SD_RB9_boundary.shp ## study area polygon Ca_State_poly.shp ## State of Ca SMCSheds2009.shp ## Watersheds of So Cal

Saves and maps

04_prob_occ_current.csv ## probability of occurrence with observed presence/absence

Analysis not really needed as also counted in script 06 and mapped in script 05

Script 5: 05_figures_current_analysis.R

Making figures for the manuscript for the current analysis

Input data -

Elev.tif ## base raster 02_obs_probs_current.RData ## observations 02_Arroyo_Toad_Prob_Occurrence_RB9.shp ## Probabilities

spatial data

SD_RB9_boundary.shp ## study area polygon Ca_State_poly.shp ## State of Ca SMCSheds2009.shp ## Watersheds of So Cal Counties.shp ## counties CA

Figure 3 created

05_probability_of_occurence_current ## map of probabilities (Figure 3) 05_presences_on_critical_habitat_current_with_legend.jpg ## within critical habitat (Figure 3) 05_presences_on_protected_land_current_with_legend.jpg ## within protected land

Script 6: 06_physcial_factors_of_FUTURE_predictions.R

Evaluating ranges within each scenario for differences in - elevation (t tests, counts and %) critical habitat (counts and %) protected land (counts and %) FFM (t tests)

Input data

02_Av_Probs_Current_RB9.csv # probabilities 03_future_ffms.csv # FFM scenario data 03_Av_Probs_Future_RB9_extremes_New.csv # future probability of occurrence all_presAbs_env_data.RData # observations input for model Elev.tif # elevation

Figures created

06_Predictions_FFM.jpg ## FFM figure (Figure 5)

Script 7: 07_baseline_future_comparison.R

Comparison of baseline/current and future scenarios Evaluate - differences in probability Range size range overlap

Input data

03_Av_Probs_Future_RB9_extremes_New.csv ## future probability of occurrence Elev.tif ## base raster

Figures created

07_change_in_probability_future_scens.jpg # change in probability (Figure 4)

Script 8: 08_checking_data.R

A place to check data

Script 9: citations.R

citiations for packages used in analysis

Metadata links

Observation data Can not share at this time

Environmental data for model build

FFM for future scenarios

Spatial Data