for peer review · for peer review 23 non-native plant species are recorded in the leb, with many...

For Peer Review

Priority threat management of non-native plants to

maintain ecosystem integrity across heterogeneous landscapes

Journal: Journal of Applied Ecology

Manuscript ID: JAPPL-2014-01063.R2

Manuscript Type: Standard Paper

Date Submitted by the Author: n/a

Complete List of Authors: Firn, Jennifer; CSIRO, Land and Water; Queensland University of

Technology, Faculty of Science and Engineering, School of Earth, Environmental and Biological Sciences Martin, Tara; CSIRO , Land and Water; ARC Centre of Excellence Decisions, the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science University of Queensland, Chades, Iadine; CSIRO, Land and Water; ARC Centre of Excellence Decisions, the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science University of Queensland, Walters, Belinda; CSIRO, Land and Water Hayes, John; Queensland University of Technology, Faculty of Science and Engineering, School of Earth, Environmental and Biological Sciences Nicol, Sam; CSIRO, Land and Water; ARC Centre of Excellence Decisions,

the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science University of Queensland, Carwardine, Josie; CSIRO, Land and Water; ARC Centre of Excellence Decisions, the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science University of Queensland,

Key-words: weed control, invasive plants, cost-effectiveness, threatened flora and fauna, biodiversity, cost-benefit analyses, stochastic dynamic programming

Journal of Applied Ecology

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Priority threat management of non-native plants to maintain ecosystem integrity across 1

heterogeneous landscapes 2

Jennifer Firn1,2, Tara G. Martin1,3, Iadine Chadès1,3, Belinda Walters1, John Hayes2, Sam 3

Nicol1,3, Josie Carwardine1,3 4

1 CSIRO Land and Water, Ecosciences Precinct Boggo Road, Brisbane, Australia 5

2Queensland University of Technology, School of Earth, Environmental and Biological 6

Sciences, Brisbane Australia 7

3ARC Centre of Excellence for Environmental Decisions, the NERP Environmental Decisions 8

Hub, Centre for Biodiversity & Conservation Science, University of Queensland, Brisbane Qld 9

4072, Australia 10

Summary: 11

1. Invasive non-native plants have negatively impacted on biodiversity and ecosystem 12

functions worldwide. Because of the large number of species, their wide distributions and 13

varying degrees of impact, we need a more effective method for prioritizing control 14

strategies for cost-effective investment across heterogeneous landscapes. 15

2. Here we develop a prioritization framework that synthesizes scientific data, elicits 16

knowledge from experts and stakeholders to identify control strategies, and appraises the 17

cost-effectiveness of strategies. Our objective was to identify the most cost-effective 18

strategies for reducing the total area dominated by high impact non-native plants in the 19

Lake Eyre Basin (LEB). 20

3. We use a case study of the ~120 million ha LEB that comprises some of the most 21

distinctive Australian landscapes, including Uluru-Kata Tjuta National Park. More than 240 22

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non-native plant species are recorded in the LEB, with many predicted to spread, but there 23

are insufficient resources to control all species. 24

4. LEB experts identified 12 strategies to control, contain or eradicate non-native species 25

over the next 50 years. The total cost of the proposed LEB strategies was estimated at 26

AU$1.7 billion, an average of AU$34 million annually. Implementation of these strategies is 27

estimated to reduce non-native plant dominance by 17 million ha – there would be a 32% 28

reduction in the likely area dominated by non-native plants within 50 years if these 29

strategies were implemented. 30

5. The three most cost-effective strategies were controlling Parkinsonia aculeata, Ziziphus 31

mauritiana and Prosopis spp. These three strategies combined were estimated to cost only 32

0.01% of total cost of all the strategies, but would provide 20% of the total benefits. Over 50 33

years, cost-effective spending of AU$2.3 million could eradicate all non-native plant species 34

from the only threatened ecological community within the LEB, the Great Artesian Basin 35

discharge springs. 36

6. Synthesis and applications. Our framework, based on a case study of the ~120 million ha 37

Lake Eyre Basin in Australia, provides a rationale for financially efficient investment in non-38

native plant management and reveals combinations of strategies that are optimal for 39

different budgets. It also highlights knowledge gaps and incidental findings that could 40

improve effective management of non-native plants; for example, addressing the reliability 41

of species distribution data and prevalence of information sharing across states and regions. 42

43

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Introduction 44

The regular movement of people and commerce have breached biogeographic barriers, 45

initiating an unprecedented period of global species migration and homogenization (Elton, 46

1958; Crosby, 1986; Ceballos et al., 2010). Investment in non-native species management is 47

occurring worldwide in an effort to stem the negative impacts on biodiversity, ecosystem 48

function and agricultural production (Levine & D'Antonio, 2003; Sinden et al., 2004; 49

Pimental et al., 2005). The estimated annual expenditure on non-native plant control in 50

Australia is AU $1.5 billion (Sinden et al., 2004), and US $9.6 billion in the United States 51

(Pimental et al., 2005). It is often unclear which management strategies represent the best 52

investments because of the challenge of measuring the impact of non-native species and 53

prioritizing strategies accordingly (Vila et al., 2011). There is also uncertainty around the 54

amount of funding required to manage priority non-native species and how best to spend 55

these funds to minimize spread and establishment. 56

Despite the prolific spread and dominance of many non-native plant species, there is mixed 57

evidence that non-native plants have caused direct extinctions of native species (Davis et al., 58

2011). However, there is substantial evidence that non-native plants can transform 59

ecosystem processes, such as water and nutrient cycling, fire frequency, and sediment 60

transport (D'Antonio & Hobbie, 2005; MacDougall & Turkington, 2005; Simberloff, 2006). 61

Combined with other disturbances such as tree clearing, grazing, irrigation, high fire 62

frequency, soil disturbance or fertilization, non-native plant species are often better able to 63

profit than native species and become dominant (Firn et al., 2008; Davies et al., 2009). 64

Under these multiple stressors, native plant biodiversity often declines (MacDougall et al., 65

2013) and as a result, key ecosystem functions are altered (Vila et al., 2010; Vila et al., 66


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2011). Once established, non-native species can change the structure of ecosystems, such as 67

the conversion of grassland to woodland (Figure 1). 68

In many regions, prompt management action is hampered by a lack of empirical data on the 69

impacts of threats and the responses of species and ecosystems to management strategies 70

to address threats. A growing body of research investigates methods for undertaking 71

conservation management appraisal and prioritization using the knowledge of experts and 72

stakeholders to complement formal scientific data (Burgman et al., 2011; Martin et al., 73

2012b). Expert information has been used to evaluate the cost-effectiveness of a range of 74

strategies to assist decision making for saving threatened species (Possingham et al., 2002; 75

Carwardine et al., 2012; Pannell et al., 2012; Chades et al., 2014; Firn et al., In press). Given 76

the urgency of many conservation issues and the difficulty of managing threats including the 77

invasion of non-native plants, evidence suggests that in many cases it is better to make 78

decisions using expert knowledge, rather than to take no action due to insufficient data 79

(Martin et al., 2012b). 80

These priority threat management approaches evaluate the alternative cost-effectiveness of 81

management strategies, where the expected benefits of each strategy (not measured in 82

dollar terms) are divided by the expected costs (Cullen et al., 2005). The potential benefits 83

of strategies can be measured as the improvement in species habitat protected (Carwardine 84

et al., 2008) or improvement in species persistence (Joseph et al., 2009; Carwardine et al., 85

2012). The costs are usually financial management costs and/or opportunity costs (Naidoo 86

et al., 2006). The expected benefits of the management strategies are the product of the 87

potential benefits multiplied by the feasibility of the management strategies, or the 88


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likelihood that the benefit will be achieved. Technical, social and political factors can all 89

influence the feasibility of a management strategy. 90

To date, such priority threat management approaches have not been used to assess the 91

management of threats posed by non-native plants on the integrity of native ecosystems. 92

This is particularly challenging because of the vast distributions of non-native plants and 93

aforementioned knowledge gaps making assessments difficult. Given the negative impacts 94

of non-native plants across large expanses of habitat there is much to be gained by ensuring 95

time and money are spent most efficiently and effectively. 96

Here we provide a rational and transparent framework for cost-effective investment in non-97

native plant species management across a heterogeneous landscape. Our objective is to 98

identify which control strategies are likely to be most cost-effective for reducing the total 99

area dominated by high impact non-native plants in each bioregion of the Lake Eyre Basin 100

(hereafter LEB), central Australia; where there are many non-native species (~240 species), 101

with varying impacts across the region, and limited resources make it impossible to invest in 102

managing them all. Our approach appraises cost-effectiveness of a set of possible 103

management strategies by drawing on empirical data and expert information to estimate 104

the expected benefits and costs of each strategy (Joseph et al., 2009; Carwardine et al., 105

2012; Pannell et al., 2012). While the strategies evaluated for controlling, containing and 106

eradicating non-native species are not new, we develop a unique approach for evaluating 107

their cost-effectiveness, and relative priority for reducing the impact of non-native plants in 108

the LEB over 50 years. 109

We specifically aim to: (i) develop a defensible non-native plant management prioritization 110

framework; (ii) apply our framework to assess a costed suite of management strategies 111


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using a simple but novel approach to estimate biodiversity benefits; (iii) quantify the likely 112

area of plant invasion within geographical subunits of the LEB and the area that can be 113

feasibly managed for non-native species and (iv) appraise the most cost-effective 114

management strategies depending on budgets. 115

Materials and methods 116

Case study 117

The Lake Eyre Basin (LEB) covers approximately 120 million ha of arid and semi-arid central 118

Australia, spanning one sixth of the Australian continent (Figure 2) and multiple states: 119

Queensland, South Australia, New South Wales and the Northern Territory. The LEB is the 120

fifth largest internally draining system in the world and one of the least degraded systems in 121

Australia. LEB habitats include internationally recognized wetlands such as the Ramsar listed 122

Coongie Lakes, grasslands such as the Astrebla Downs National Park and deserts such as the 123

Simpson Desert National Park. The LEB Great Artesian Basin (GAB) discharge springs 124

wetlands (Figure S1 in Supporting Information) are listed as endangered under the 125

Australian Commonwealth Environmental Protection and Biodiversity Conservation Act 126

1999. These permanent springs in an otherwise semi-arid to arid landscape with highly 127

variable rainfall support at least 13 endemic plant species and 65 endemic fauna species 128

(Fensham et al., 2007). 129

More than 240 non-native plants are recorded in the LEB, including 20 Weeds of National 130

Significance (WONS; the federal government classification for the most significant weeds in 131

Australia) (Thorp & Lynch, 2000; Australian Weeds Committee, 2012; CSIRO and QUT, 2013). 132

The current distributions of seven of these WONS are predominantly within the LEB 133


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including: Prosopis spp. (mesquite complex: Prosopis pallida (single-stemmed), and Prosopis 134

glandulosa, Prosopis juliflora, Prosopis velutina (multi-stemmed)), Parkinsonia aculeata 135

parkinsonia, Tamarix aphylla athel pine, Opuntia spp. and Cylindropuntia spp. (cacti 136

grouping, more than 14 spp.), Cryptostegia grandiflora rubber vine, Jatropha gossypifolia 137

bellyache bush, and Vachellia nilotica prickly acacia (Figure 1). 138

In addition, a number of high impact non-native plants have limited distributions within the 139

LEB but have the potential to spread in the higher rainfall (semi-arid) regions, e.g. Ziziphus 140

mauritiana chinee apple, and Bryophyllum spp. (mother of millions grouping: Bryophyllum 141

delagoense, Bryophyllum houghtonii and Bryophyllum pinnatum). 142

Buffel grass Cenchrus ciliaris L. has been introduced across the LEB for pasture production 143

and is the most widely distributed non-native plant species across the LEB (Marshall et al., 144

2011). Buffel grass has a “dual impact” in the LEB, being of economic value to many graziers 145

and one of the most serious threats to rangeland biodiversity (Martin et al., 2006; Friedel et 146

al., 2009; Pavey & Nano, 2009; Grice et al., 2012). 147

Data collection 148

We collated existing information on the distribution of 243 non-native plants that are 149

recorded within the LEB from the scientific literature and the Atlas of Living Australia (Atlas 150

of Living Australia website). Further information required for the analysis was gathered from 151

stakeholders and experts (hereafter ‘participants’) at a three-day workshop and follow-up 152

consultations using a structured elicitation approach. Participants during and in follow-up 153

consultations after the workshop included resource managers from local natural resource 154

management boards, non-government organizations, Aboriginal Land Council Members, 155


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land managers, indigenous rangers, park managers, graziers, scientists, state and territory 156

governments, and the Federal Department of Sustainability, Environment, Water, 157

Population and Communities, including several involved with the Lake Eyre Basin Scientific 158

Panel and Community Advisory Committee. Of the 33 experts contacted, 19 contributed in 159

some form, and 11 attended the workshop. Information on the three-day structured 160

elicitation process, biodiversity of concern and invasive plants recorded in the LEB were sent 161

to participants prior to the workshop and an independent facilitator ran the workshop. 162

The first step in the structured elicitation process was to identify a set of management 163

strategies for individual or groups of non-native species that have a significant impact in the 164

Basin. Twelve strategies were identified at the start of the workshop (Table S1). Participants 165

then estimated the potential benefit and cost of implementing each strategy. 166

i. Estimating benefits and costs of strategies 167

Participants provided the information required for the benefit metric by estimating the 168

expected proportion of invadable habitat in each bioregion that each non-native species (or 169

group) is likely to dominate (>30% coverage at a site) in 50 years: 170

• without implementation of any strategy; and 171

• with implementation of the strategy targeted to manage that non-native plant 172

species (or group). 173

For each of these scenarios participants gave their best guess, upper (most optimistic) and 174

lower (most pessimistic) bounds, and a level of confidence that the ‘true’ estimate lays 175

within this range (Martin et al., 2012b; McBride et al., 2012). Participants quantitatively 176

defined dominance and invadable habitat. Dominance was defined as a level of cover 177


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exceeding 30%. If a non-native plant was dominant at a site, participants agreed that the 178

site would be considered dramatically altered. The invadable habitat was assumed to be the 179

proportion of suitable habitat for the non-native species in each bioregion, which also 180

considers the likelihood of the success. 181

Participants agreed that the target for managing the threatened ecosystem GAB discharge 182

springs would be to remove all non-native plant species and prevent new introductions. The 183

benefits of the GAB discharge springs strategy was estimated for the entire LEB rather than 184

each bioregion. 185

Participants during the workshop and in follow-up consultations costed each non-native 186

plant control program action within each bioregion over 50 years, and estimated the annual 187

costs/ha of managing (eradicating, containing or controlling) each non-native plant at high 188

and low densities and provided realistic time frames for management over 50 years (e.g. 189

treatment every 2, 10, 20 or 25 years). In all cases the costs of undertaking each strategy i 190

by its component actions in each bioregion j were estimated by considering the costs of 191

previous and current management activities and spatial variants such as land tenure and 192

remoteness. The economic cost Cij was the cost in present day Australian dollars of activities 193

associated with strategy i in bioregion j over 50 years. 194

We used the best available data on the distributions of non-native plant species although 195

only occurrence data with coarse resolutions were available. The potential distributions of 196

most non-native plant species were obtained from the Weeds of National Significance 197

(WONS) program (Thorp & Lynch, 2000). Potential distribution maps for mother of millions 198

and chinee apple were assessed by overlaying 50-km2 squares (a similar approach was used 199

in the WONS mapping) over occurrence maps downloaded from the Atlas of Living Australia 200


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(Atlas of Living Australia website), as it was assumed that the area surrounding an existing 201

population of a non-native plant species will have the highest likelihood of becoming 202

invaded. For buffel grass distribution, we used habitat suitability maps created for this 203

species using key indicative variables e.g. soil moisture, soil type, temperature, rainfall, 204

grazing intensity and fire frequency (Martin et al., 2012a). 205

Individual estimates by experts of strategy benefits in a bioregion were aggregated by 206

averaging. The total expected benefit, �� of strategy i in bioregion j was defined by: 207

�� = ��∑ (�� )

��

�, eqn 1 208

where: 209

• �� is the total area of invadable habitat (ha) in bioregion j for the non-native species 210

managed under strategy i 211

• �� is the proportion of invadable area dominated by the non-native species if no 212

action is taken in bioregion j estimated by expert k over the time period (50 years) 213

• �� is the proportion of invadable area dominated by the weed under strategy i in 214

bioregion j estimated by expert k over the time period (50 years) 215

• � is the number of experts who made an estimate for strategy i in bioregion j. 216

To analyse the sensitivity of our results to participant uncertainty, this process was repeated 217

using the upper and lower bound estimates for the proportion of invadable habitat 218

dominated by the non-native plant species with and without strategies being implemented. 219

Cost-effectiveness ranking approach 220


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Cost-effectiveness analysis provides a ranking of strategies based on an expected cost to 221

benefit ratio, where the benefit is not measured in terms of dollars (Levin & McEwan 2001). 222

To determine the total expected cost for a strategy over a bioregion, we summed the cost of 223

all actions required to implement each strategy in a bioregion. For strategies with actions 224

that were costed on a per hectare basis, we used maps of the current distribution of each 225

non-native species and information on the treatment area for each strategy to convert the 226

cost information into an average cost per year over 50 years for each bioregion. In all cases 227

one-off costs, such as building a fence, were counted once, while on-going annual costs, 228

such as maintaining the fence, were summed over 50 years using a rate of 2% per year to 229

calculate net present value, a low discount rate that is considered to be appropriate for 230

inter-generational public goods (Weitzman, 1998; Gollier & Weitzmann, 2010). 231

The cost-effectiveness,��, in ecological terms, of each strategy i in each bioregion j was 232

calculated by: 233

�� =��

�� eqn 2 234

where �� is the total cost of strategy i in bioregion j. The strategies were then ranked by 235

their cost-effectiveness across all bioregions and within each bioregion. 236

Best spatial sets of strategies for a range of budgets 237

The cost-effectiveness approach ranks the most cost-effective strategies, but may not 238

identify the optimal set of strategies to implement for a given budget. Providing the optimal 239

combinations of strategies for a given budget can be useful information to aid managers to 240

negotiate budgets on the basis of expected outcomes. For example, we may discover that a 241

small increase in budget could lead to a sharp decrease of invaded area, an additional cost 242

that decision makers may find worthwhile to invest in. Alternatively, the optimal set of 243


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solutions may reveal that investing half of the budget leads to almost the same benefits as 244

investing the entire budget. Identifying these thresholds of high or negligible return on 245

investment provides critical information to decision makers when allocating appropriate 246

budgets. 247

Finding the optimal combinations of strategies that minimizes the total area invaded at any 248

given budget across all bioregions requires solving a spatial combinatorial optimization 249

problem (0–1 knapsack problem (Bellman, 1957): 250

max∑ ��!��,�∈#,� subject to ∑ ��!�� ≤ �%&'() eqn 3 251

Where !�� is a binary decision variable that denotes whether (!��=1) or not (!��=0) a strategy 252

i in bioregion j is included in the optimal set of strategies. A vector * ∈ {!,,,, !,,-, … , !|#|,|�|} 253

represents a combination of selected strategies. S represents the set of strategies listed in 254

Table S1 and N the number of bioregions. �� identifies the benefits of implemented 255

strategy i in bioregion j. We used dynamic programming to find the optimal set of decisions 256

for budgets ranging from $0 to $2.160 million (Bellman 1957). Algorithm details are 257

presented in Text S1. 258

Results 259

Workshop participants defined 12 management strategies (Table S1) focussing on 10 non-260

native plant species considered to significantly impact biodiversity in the LEB now or in the 261

future (Table 1). With the exception of buffel grass, mother of millions and chinee apple, all 262

species are classified as WONS (Thorp & Lynch 2000). Each strategy consisted of one or 263

more supporting actions, many of which were spatially linked to IBRA (Interim 264

Biogeographical Regionalisation of Australia) bioregions. 265


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The first strategy was an overarching recommendation for improved mapping, information 266

sharing, education and extension efforts in order to facilitate the more specific non-native 267

plant management strategies. This LEB prevention and monitoring strategy was estimated 268

to cost $17.5 million in total over 50 years. Strategy 12 involved the investment of $2.3 269

million over the next 50 years to eradicate all non-native plant species from the GAB 270

discharge springs, which was estimated at the Basin level. We were not able to compare 271

quantitatively strategies 1 and 12 with strategies 2–11 (Table S1). However when predicting 272

the expected benefits for reducing the dominance of the non-native plants over 50 years, 273

experts assumed that strategy 1 would be implemented. 274

The total cost of implementing all 12 non-native plant management strategies over the next 275

50 years was estimated at $34 million per year (Total = $1.7 billion; Table S1). 276

Implementation of these strategies was estimated to result in a reduction of non-native 277

plant dominance by 17 million ha (a potential 32% reduction), roughly 14% of the LEB (Table 278

1). To put this in perspective, on average, implementing all strategies would cost 279

approximately $100 per hectare for the 50-year period, or $2 per hectare per year. If only 280

targeting WONS, the total cost was estimated to be $2.3 million per year for 50 years (Total 281

= $113 million). The strategy for controlling, eradicating and containing buffel grass was the 282

most expensive representing over 90% of the estimated costs, requiring >$30 million 283

annually for 50 years (Total = $1.5 billion, Figure 3). This strategy would account for 0.06% of 284

the total benefits with an estimated reduction in invaded extent of 1 million ha. 285

Amongst the 10 species-specific management strategies, the top five most cost-effective for 286

the entire LEB were the management of: 1) parkinsonia, 2) chinee apple, 3) mesquite, 4) 287

rubber vine and 5) bellyache bush. These strategies combined would cost approximately 288


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$254 000 per year and are estimated to achieve 30% of the total benefits (Table 1, Figure 3). 289

An additional budget of 1 million per year could see avoidance of 16 million ha (94% of the 290

estimated benefits) of land dominated by prickly acacia, mother of millions and Athel pine 291

(Figure 3). Benefit estimates by the experts varied depending on the strategy with standard 292

deviations being more than half the average best guess estimate for three strategies: chinee 293

apple, athel pine and cacti (Table 1). 294

The cost-effectiveness rankings remained the same for all 10 of the basin-wide strategies 295

when calculated with the best guess and upper estimates for expected benefits (Table 1), 296

but did vary with the lower estimates. Using the lower estimates for benefits, parkinsonia 297

was the most cost-effective strategy, mesquite the second most effective, followed by 298

chinee apple, rubbervine and bellyache bush. Mother of millions changed ranks with prickly 299

acacia so that the two species became the sixth and seventh most cost-effective strategies 300

respectively. 301

The top five most cost-effective strategies within separate bioregions (Figure 2) were the 302

management of (Table 2 & S2): 1) parkinsonia in the Channel Country, 2) parkinsonia in the 303

Desert Uplands, 3) mesquite in the Mitchell Grass Downs, 4) parkinsonia in the Mitchell 304

Grass Downs, and 5) mother of millions in the Desert Uplands. 305

When considering implementing strategies at the bioregional level, we found the best 306

combinations of strategies for all budgets by solving the knapsack problem (see Materials 307

and methods). Because we are seeking to maximize the impact of management and 308

minimize our spending at the same time, the solution is a Pareto frontier where every point 309

of the curve represents an optimal solution. In our case, the Pareto frontier exhibits four 310

distinct thresholds (A, B, C and D, Figure 4). When the budget was below $513 000 per year 311


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the total invaded area could reach more than 10 million ha across the LEB (Figure 4A). The 312

benefit of managing non-native plants could be doubled if the yearly budget was increased 313

by about $300 000 per year allowing the implementation of strategy 'S11. prickly acacia' in 314

the Mitchell Grass Downs bioregion (Figures 4 & 5B). Invasion of most of the potential area 315

could be avoided for a yearly budget of about $1.35 million (Figures 4 & 5C). 316

Discussion 317

In the LEB, like many regions of the world, there are more non-native plant species than 318

resources to manage them, and a significant amount of knowledge concerning the location 319

and best control strategies for managing non-native plants is held in the experience and 320

knowledge of natural resource managers (Bart, 2006). Previous attempts at non-native plant 321

species prioritization such as the Australian Weeds of National Significance (Australian 322

Weeds Committee, 2012) were based on the potential ecological and agricultural impacts. 323

The results of our approach, which appraises the benefits and costs of management of these 324

high impact species using a structured elicitation process, provide a prospectus for guiding 325

further investment in non-native species management and in improving information 326

availability and sharing across governmental boundaries. 327

Our analyses suggest that management of several WONS such as parkinsonia and mesquite 328

are likely to be more cost-effective investments than others in large parts of the LEB. 329

Management of parkinsonia was estimated as both relatively cheap and to have high 330

benefits, so control of parkinsonia was consistently ranked as the most cost-effective 331

strategy across the LEB. Other non-native plants will be more costly to manage, but may still 332

be cost-effective to control. Implementation of all non-native plant species was estimated to 333

come at a modest cost ($2 per ha per year on average), but not all strategies are equally 334


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useful, particularly when the budget is limited. We show that there are thresholds in the 335

effectiveness of investment, so that it is important to ensure that the budget allocated to 336

the LEB is realistic to achieve the desired level of impact. While there are cost-effective 337

strategies that can be implemented for less than $513 000 per year, investing less than this 338

amount means that at least 10 million ha of the LEB remains vulnerable to invasion. Our 339

results suggest $900 000 per year could make a substantial improvement on the total area 340

potentially invaded by non-native plants in the LEB by implementing the most effective 341

strategies (control of prickly acacia, Figures 4 &5). Costs per year may appear low given the 342

large spatial extent of the LEB, but strategies were not recommended to be applied to all 343

areas of the LEB, and only where non-native plant control was considered feasible, 344

reasonable or a high priority. 345

Buffel grass management was orders of magnitude more expensive than the management 346

of all other species considered, because of its wide distribution and its high value for 347

rangelands within the LEB. The high costs and low cost-effectiveness ranking for buffel grass 348

control may motivate research into more effective management strategies for buffel grass 349

and also strongly suggest that key sites should be prioritized for protection from buffel grass 350

dominance (e.g. sites of high cultural value such as Uluru National Park (Grechi et al., 2014)). 351

The framework we develop here could be applied to find the most ecologically cost-352

effective solutions for non-native plant control in any management area where multiple 353

stakeholders and landowners reside. A key characteristic of the framework is flexibility, as it 354

can be tailored to a region’s geographical, biological and social characteristics, and the 355

strategies assessed can be updated to suit different perspectives or to account for updates 356

and changes in situations and information. The cost-effectiveness analyses are completed in 357


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a spreadsheet that is provided to the managers of a region following completion of the 358

study and can be updated when necessary. There are many uncertainties in future 359

conditions for undertaking non-native plant species control and eradication in the LEB and 360

many other landscapes, such as the consequences of climate change and future 361

developments not considered in the analysis, which may compound existing threats. A 362

precautionary approach suggests that we should increase investment early, then monitor 363

and review the effectiveness of actions and be aware of emerging threats. A flexible 364

approach, like the one presented here, also means that identified priorities can be 365

integrated with existing and future priority setting approaches. 366

We also make some assumptions with our priority threat management approach. The 367

expert-elicited strategies were designed to have an equal chance of being implemented, 368

provided resources were available. Participants chose strategies that were considered 369

feasible if resources are available, with a few exceptions: for example an organic farmer may 370

be reluctant to spray a non-native plant and lose their certification. In these cases, cost-371

effectiveness was likely overestimated. Estimates of the costs and benefits for the strategies 372

may prove to be higher or lower than predicted because they were based on expert and 373

local knowledge. Changes to these estimates could affect priority rankings. This is why the 374

flexibility of our approach is important – policymakers and land managers can update 375

spreadsheets when more information becomes available or situations change. 376

We also assumed that dominance of habitat by one non-native plant has an equal impact to 377

dominance by another non-native plant and that a cover of greater than 30% was a 378

threshold indicating a species has a biodiversity impact. In reality a variable range of impacts 379

would occur prior to and beyond this binary threshold, depending upon non-native plant 380


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type, ecosystem type, existing dominant and threatened species, and other threats. Our 381

approach could be extended to incorporate differential weights to quantify estimates of 382

higher impacts for some non-native plants over others, which could vary depending on 383

characteristics of each bioregion. 384

Our analysis does not include how potential interactions between strategies will change the 385

expected benefits and costs of strategies, although non-native plant management could be 386

carried out for more than one non-native plant at a time. In these cases the cost-387

effectiveness of carrying out management strategies could be underestimated. 388

Although we used the best data available on the current and potential distributions of non-389

native plants in each of the LEB bioregions, it was recognized by all participants that 390

knowledge of the distributions of non-native plant species is patchy and unreliable, 391

evidenced by the recommendation of strategy 1 that included the centralization of 392

mapping, monitoring and surveillance and information sharing for non-native species 393

incursions and extents. 394

Conclusion 395

A major challenge facing managers when controlling non-native plants is that there are 396

numerous species, each with varying levels of impact. Additionally, the values placed on 397

impacts depend on the objectives of management and the resources available to control or 398

eradicate them. Therefore it is critical to evaluate systematically which non-native species, 399

management actions, and locations should be invested in to provide the greatest benefits 400

for ecosystem integrity per unit cost. 401

Our approach is applicable to non-native species management across large landscapes 402

worldwide because it is, systematic, knowledge-based, and can be easily updated as 403


For Peer Review

improved information on the costs and expected benefits for invasive plant management 404

become available. 405

Acknowledgements 406

We thank the broad range of stakeholders (policymakers, managers, scientists, community 407

representatives) who generously shared their time and expertise at a workshop and in 408

follow-up consultations. This project was funded by DSEWPaC’s National Environmental 409

Research Program, and a CSIRO Julius Career Award (IC). We thank R. Ponce and H. Murphy 410

for providing valuable comments on earlier version of this manuscript. 411

Data Accessibility 412

Data is archived with CSIRO public data access portal. Please follow this link: 413

https://data.csiro.au/dap/landingpage?pid=csiro:13800. 414

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Supporting Information 522

Additional Supporting Information may be found in the online version of this article: 523

Table S1: Estimates of the cost of actions that make up the specific non-native plant species 524

strategies. 525

Table S2: Appraisal of key non-native plant management strategy in each of the LEB 526

bioregions. 527


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Figure S1: Map of known locations of the GAB discharge springs within each of the LEB 528

bioregions. 529

Text S1. Dynamic programming algorithm to find the optimal spatial sets of strategies at 530

various budgets. 531

Table S3. Dynamic programming algorithm used to calculate the set of optimal solutions for 532

all budgets. 533

534


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Table 1: Appraisal of key non-native plant management strategies across the Lake Eyre Basin – average of estimated expected benefits 535

(reduction in total area potentially invaded by the non-native plant species in ha over 50 years, n = 1–6 participants per estimate, based on 536

best guess, upper benefit estimates and lower benefit estimates), standard deviation of ‘best guess’ benefit estimates, net present value of 537

costs (50 years) and cost-effectiveness (CE). Some actions as indicated in Table S1 were not costed over 50 years, but the values are shown this 538

way for comparison purposes. All costs presented are in Australian dollars; m = millions and t= thousands 539

540 Rank (CE)

Best

guess

Rank

(CE)

Upper

Rank (CE)

Lower

Strategy (S) Average benefits

(dominated area

avoided, ha)

best guess (∆ upper

and ∆ lower)

Average

benefits best

guess

standard

deviation

Average

annual costs

(50 years)

1 (40.8) 1 (42.3) 1 (28.8) S3. parkinsonia 1.7 m (+60 t, - 487 t) 758 t $40 678

2 (19.1) 2 (22.7) 3 (13.3) S9. chinee apple 34 t(+17 t, -10 t) 26 t $1797

3 (18.8) 3 (21.04) 2(14.0) S2. mesquite 2.1 m (+252 t, -546 t) 995 t $112 681

4 (13.5) 4 (14.1) 4 (10.5) S4. rubber vine 1.2 m (+50 t, -277 t) 92 t $92 301

5 (11.1) 5(12.7) 5 (7.8) S6. bellyache bush 71 t (+10 t, -21 t) 6 t $6391

6 (10.6) 6 (12.7) 7 (6.0) S11. prickly acacia 10.1 m (+1.9 m, 4.4 m) 956 t $955 678

7 (10.1) 7 (11.5) 6 (7.2) S8. mother of millions 53 t (+7 t, -14 t) 5 t $5340

8 (6.6) 8 (7.1) 8 (3.30) S10. athel pine 331 t (+18 t, -168 t)

155 t $50 071

9 (0.7) 9 (0.9) 9 (0.5) S7. cacti (e.g. coral,

harissia, devils rope) 713 t (+127 t, -200 t)

455 t $1 004 986

10 (0.03) 10 (0.04) 10 (0.03) S5. buffel grass 1.1 m (+32 t, -174 t) 477 t $30 898 881


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Table 2: Summary of the average annual expenditure on each of the non-native plant 541

species strategies and the proportion spent, where the strategies are ordered into 542

decreasing cost-effectiveness. The buffel grass strategy was not included in the bioregion 543

level analyses 544

Strategy (S) Proportional allocation to each bioregion (%)

MGD DEU CHC BHC STP FLB MCR SSD FIN BTP Tan, MII

S3.

parkinsonia

67% 8% 8% 6% 6% 6% – – –

S1

prevention

and

control

programs

for all non-

native

plants

S9. chinee

apple

100% – – – – – – – –

S2. mesquite 38% 12% 38% 12% – – – – –

S4. rubber

vine

21% 58% 21% – – – – – –

S6. bellyache

bush

– 100% – – – – – – –

S11. prickly

acacia

87% – 13% – – – – – –

S8. mother

of millions

74% 26% – – – – – – –

S10. athel

pine

3% – 13% 9% 9% 9% 16% 9% 16% 16%

S7. cacti (e.g.

coral,

harissia,

devil’s rope)

81% 1% 11% 3% <1% 3% – – – –

MGD = Mitchell Grass Downs, DEU = Desert Uplands, CHC = Channel Country, BHC = Broken 545

Hill Complex, STP = Stony Plains, FLB = Flinders Lofty Block, MCR = MacDonnell Ranges, SSD 546

= Simpson Strzelecki Dunefields, FIN = Finke, BTP = Burt Plains, Tanami = Tan, MII = Mount 547

Isa Inlier 548

549

550


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a) 551

b) 552

Figure 1: a) Photo of the endemic Mitchell Grass Downs site (54 million ha across eastern 553

and northern Australia on clay and silt rich soils with a mean annual rainfall of 200–550 mm 554

falling mostly in summer) b) Photo of Mitchell Grass Downs site being invaded by prickly 555

acacia Acacia nilotica, which is converting these natural grasslands to open shrubland 556

(photo credit Jennifer Firn). 557


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558

559

Figure 2: Map of the Lake Eyre Basin (LEB) showing Interim Biogeographic Regionalisation 560

for Australia (IBRA), spanning one-sixth of the Australian continent. 561

562


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563

Figure 3: Total dominated area avoided (>30% cover) by the implementing of the ten key 564

non-native plant species management strategies at increasing levels of annual investment 565

into non-native plant species control when assuming each strategy, when selected, was 566

implemented across the Lake Eyre Basin. 567

568

0

2

4

6

8

10

12

14

16

18

Average annual expenditure (millions $AUD)

Cum

ula

tive

be

nefit,

tota

l dom

inate

d a

rea

avoid

ed (

mill

ion

s h

a)

0 1 2 3

Parkinsonia

Chinee apple

Mesquite

Rubber vine Bellyache Bush

Prickly Acacia

Mother of Millions

Athel pineCacti

33 34

Buffel grass


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569

570

Figure 4: Optimal sets of strategies at the bioregion level for reducing the total area invaded 571

with budgets ranging from AUD$0 to $2.2 million per year represented as a Pareto frontier 572

(see Figure 5 for detailed sets). Additional budget between A and B or C and D will be 573

insufficient to invest in additional strategies to decrease the expected invaded area. The set 574

of strategies in area D only brings small additional benefits. A budget = $513 000, B budget = 575

$829 000, C budget = $1.34 million and D budget = $2.04 million. 576


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577

Figure 5: Optimal set of strategies for a given budget. Additional budget between A and B or 578

C and D will not lead to the selection of additional strategies and decrease of the expected 579

invaded area. Grey areas represent strategies that are selected and white areas represent 580

strategies that are not selected in the optimal set for the given budget. A budget = $513 581

000, B budget = $829 000, C budget = $1.34 million and D budget = $2.04 million. MGD = 582

Mitchell Grass Downs, DEU = Desert Uplands, CHC = Channel Country, BHC = Broken Hill 583

Complex, STP = Stony Plains, FLB = Flinders Lofty Block, MCR = MacDonnell Ranges, SSD = 584

Simpson Strzelecki Dunefields, FIN = Finke, BTP = Burt Plains. 585

586

A B C D


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Supplementary material 1

Table S1: Estimates of the cost of actions that make up each of the specific non-native 2

plant species strategies, including costs (discounted) for the actions over 50 years, and 3

average annual costs over the 50 years (some actions as indicated below were not costed 4

over 50 years, but the values are shown this way for comparison purposes). All costs 5

presented are in Australian dollars. 6

Strategies Description of actions Net present

value (50 years,

discounted)

Average

annual costs

S1.

Prevention

and

monitoring

program for

all weeds

Develop weed management task/plan basin-wide $160,729 $3,215

Mapping, monitoring and surveillance (ground and

aerial, build on weed spotters network) $217, 651 $4,353

Centralised information sharing for weed

incursion/extents $20,000 $400

Secure positions: two FTEs, one FTE for mapping and

centralised information sharing and one FTE for on-

ground activities $8,333,540 $166,671

mesquite awareness campaign $76,651 $1,533

bellyache bush awareness campaign $76,651 $1,533

chinee apple awareness campaign $76,651 $1,533

buffel grass awareness campaign $217,651 $4,353

S2.

mesquite

(Prosopis

glandulosa)

Eradicate from the following bioregions: Mitchell

Grass Downs (MGD), Desert Uplands (DEU), Broken

Hill Complex (BHC) and the Channel Country (CHC);

$800,000 investment in the first year and then

$300,000 per year for next four years. $3,028,191 $60,564

Periodic suppression of new infestations over time;

investment of $200,000 per year for the first three

years and repeated every 10 years $2,6051873 $52,118

S3.

parkinsonia

Prevent spread into Diamantina National Park by

eradicating from Springcreek and Diamantina river

north of the National Park up to the western river

convergence $471,346 $9,427

Eradicate from the following bioregions: Stony Plains,

Broken Hill (STP) Complex (BHC), Flinders Lofty Block $343,193.00 $6,864


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(FLB) (SA)

Control downstream outliers and establish large-scale

buffer zones in the Georgina and Thomson Rivers

(downstream of Boulia and Jundah/Windorah

respectively). Requires initial control program (three

years) then periodic suppression every 10 years.

$716,065

$14,321

Impact reduction through introduction and

proliferation of dieback biological control agents in

established infestation areas of the MGD, DEU and

CHC $503,278 $10,066

S4. rubber

vine

Contain and control in DEU $2,264776

$53,896

Eradicate from MGD, CHC

$1,920,286

$38,406

S5. buffel

grass

Contain in STP, Finke (FIN) and control in all other SA

bioregions

$421,611

$8,432

Control in MacDonnell ranges (MDR) to reduce hot

burns – especially along creeks

$228,386,768

$4,567,735

Control and locally eradicate (including rehabilitation)

and prevent incursions into clean areas in gazetted

conservation areas in Queensland

$1,316,135,691

$26,322,714

S6.

bellyache

bush

Eradicate from DEU $319,543

$6,391

S7. cacti

(e.g. coral,

harissia,

devil’s

rope)

Contain all cacti spp. in southern part of SA, eradicate

to north

$3,613,715

$72,274

Control and contain all cacti elsewhere $46,635,538 $932,711


For Peer Review

S8. mother

of millions

Eradicate from urban areas in all areas of the Basin

$26,948

$539

Control and contain in DEU, MGD and any other

occurrences

$240,033

$4,801

S9. chinee

apple

Eradicate from MGD

$89,826

$1,797

S10. athel

pine

Eradicate weedy and high risk Athel pine from

Queensland (e.g. MGD and CHC), and Northern

Territory, except from the lower FIN

$1,358,496

$27,170

Control in South Australia

$1,145,048

$22,900

S11. prickly

acacia

Eradicate from South Australian part of CHC

$10,000

$200

Eradicate from Northern Territory part of MGD

$20,000

$400

Prevent further spread southwards down the three big

rivers (from Stonehenge on cooper system, converging

Diamantina and Western and Wokingham creek,

Boulia on the Georgina)

$32,052,078

$641,042

Containment and progressive reduction of already

infested areas - DEU, MGD, CHC $12,579,442 $251,589

S12. GAB

discharge

springs

Eradicate all non-native plants from the discharge

springs $2,253,453 $43,069

$1,689,549,071 $33,333,017


For Peer Review

Total

7

8


For Peer Review

Table S2: Appraisal of key non-native plant management strategy in each of the 9

bioregions of the LEB – estimated average expected benefits (reduction in total area 10

potentially invaded by the non-native plant species), average costs (discounted) and cost-11

effectiveness (CE). Not all strategies were costed within all bioregions, as not all non-12

native plant species can become established within each of the LEB bioregions, because of 13

low and unpredictable rainfall and low nutrient edaphic conditions. Strategies were 14

ranked based on their cost-effectiveness within each of the bioregions and also across all 15

bioregions. The buffel grass strategy was not included in the bioregion level analyses. All 16

costs presented are in Australian dollars (m = millions and t = thousands). 17

Bioregions Strategies Rank CE

within

bioregion

Rank CE

across

LEB

Average

benefits

between

experts over

50 years (ha)

Average Annual

costs

CE

Mitchell

Grass Downs

S2. mesquite 1 3 1.5m $43,226 35.6

S3. parkinsonia 2 4 931t $27,103 34.4

S10. athel pine 3 6 32t $1,712 19.1

S9. chinee apple 4 8 23t $1,797 13.2

S11. prickly

acacia

7 9 9.6m $829,783 11.6

S4. rubber vine 5 11 187t $19.203 9.8

S8. mother of

millions

6 22 1t $3,968 3.4

S7. cacti 8 33 175t $811,090 0.2

Desert

Uplands

S3. parkinsonia 1 2 162t $3,355 48.3

S8. mother of

millions

2 5 28t $1,372 20.4

S6. bellyache

Bush

3 12 61t $6,800 9.7

S4. rubber vine 4 13 400t $53,896 7.40

S2. mesquite 5 14 79t $13,114 6.1

S7. cacti 6 26 41t $14,189 2.9

Channel

Country

S3. parkinsonia 1 1 450t $3,355 134.1

S10. athel pine 2 7 104t $6,292 16.70

S4. rubber vine 3 10 196t $19,203 10.20

S2. mesquite 4 18 197t $43,226 4.6

S11. prickly

acacia

5 25 373t $125,894 3.00

S7. cacti 6 34 14t $107,43 0.10

Broken Hill

Complex

S3. parkinsonia 1 21 8t $2,288 3.7

S10. athel pine 2 27 13t $4,580 2.9

S7. cacti 3 30 50t $31,424 1.6

S2. mesquite 4 32 20t $13,114 1.53

Stony Plains S10. athel pine 1 17 21t $4,580 4.6


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S3. parkinsonia 2 20 8t $2,288 3.8

S7. cacti 3 23 3t $9,427 3.2

Flinders

Lofty Block

S3. parkinsonia 1 15 13t $2,288 5.9

S10. athel pine 2 24 14t $4,580 3.1

S7. cacti 3 31 48t $31,423 1.5

Simpson

Strzelecki

Dunefields

S10. athel pine 1 28 11t $4,580 2.3

Finke S10. athel pine 1 19 32t $7,915 4.1

MacDonnell

Ranges

S10. athel pine 1 16 40t $7,915 5.1

Burt plains S10. athel pine 1 29 14t $7,915 1.8

Tanami

S1 prevention and control programs for all non-native plants Mt Isa Inlier

18


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19

20

Figure S1: Map of known locations of the GAB discharge springs within each of the LEB 21

bioregions. Data sourced from the IBRA and Department of Sustainability, Environment, 22

Water, Population and Communities, Australia Commonwealth Government. 23

24

25


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Text S1. Dynamic programming algorithm to find the optimal spatial sets of strategies at 26

various budgets. 27

Intuitively, let us assume we computed an optimal solution � for a given budget b. If we 28

remove a strategy j with benefit Bj from the optimal solution, the remaining solution �-{�� } 29

will remain optimal if the budget available is b-Cj, where Cj is cost. This optimal substructure 30

property allows us to solve knapsack problems using dynamic programming. Dynamic 31

programming solves a small sub-problem of the knapsack and then extends this solution 32

iteratively until the complete problem is solved. 33

max∑ �∈� � subject to ∑ � ≤ �� 34

Where is a binary decision variable that denotes whether (=1) or not (=0) a 35

strategy j is included in the optimal set of strategies. A vector � ∈ {�, �, … , |�||�|} 36

represents a combination of selected strategies. S represents the set of strategies listed 37

in Table S1 and N the number of bioregion. � identifies the benefits of implemented 38

strategy j. Below we provide the details of the dynamic programming algorithm we used 39

to solve our knapsack problem. The algorithm is in two parts. The first part computes 40

the dynamic programming equations for all budgets (Table S3, Lines 1-18, Figure 4) by 41

applying Bellman recursions. A strategy is either too expensive to be selected (Line 7) 42

or if it isn’t we evaluate the added benefits of selecting this strategy (Line 10). If the 43

strategy j improves upon previous strategies, it is selected. The strategy contributes 44

B(j) at a cost of C(j) (Lines 11-14). The second part of the algorithm retrieves the 45

optimal decisions by exploring the temporary variable A (Table S3, Lines 19-36, Figure 46

5). Interested readers can refer to (Kellerer et al., 2004)for additional details and 47

alternative approach to solving knapsack problems. 48

49


For Peer Review

Table S3. Dynamic programming algorithm used to calculate the set of optimal solutions for 50

all budgets adapted from Hans et al. (2004). 51

Dynamic programming for all budgets

Input variable:

n: total number of strategies

B: vector of benefits of length n

C: vector of costs of length n

Cmax: maximum budget we are willing to invest.

Output variable:

zn(0..Cmax) : optimal allocation of funds for all budgets

X(1..n,0..Cmax) : optimal decisions for all budgets

1

2

A = zeros(0..n,0..Cmax); % we need A to retrieve X

For budget b=0 to Cmax

3 z0(b)=0; % initialise the first value

4 endFor

5 For Strategy j=1 to n

6 For budget b=0 to C(j)-1

7 zj(b) = zj-1(b) % strategy j is too expensive

8 endFor

9 For budget b=C(j) to Cmax

10

11

12

13

14

15

16

17

if zj-1(b-C(j))+ B(j) > zj-1(b) then

% select strategy j by adding benefits to zj(b)

zj(b) = zj-1(b-C(j)) + B(j)

% update A

A(j,b) = 1

else zj(b)=zj-1(b)

endif

endfor

18 endFor


For Peer Review

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

% We have calculated all the zj(b) including the optimal solution zn(b) for all

% budget b. We now need to retrieve the optimal decision X from A

For budget b=Cmax to 0 % for all budgets

strategy={};

j=n;

c=b;

While j>= 1 and b>0

While j>= 1 and A(j,c)==0

j=j-1; % strategy j has not been selected

endWhile

If j>=1 and A(j,c)==1

strategy=strategy + {j}; % strategy j was selected

c = c - C(j); % update cost, look for the next strategy selected

j=j-1; % update strategy number

endIf

endWhile

X(strategy,b)=1;

endFor

% We have retrieved all the optimal decisions.

52

References 53

Kellerer, H., Pferschy, U. & Pisinger, D. (2004) Knapsack problems. Springer. 54

55


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