DOI: 10.5061/dryad.t1g1jwt2p
title: Reconstructing the historical fauna of a large continental island: a multispecies reintroduction risk analysis

description: 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 at-risk 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.
description: 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 <i>FindMultiplier.m</i> 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 <i>CreateModelsForAll.m</i>.
 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
 <i>FindMultiplier.m</i>), IM’X’.xlsx, where
 <i>‘X’∈{1,…,7}</i>,
 and Rvector.xlsx. This script also uses the functions and scripts
 <i>ExtractCoexistenceConstraints.m</i>,
 <i>Coexistence_constraint.m</i>,
 <i>check_stability.m</i>, and
 <i>GrowthRateConstraint.m</i>, 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
 <i>ModelEnsembleIM’X’.mat</i>, where
 <i>‘X’∈{1,…,7}</i>,
 which contains the ensemble of models (and their associated parameters)
 for interaction matrix <i>‘X’</i>. Finally, we use the ensemble of
 models to simulate the reintroduction strategies. This is done using the
 script, <i>Spatial_reintroduction_simulation.m</i>. This
 script uses <i>ModelEnsembleIM’X’.mat</i>, where
 <i>‘X’∈{1,…,7}</i>,
 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
 <i>‘Y’∈{1,…,23}</i>.
 This script then uses <i>species_DE_spatial.m</i> to simulate
 the system. The output of
 <i>Spatial_reintroduction_simulation.m</i> saves
 <i>OutcomesSetBIGIM’X’.mat</i> (the set of species which
 failed in a given simulation for a given translocation alternative), and
 <i>SimulationSetBIGIM’X’.mat</i> (the timeseries abundance and
 time data for every simulation), where
 <i>‘X’∈{1,…,7}</i>. 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: <i>VisuallyCmpareMatrices.m</i> Fig 3: <i>Figure_key_timeseries_SUBSET.m</i> Fig 4: <i>Figure_4.m</i> Fig 5: <i>ExplainWhyExtinct_quantiles.m</i> Fig 6: <i>Figure_checkerbard_particular_species.m</i> Supplementary Figures Supplementary Infrmation 2: <i>SUPPFIG_Figure_key_timeseries_AllSupps.m</i> Supplementary Infrmation 3: <i>SUPPFIG_matrix_vilins.m</i> Supplementary Infrmation 4: <i>SUPPFIG_Figure_checkerbards_bars.m</i> Supplementary Infrmation 5: <i>ExplainWhyExtinct_quantiles.m</i> Input parameters are specified at the beginning of major scripts/functions. If you have questions, please contact <b>kpeter10@umd.edu or michael.bode@qut.edu.au</b> 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 <i>FindMultiplier.m</i> 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 <i>CreateModelsForAll.m</i>. 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 <i>FindMultiplier.m</i>), IM’X’.xlsx, where <i>‘X’∈{1,…,7}</i>, and Rvector.xlsx. This script also uses the functions and scripts <i>ExtractCoexistenceConstraints.m</i>, <i>Coexistence_constraint.m</i>, <i>check_stability.m</i>, and <i>GrowthRateConstraint.m</i>, 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 <i>ModelEnsembleIM’X’.mat</i>, where <i>‘X’∈{1,…,7}</i>, which contains the ensemble of models (and their associated parameters) for interaction matrix <i>‘X’</i>. Finally, we use the ensemble of models to simulate the reintroduction strategies. This is done using the script, <i>Spatial_reintroduction_simulation.m</i>. This script uses <i>ModelEnsembleIM’X’.mat</i>, where <i>‘X’∈{1,…,7}</i>, 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 <i>‘Y’∈{1,…,23}</i>. This script then uses <i>species_DE_spatial.m</i> to simulate the system. The output of <i>Spatial_reintroduction_simulation.m</i> saves <i>OutcomesSetBIGIM’X’.mat</i> (the set of species which failed in a given simulation for a given translocation alternative), and <i>SimulationSetBIGIM’X’.mat</i> (the timeseries abundance and time data for every simulation), where <i>‘X’∈{1,…,7}</i>. 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: <i>VisuallyCmpareMatrices.m</i> Fig 3: <i>Figure_key_timeseries_SUBSET.m</i> Fig 4: <i>Figure_4.m</i> Fig 5: <i>ExplainWhyExtinct_quantiles.m</i> Fig 6: <i>Figure_checkerbard_particular_species.m</i> Supplementary Figures Supplementary Infrmation 2: <i>SUPPFIG_Figure_key_timeseries_AllSupps.m</i> Supplementary Infrmation 3: <i>SUPPFIG_matrix_vilins.m</i> Supplementary Infrmation 4: <i>SUPPFIG_Figure_checkerbards_bars.m</i> Supplementary Infrmation 5: <i>ExplainWhyExtinct_quantiles.m</i> Input parameters are specified at the beginning of major scripts/functions. If you have questions, please contact <b>kpeter10@sesync.org</b>,<b> cailan.jeynessmith@hdr.qut.edu.au</b>,<b> </b>or<b> michael.bode@qut.edu.au</b>
description: We provide a summary of all
 functions with their inputs, dependent functions/data, and
 outputs. To
 generate models: FindMultiplier.m Determines the
 diagnal multiplier ‘<i>FinalMult</i>’ 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,
 <b>Cexistence_constraints_list.mat</b> 
 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 <i>PLOT_checker_SameMat = 1</i>
 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