Reconstructing the historical fauna of a large continental island: a multispecies reintroduction risk analysis
Data files
Jul 01, 2021 version files 14.49 MB

Alt1.xlsx

Alt10.xlsx

Alt11.xlsx

Alt12.xlsx

Alt13.xlsx

Alt14.xlsx

Alt15.xlsx

Alt16.xlsx

Alt17.xlsx

Alt18.xlsx

Alt19.xlsx

Alt2.xlsx

Alt20.xlsx

Alt21.xlsx

Alt22.xlsx

Alt23.xlsx

Alt24.xlsx

Alt25.xlsx

Alt26.xlsx

Alt3.xlsx

Alt4.xlsx

Alt5.xlsx

Alt6.xlsx

Alt7.xlsx

Alt8.xlsx

Alt9.xlsx

AlternativeNames_23.xlsx

AlternativeNames.xlsx

Coexistence_constraints_CS_SC.xlsx

Coexistence_constraints_list.mat

DHINames_medium.xlsx

DHINames_short.xlsx

DHINames.xlsx

DispLowerBound.xlsx

DispTimeDelay.xlsx

DispUpperBound.xlsx

IM1.xlsx

IM2.xlsx

IM2edit.xlsx

IM3.xlsx

IM4.xlsx

IM5.xlsx

IM6.xlsx

IM7.xlsx

ModelEnsembleIM1.mat

ModelEnsembleIM2.mat

ModelEnsembleIM3.mat

ModelEnsembleIM4.mat

ModelEnsembleIM5.mat

ModelEnsembleIM6.mat

ModelEnsembleIM7.mat

Multipliers.mat

OUTCOMES_BLOCK.mat

OutcomesSetBIGIM1.mat

OutcomesSetBIGIM2.mat

OutcomesSetBIGIM3.mat

OutcomesSetBIGIM4.mat

OutcomesSetBIGIM5.mat

OutcomesSetBIGIM6.mat

OutcomesSetBIGIM7.mat

Rvector.xlsx
Abstract
1. Reintroduction projects, which are an important tool in threatened species conservation, are becoming more complex, often involving the translocation of multiple species. Ecological theory predicts that the sequence and timing of reintroductions will play an important role in their success or failure. Following the removal of sheep, goats and feral cats, the Western Australian government is sequentially reintroducing 13 native fauna species to restore the globally important natural and cultural values of Dirk Hartog Island.
2. We use ensembles of ecosystem models to compare 23 alternative reintroduction strategies on Dirk Hartog Island, in Western Australia. The reintroduction strategies differ in the order, timing, and location of releases on the island. Expert elicitation informed the model structure, allowing for use of different presumed species interaction networks which explicitly incorporated uncertainty in ecosystem dynamics.
3. Our model ensembles predict that almost all of the species (~12.5 out of 13, on average) will successfully establish in the ecosystem studied, regardless of which reintroduction strategy is undertaken. The project can therefore proceed with greater confidence and flexibility regarding the reintroduction strategy. However, the identity of the atrisk species varies between strategies, and depends on the structure of the species interaction network, which is quite uncertain. The model ensembles also offer insights into why some species fail to establish on Dirk Hartog Island, predicting that most unsuccessful reintroductions will be the result of competitive interactions with extant species.
4. Synthesis and applications: Our model ensembles allow for the comparison of outcomes between reintroduction strategies and between different species interaction networks. This framework allows for inclusion of high uncertainty in dynamics. Finally, an ensemble modelling approach also creates a foundation for formal adaptive management as reintroduction projects proceed.
Methods
This is the code and data that generates the models and figures used in the manuscript and supplementary documents.
We begin by generating an ensemble of models for each transition matrix, which are associated with the same dynamic behaviours determined through expert elicitation. To do this we first determine a scalar multiple for each transition matrix, that will be multiplied along the diagonal of the coefficient matrix and ensure that one thousand models are generated in approximately 24 hours. This is done using the Matlab script FindMultiplier.m in which a user selects the transition matrix to investigate (and its type: 1 if all signs are defined, 2 if the sign is uncertain).
The ensemble of models is then generated using the Matlab script CreateModelsForAll.m. This script requires the user to define the transition matrix to investigate and the number of models which we want to generate. The user must also change the save name at the bottom of the script. This script uses Multipliers.mat (generated by the user by running FindMultiplier.m), IM’X’.xlsx, where ‘X’∈{1,…,7}, and Rvector.xlsx. This script also uses the functions and scripts ExtractCoexistenceConstraints.m, Coexistence_constraint.m, check_stability.m, and GrowthRateConstraint.m, which are used to determine if models match the elicited dynamic constraints. We will discuss these constraints in more detail later. Finally this script outputs ModelEnsembleIM’X’.mat, where ‘X’∈{1,…,7}, which contains the ensemble of models (and their associated parameters) for interaction matrix ‘X’.
Finally, we use the ensemble of models to simulate the reintroduction strategies. This is done using the script, Spatial_reintroduction_simulation.m. This script uses ModelEnsembleIM’X’.mat, where ‘X’∈{1,…,7}, along with elicited dispersal data in DispTimeDelay.xlsx, DispUpperBound.xlsx, DispLowerBound.xlsx. The translocation strategies are loaded in using AlternativeNames.xlsx, and Alt’Y’.xlsx, where ‘Y’∈{1,…,23}. This script then uses species_DE_spatial.m to simulate the system. The output of Spatial_reintroduction_simulation.m saves OutcomesSetBIGIM’X’.mat (the set of species which failed in a given simulation for a given translocation alternative), and SimulationSetBIGIM’X’.mat (the timeseries abundance and time data for every simulation), where ‘X’∈{1,…,7}.
To then analyse these simulations and generate the figures used in the manuscript and supplementary materials we use the following scripts and functions:
 Manuscript Figures:
 Fig 2: VisuallyCmpareMatrices.m
 Fig 3: Figure_key_timeseries_SUBSET.m
 Fig 4: Figure_4.m
 Fig 5: ExplainWhyExtinct_quantiles.m
 Fig 6: Figure_checkerbard_particular_species.m
 Supplementary Figures
 Supplementary Infrmation 2: SUPPFIG_Figure_key_timeseries_AllSupps.m
 Supplementary Infrmation 3: SUPPFIG_matrix_vilins.m
 Supplementary Infrmation 4: SUPPFIG_Figure_checkerbards_bars.m
 Supplementary Infrmation 5: ExplainWhyExtinct_quantiles.m
Input parameters are specified at the beginning of major scripts/functions.
If you have questions, please contact kpeter10@umd.edu or michael.bode@qut.edu.au
This is the code and data that generates the models and figures used in the manuscript and supplementary documents.
We begin by generating an ensemble of models for each transition matrix, which are associated with the same dynamic behaviours determined through expert elicitation. To do this we first determine a scalar multiple for each transition matrix, that will be multiplied along the diagonal of the coefficient matrix and ensure that one thousand models are generated in approximately 24 hours. This is done using the Matlab script FindMultiplier.m in which a user selects the transition matrix to investigate (and its type: 1 if all signs are defined, 2 if the sign is uncertain).
The ensemble of models is then generated using the Matlab script CreateModelsForAll.m. This script requires the user to define the transition matrix to investigate and the number of models which we want to generate. The user must also change the save name at the bottom of the script. This script uses Multipliers.mat (generated by the user by running FindMultiplier.m), IM’X’.xlsx, where ‘X’∈{1,…,7}, and Rvector.xlsx. This script also uses the functions and scripts ExtractCoexistenceConstraints.m, Coexistence_constraint.m, check_stability.m, and GrowthRateConstraint.m, which are used to determine if models match the elicited dynamic constraints. We will discuss these constraints in more detail later. Finally this script outputs ModelEnsembleIM’X’.mat, where ‘X’∈{1,…,7}, which contains the ensemble of models (and their associated parameters) for interaction matrix ‘X’.
Finally, we use the ensemble of models to simulate the reintroduction strategies. This is done using the script, Spatial_reintroduction_simulation.m. This script uses ModelEnsembleIM’X’.mat, where ‘X’∈{1,…,7}, along with elicited dispersal data in DispTimeDelay.xlsx, DispUpperBound.xlsx, DispLowerBound.xlsx. The translocation strategies are loaded in using AlternativeNames.xlsx, and Alt’Y’.xlsx, where ‘Y’∈{1,…,23}. This script then uses species_DE_spatial.m to simulate the system. The output of Spatial_reintroduction_simulation.m saves OutcomesSetBIGIM’X’.mat (the set of species which failed in a given simulation for a given translocation alternative), and SimulationSetBIGIM’X’.mat (the timeseries abundance and time data for every simulation), where ‘X’∈{1,…,7}.
To then analyse these simulations and generate the figures used in the manuscript and supplementary materials we use the following scripts and functions:
 Manuscript Figures:
 Fig 2: VisuallyCmpareMatrices.m
 Fig 3: Figure_key_timeseries_SUBSET.m
 Fig 4: Figure_4.m
 Fig 5: ExplainWhyExtinct_quantiles.m
 Fig 6: Figure_checkerbard_particular_species.m
 Supplementary Figures
 Supplementary Infrmation 2: SUPPFIG_Figure_key_timeseries_AllSupps.m
 Supplementary Infrmation 3: SUPPFIG_matrix_vilins.m
 Supplementary Infrmation 4: SUPPFIG_Figure_checkerbards_bars.m
 Supplementary Infrmation 5: ExplainWhyExtinct_quantiles.m
Input parameters are specified at the beginning of major scripts/functions.
If you have questions, please contact kpeter10@sesync.org, cailan.jeynessmith@hdr.qut.edu.au, or michael.bode@qut.edu.au
Usage notes
We provide a summary of all functions with their inputs, dependent functions/data, and outputs.
To generate models:
 FindMultiplier.m
 Determines the diagnal multiplier ‘FinalMult’ fr a given transition matrix, IM’X’.xlsx, to generate 1000 samples in approximately 24 hours when used in CreateModelsForAll.m.
 Inputs: Select a transitin matrix, IM’X’.xlsx, to investigate (and its type: 1 if all signs are defined, 2 if there is sign uncertainty),
 Lads Rvector.xlsx, IM’X’.xlsx
 Runs ExtractCexistenceConstraint.m, Coexistence_constraint.m, check_stability.m, GrowthRateConstraint.m
 CreateModelsForAll.m
 Finds mdels which meet dynamic constraints for a selected transition matrix.
 Inputs: select transitin matrix, IM’X’.xlsx, to investigate (and its type: 1 if all signs are defined, 2 if there is sign is uncertain) and number of coexisting matrices which we want. Change output save name at the bottom of the file
 Lads Multipliers.mat, IM’X’.xlsx, Rvector.xlsx, Cexistence_constraints_list.mat
 Runs Cexistence_constraint.m, check_stability.m, GrowthRateConstraint.m, ExtractCoexistenceConstraints.m
 Saves MdelEnsembleIM’X’.mat
 Spatial_reintroduction_simulation.m
 This generates simulatins of the ecosystems which are then used to generate figures
 Lads in DispTimeDelay.xlsx, DispUpperBound.xlsx, DispLowerBound.xlsx, AlternativeNames.xlsx, AltX.xlsx, ModelEnsembleIMX.mat,
 Runs species_DE_spatial.m
 Saves OutcmesSetBIGIMX.mat, SimulationSetBIGIMX.mat
 ExtractCoexistenceConstraints.m
 Generates a matrix with abundance cnstraints
 Lads Coexistence_constraints_CS_SC.xlsx
 Saves Cexistence_constraints_list.mat
 Check_stability.m
 Checks the stability f a system
 Is run by CreateMdelsForAll.m and Coexistence_constraint.m
 Coexistence_constraint.m
 Checks if a subset f species can coexist together
 Runs Check_stability.m
 Is run by CreateMdelsForAll.m
 GrowthRateConstraint.m
 Checks if species have grwth rates which exist within the bounds identified by Rvector.xlsx
 Is run by CreateMdelsForAll.m
 species_DE_spatial.m
 Runs a simulatin of a translocation strategy, taking into account spatial factors such as translocation
 is run by Spatial_reintrduction_simulation.m
Manuscript Figures:
 Fig 2: VisuallyCompareMatrices.m
 Input: Change ‘i’ t range from 1:6 to 1:7 (line 53) to obtain all Figures found in manuscript
 Lads DHINames.xlsx, DHINames_shrt.xlsx, DHINames_medium.xlsx, IM’X’.mat,
 Fig 3: Figure_key_timeseries_SUBSET.m
 Lads AlternativeNames_23.xlsx, SimulatinSetBIGIMX.mat, OutcomesSetBIGIMX.mat, DHINames.xlsx
 Calls Make_TIFF.m, tight_subplt.m
 Saves Reintr_timeseries_LowRes.tiff, Reintro_timeseries.tiff
 Fig 4: Figure_4.m
 Lads AlternativeNames_23.xlsx, OutcomesSetBIGIMX.mat, DHINames.xlsx, OUTCOMES_BLOCK.mat
 Calls TranslcationAlternativesNames.m, Make_TIFF.m
 Saves Figure_4_LwRes.tiff, Figure_4.tiff
 Fig 5: ExplainWhyExtinct_quantiles.m
 Lads ModelEnsembleIMX.mat, OutcomesSetBIGIMX.mat, SimulationSetBIGIMX.mat, DHINames.xlsx, DHINames_short.xlsx, DHINames_medium.xlsx, AlternativeNames_23.xlsx
 Calls Make_TIFF.m
 Saves ‘SpeciesName’_failure_ExpertMatrix_’X’.tiff
 Fig 6: Figure_checkerboard_particular_species.m
 Lads AlternativeNames_23.xlsx, OutcomesSetBIGIMX.mat, DHINames.xlsx, DHINames_short.xlsx, DHINames_medium.xlsx
 Calls TranslcationAlternativesNames.m, Make_TIFF.m, tight_subplot.m
 Figure_6_LwRes.tiff, Figure_6.tiff
 ManuscriptFigure_checkerboards_bars_consensus.m:
 Input: set variable PLOT_checker_SameMat = 1
 Lads AlternativeNames_23.xlsx, OutcomesSetBIGIM’X’.mat, DHINames_short.xlsx, DHINames_medium.xlsx
 Saves OUTCOMES_BLOCK.mat which is used in Figure_4.m
 TranslocationAlternativesNames.m:
 Input: ALTNAME (defined in functins which call it)
 Lads ALTNAME.xlsx
 Defines the names used in figures
Supplementary Figures
 Supplementary Information 2: SUPPFIG_Figure_key_timeseries_AllSupps.m
 Lads AlternativeNames_23.xlsx, SimulationSetBIGIMX.mat, OutcomesSetBIGIMX.mat, DHINames.xlsx, DHINames_short.xlsx
 Calls Make_TIFF.m, tight_subplt.m
 Saves SuppFig_Reintr_timeseries_X.tiff
 Supplementary Information 3: SUPPFIG_matrix_violins.m
 Lads AlternativeNames_23.xlsx, OutcomesSetBIGIMX.mat, DHINames.xlsx,
 Calls Make_TIFF.m, TranslcationAlternativesNames.m
 Saves SuppFig_vilin_X.tiff
 Supplementary Information 4: SUPPFIG_Figure_checkerboards_bars.m
 Lads AlternativeNames_23.xlsx, OutcomesSetBIGIMX.mat, DHINames.xlsx, DHINames_short.xlsx, DHINames_medium.xlsx
 INPUT: PLOT_checker_SameSpp, PLOT_checker_SameAlt, PLOT_checker_SameMat are binary variables at the tp of the file, which are set to 1 if the desired figure is to be generated
 Calls TranslcationAlternativesNames.m, Make_TIFF.m, tight_subplot.m
 Saves SuppFig_MatrixAlternativeChecquerbard.tiff, SuppFig_MatrixSpeciesChecquerboard.tiff, SuppFig_AlternativeSpeciesChecquerboard.tiff
 Supplementary Information 5: ExplainWhyExtinct_quantiles.m
 Lads ModelEnsembleIMX.mat, OutcomesSetBIGIMX.mat, SimulationSetBIGIMX.mat, DHINames.xlsx, DHINames_short.xlsx, DHINames_medium.xlsx, AlternativeNames_23.xlsx
 Calls Make_TIFF.m
 Saves ‘SpeciesName’_failure_ExpertMatrix_’X’.tiff
External Function:
 tight_subplot.m:
 generates subplts with minimised whitespace.
 Dwnloaded from MathWorks