Simulation looking at the impact of using fishery-dependent data in SDM and forecasting distributions under future climate change
Code authors: Melissa Karp, Stephanie Brodie, James Smith, Owen Liu, Kate Richerson, Becca Selden
Relevant paper: Karp et al. Projecting species distributions using fishery-dependent data.
File Descriptions:
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SimulatedWorld_ROMS_FishDep_Final_updatedPres.R: function to simulate species presence and abundance in time and space with respect to environmental habitat preferences, and the different fishery-dependent sampling scenarios.
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ModelComparison_FishSuitability_v_10_28_21.R: this code uses the function above to generate data, then builds GAM and BRT models, and makes predictions into the future 2011-2100
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Fitting_BRTs.R: code to fit the boosted regression tree models to the simulated data with just environmental covariates. This is called in 2 above.
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Fitting_GAMs.R: code to fit the generalized additive models to the simulated data with just environmental covariates. This is called in 2 above.
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BRT_spacetime.R: code to fit the boosted regression tree models to the simulated data with environmental and space and time covariates. This is called in 2 above.
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GAM_SpaceTime_Config3.R: code to fit the generalized additive models to the simulated data with environmental and space and time covariates. This is called in 2 above.
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calculate_kl_ks_hd_and_cohens_d_All.R: code to calculate the climate bias and climate novelty (e.g., Hellinger Distance and Cohen's d)
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DistancetoPort.R: This code calculates the distance from every cell in the ROMS extent to 5 different fishing ports along the US West Coast in CA, OR, and WA.