Post on 03-Apr-2021
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Benefits of the Ballot Box for 1
Species Conservation 2
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Kailin Kroetz1, James N. Sanchirico
2, Paul R. Armsworth
3, H. Spencer Banzhaf
4 4
1 Department of Agricultural and Resource Economics, University of California, Davis, One Shields 5
Avenue, Davis, CA 95616 ; email: kkroetz@ucdavis.edu 6 2 Department of Environmental Science and Policy, University of California, Davis, One Shields Avenue, 7
Davis, CA 95616 and University Fellow, Resources for the Future; email: jsanchirico@ucdavis.edu; 8 phone: (530) 754-9883 9 3 Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, TN 37996; email: 10
p.armsworth@utk.edu 11 4 Department of Economics, Andrew Young School of Policy Studies, Georgia State University, 14 12
Marietta Street, NW, Atlanta, GA 30303, and Research Associate at the NBER, and a Senior Research 13 Fellow at the Property and Environment Research Center (PERC); email: hsbanzhaf@gsu.edu 14
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Running title: Ballot Box Conservation 16
Keywords: Biodiversity, conservation, conservation movement, endangered species, integer 17
programming, open space, referenda, reserve site selection 18
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Type of article: Essay 20
Manuscript length: Abstract (143 words), Body (5,000), References (43), Figures (4), Tables (1) 21
Corresponding author: 22
James N. Sanchirico 23
Department of Environmental Science and Policy 24
University of California, Davis 25
One Shields Avenue, Davis, CA 95616 26
Telephone: (530) 754-9883 Email: jsanchirico@ucdavis.edu 27
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Author Contributions: K.K, J.N.S., P.R.A., and H.S.B. designed research, analyzed results, 29 and wrote the paper. 30 31
The authors declare no conflict of interest. 32
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Benefits of the Ballot Box for 33
Species Conservation 34
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Kailin Kroetz, James N. Sanchirico, Paul R. Armsworth, H. Spencer Banzhaf 36
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Abstract 38 39
Recent estimates reaffirm that conservation funds are insufficient to meet biodiversity 40
conservation goals. Organizations focused on biodiversity conservation therefore need to 41
capitalize on investments that societies make in environmental protection that provide ancillary 42
benefits to biodiversity. Here, we undertake the first assessment of the potential ancillary 43
benefits from the ballot box in the United States, where citizens vote on referenda to conserve 44
lands for reasons that may not include biodiversity directly but that indirectly might enhance 45
biodiversity conservation. Our results suggest that referenda occur in counties with significantly 46
greater biodiversity than counties chosen at random. We also demonstrate that large potential 47
gains for conservation are possible if the past and likely future outcomes of these ballot box 48
measures are directly incorporated into national-scale conservation planning efforts. The possible 49
synergies between ballot box measures and other biodiversity conservation efforts offer an 50
under-utilized resource for supporting conservation. 51
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57 58
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Introduction 59
60
Global conservation funding needs to at least double to meet the 2020 biodiversity commitments 61
of the Convention on Biological Diversity (McCarthy et al. 2012). The shortfall of funding 62
heightens the importance of finding additional funding sources to support conservation. It also 63
means that what resources are available need to be deployed efficiently and has led to calls for 64
improving the coordination and planning of conservation organizations in a bid to capture 65
potential efficiency gains (Mace et al. 2000; Kark et al. 2009). The idealized coordinated efforts 66
that some authors have called for would prioritize sites that protect biodiversity at low cost 67
(Margules & Pressey 2000; Naidoo et al. 2006; Wilson et al. 2009), engage in planning that 68
operates at a number of scales (Erasmus et al. 1999; Meretsky et al. 2012), and have access to 69
resources for conservation that are fungible over these scales (Balmford et al. 2003). 70
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Although the conservation biology literature includes pleas for more systematic planning 72
(Margules & Pressey 2000; Wilson et al. 2009), these efforts often are not well-coordinated 73
(Bode et al. 2011) or when coordinated, there is a mismatch between ecosystem and planning 74
scale (Meretsky et al. 2012). Indeed, much of the support for conservation is locally sourced 75
(Armsworth et al. 2012) and is intended to meet locally derived priorities (e.g. to provide open 76
space, recreation opportunities and other ecosystem services). For example, in the United States 77
there are over 1,600 active nonprofit land trust organizations that have varying objectives 78
including open space preservation, but whose activities may provide ancillary benefits for 79
biodiversity conservation (see e.g. Chang (2011)). As these groups have their own locally-80
derived objectives aside from biodiversity, their conservation activities might not be judged as 81
efficient in terms of biodiversity conservation per dollar spent. Nevertheless, their efforts are 82
likely beneficial to biodiversity. Understanding the magnitude of these potential gains and how 83
best to capitalize on them in biodiversity planning is an important question for the conservation 84
community. 85
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Much of the support for local land trusts derives from the direct democracy process, where 87
citizens vote on ballot initiatives to conserve lands for a myriad of reasons (e.g., public access to 88
open-space, conservation, groundwater protection, and recreation). According to the Land Trust 89
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Alliance (LTA), there have been approximately 2,400 land-vote referenda since 1988 occurring 90
in over 46 states and setting aside more than $58 billion in conservation funds (Trust for Public 91
Land 2012). Although larger conservation organizations (e.g., LTA, The Nature Conservancy) 92
do provide support to help formulate initiatives and bring them to the ballot (Kline 2006; 93
Kotchen & Powers 2006; Sundberg 2006; Nelson et al. 2007; Banzhaf et al. 2010), ultimately 94
the success of the referendum depends on the preferences of the jurisdictional (e.g., municipality, 95
county) residents towards land conservation as expressed through their votes (see, e.g., Deacon 96
& Shapiro (1975)). 97
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To date, there is no systematic assessment of the potential ancillary benefits of the ballot box 99
initiatives on biodiversity protection. Even though the local services citizens derive from land 100
conservation are likely not the same as the value of a site assigned by a planner with the 101
objective of maximizing biodiversity, the potential biodiversity benefits can be nonetheless large 102
in aggregate because ballot initiatives are prevalent and the sums of money are substantial (e.g., 103
according to Jordan et al. (2007), the average yearly expenditure on these initiatives is 104
approximately on par with the U.S. average annual expenditure of the U.S. Conservation Reserve 105
Program). 106
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Furthermore, the potential for efficiency gains by incorporating these ballot measures into 108
national-scale planning is an open question. For example, Abbitt, Scott, and Wilcove (2000) 109
identified U.S. county-level hotspots of vulnerability across the United States as a type of area 110
for central planning efforts to target. These hotspots where based on projected increases in 111
populations and development and occur in areas near urban centers. These areas, however, 112
might also be the places more likely to hold ballot measures for land conservation (see e.g. Press 113
(2002)). 114
115
We contribute to the literature by developing insights into the complementarity of these two 116
processes: top-down national-scale biodiversity planning and bottom-up citizen voting. 117
Specifically, our paper connects the political-economy research analyzing the occurrence and 118
success of the land-vote referenda (e.g., Kline (2006), Kotchen & Powers (2006), Nelson et al. 119
(2007) and Banzhaf et al. (2010)) and the conservation biology literature on the optimal 120
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conservation site selection that assumes a nationally-planned and well-coordinated set of 121
activities. In particular, we compare the outcome of the direct democracy process with a 122
hypothetical top-down planner to address the following questions: how well has direct 123
democracy done at directing funding towards places that the top-down planner would have 124
identified and how well is direct democracy likely to do by this standard in the future? We also 125
illustrate the potential for efficiency gains by incorporating the spatial patterns of direct 126
democracy directly into conservation planning. 127
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Materials and Methods 130
131
We divide up our analysis into three parts. First, we undertake a retrospective analysis and 132
examine the overlap of the location of past successful ballot measures with areas of high species 133
concentration. We also compare the successful ballot measures with both a random selection 134
process and one that corresponds to the recommendation of a hypothetical top-down biodiversity 135
planner allocating a fixed conservation budget across the United States. The planner is 136
represented by the solution of a reserve site selection algorithm (RSS). In the second part, we do 137
a prospective analysis using a multivariate regression model to predict the likelihood of 138
jurisdictions holding and passing land vote referenda. We compare the set of predicted counties 139
to data on the presence of endangered species and to the sites selected by the top-down planner. 140
Finally, we do an illustrative experiment where we include the past results of referenda directly 141
into the reserve site RSS algorithm to investigate the potential efficiency gains from 142
incorporating direct democracy outcomes in conservation planning. 143
144
Our analysis uses a number of different data sources to capture the two processes. The three 145
main data sets include; county-level USDA agricultural land values as a proxy for the cost of 146
conservation land in a county; county-level data on the presence of endangered species; and 147
county referenda ballot and outcome data between 1988-2006 come from the Trust for Public 148
Land’s “Landvote database”. To focus on referenda that have potential ancillary benefits for 149
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conservation, we exclude measures that list only recreational and historical purposes (removes 150
~10% of the total referenda from 1988-2006). 151
To derive species presence/absence information, we utilize NatureServe’s GIS files and 152
calculate, for each U.S. county, a list of the species that are present and the rating the species 153
receives from NatureServe. NatureServe rates species on a G1 to G5 scale, where G1 is 154
critically imperiled and G5 is secure. We focus on species classified as G1 (critically imperiled) 155
and G2 (imperiled). We also use the same data on Federal endangered species that have been 156
used in other site selection algorithms (see e.g. Ando et al. (1998)). NatureServe’s G1 and G2 157
designations include 3,949 species, which is much more inclusive than the Federal endangered 158
species list which only includes 874 species. The correlation between the number of 159
NatureServe G1G2 species in a county and the number of ES is .74, suggesting there are 160
differences in the spatial distribution of biodiversity represented by the two datasets (Stein et al. 161
2000). 162
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Retrospective Analysis 164
In the retrospective analysis, we examine two questions: 165
Do successful ballot referenda occur in counties with more or fewer G1G2 species and 166
ES than the G1G2 species and ES in randomly sampled counties? 167
Are successful ballot referenda more likely to have occurred in counties targeted by RSS 168
algorithms? 169
170
To answer the first question, we compare ballot box outcomes to a random sample of counties. 171
First we compare the number of species in ballot box counties to the number of species covered 172
when randomly selecting 146 counties (equal to the number of counties with prior successful 173
referenda; see Figure SI-14 and SI-15 for G1G2 species and ES). Then we compare the number 174
of species in ballot box counties to the number of species covered by randomly selecting 175
counties having the same overall area as those with successful ballot measures (see Figure SI-16 176
and SI-17 for G1G2 species and ES). 177
178
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To answer the second question, we examine how outcomes of successful ballot box measures 179
compare to the set of sites selected by a top-down biodiversity planner. In this case our 180
benchmark is the outcomes of an RSS algorithm. While there are many possible variants of RSS 181
formulations to consider (see, e.g. Sarkar et al. (2006) and our review in the SI), we choose for 182
illustration purposes the simple yet seminal framework of Ando et al. (1998). Following Ando 183
et al. (1998), we use two common site selection approaches to summarize the results of top-down 184
conservation planning. Specifically, we solve the set covering problem (SCP) (Underhill 1994) 185
and the maximum coverage problem (MCP) (Camm et al. 1996; Church et al. 1996) (see SI for 186
mathematical formulation) from operations research. We explore several budgets in our 187
analysis. Our base budget amount is consistent with that used in Ando et al. (1998), except that 188
we account for the differences in farmland values (1992 in Ando et al. and 2002 here) by 189
inflating the budget to reflect an 8% increase per year over the 10-year period. 190
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Prospective Analysis 192
A key component of the prospective analysis is the development of a predicted probability of a 193
successful referendum for each county in the U.S. that reflects the likelihood of the local citizens 194
putting a measure on the ballot and passing it. We use these predicted probabilities to examine 195
the overlap between predicted sites of successful referenda and species presence/absence and the 196
RSS benchmark. Specifically, we address the following two questions: 197
How do the predictions of our model of successful ballot box referenda compare to 198
counties with G1G2 species or ES? 199
How do the predictions of our model of successful ballot box referenda compare to 200
counties targeted by RSS? 201
202
To predict the probability of a successful referendum, we build off of the econometric analysis of 203
Banzhaf, Oates, and Sanchirico (2010) and utilize the same set of covariates. They estimated a 204
polychotomous sample selection model using Landvote data from 1988-2006. Their set of 205
covariates included U.S. Census data, USDA Economic Research Service land use data, U.S. 206
county election data, and data on state characteristics that may influence the occurrence of 207
referenda. 208
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Based on their results, we make a number of simplifying assumptions. First, we use a probit 210
model for estimating the probability of holding a successful referendum. Banzhaf, Oates, and 211
Sanchirico (2010) used a multinomial logit model due to their interest in developing predictions 212
specific to funding mechanism (e.g. bond, tax) for the referendum. The funding mechanism, 213
however, is not of prime interest for our analysis. Second, we do not control for the potential 214
selection issue (that is, we only observe counties that have held referenda), because they found 215
along with Kotchen and Powers (2006) and Nelson, Uwasa, and Polasky (2007) that a two-step 216
Heckman (1979) correction for sample selection is not necessary. 217
218
The probit model is 𝑃𝑟(𝑆𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 𝑅𝑒𝑓 = 1|𝑋𝑖) = 𝛷(𝛽𝑋𝑖), where Successful Ref is the 219
dummy variable for whether or not the county has held at least one successful referendum from 220
1998-2006, X is a matrix of explanatory variables, Φ is the cumulative normal distribution, and β 221
is a vector of coefficients on the explanatory variables. The set of explanatory variables include 222
those in Table 1 and public finance (e.g., type of measure, tax or bond), political-economy (e.g., 223
% voting for Bush in 2000, voter turnout in the election, home rule index, etc.), along with other 224
controls (e.g., latitude and longitude, land area (sq. miles), % change in farmland, % of land in 225
farming, % living in urbanized area, etc.). Estimation results are available in the SI. Using the 226
estimated coefficients, we predict the probability of a successful referendum. 227
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Efficiency gains experiment 229
230
In addition to illustrating the overlap between areas of conservation interest and those places that 231
have passed or are likely to pass ballot measures, we construct a thought experiment to 232
illuminate the potential gains from directly incorporating the outcomes of ballot measures into 233
conservation planning. 234
235
In particular, we ask the following question: 236
How much would including counties with successful ballot referenda in RSS algorithms 237
improve the efficiency of conservation expenditures? 238
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The answer to this question depends in part on the nature of the conservation organization that is 240
engaging in national-scale planning. We consider the case where the organization views any land 241
preserved through referenda as exogenous to their efforts and as a potential substitute for their 242
own land acquisitions (we discuss other possible scenarios in the Discussion section). 243
Substitution is equivalent to assuming that the organization will count species as covered if 244
counties with successful referenda coincide with species ranges. For such an organization, we 245
illustrate the potential gain they could realize by adapting their prioritization of land purchases to 246
account for the ballot box measures. 247
248
We measure the gains by examining the change in number of species conserved and budget 249
invested when (1) RSS is conducted independently of land vote and (2) RSS takes into account 250
locations of past successful referenda and assuming species in these counties are covered at zero 251
cost. The latter assumption implies that the hypothetical conservation planner can focus their 252
limited budget only on the remaining, unprotected species. 253
254
A possible extension could consider an organization that does not want to work directly within 255
the land vote process but wants to invest resources in co-locating conserved and referendum 256
sites. In this case, the spatial configuration of sites is important for measuring the potential gains, 257
as groups can benefit from investing and/or partnering with other groups that are also conducting 258
local conservation efforts, possibly supported by funding made available through the ballot box. 259
260
Results 261
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Retrospective Analysis 263
Analyzing the Land Vote data for U.S. counties from 1988-2006, we find that counties with at 264
least one successful referendum tend to have a higher median household income, a higher 265
median home value, and a higher population density than counties with none (see Table 1). 266
These successful referenda counties, therefore, have similar characteristics to those used by 267
Abbitt et al. (2000) to create their vulnerability index, suggesting there may be some overlap 268
with counties important for biodiversity investments. 269
270
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To test this possibility, we compare the distribution of successful referenda counties with the 271
G1G2 species and ES presence/absence data. Just over 20% of species classified as G1G2 occur 272
in the set of 146 counties that had successful referenda from 1988-2006 (see Figure SI-1 for a 273
map of all counties with at least one successful referendum from 1988-2006). Approximately 274
35% of ES are present in counties that had successful referenda. These counties tend to be in the 275
Northeast, Florida, and the West. 276
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To provide context for these percentages, we compare ballot box outcomes to a random sample 278
of counties. We test whether the number of G1G2 species and ES in ballot box counties is 279
greater than the number of G1G2 species and ES covered when randomly selecting 146 counties 280
(equal to the number of counties with prior successful referenda). We find that counties with 281
successful referenda cover more species than would be expected by a random sample: the p-282
values associated with the hypothesis test for G1G2 species and ES are .00018 and .00308, 283
respectively. We also find that the number of G1G2 species and ES in ballot box counties is 284
greater than the number covered by randomly selecting counties having the same overall area as 285
those with successful ballot measures (the p-values associated with the hypothesis tests for G1G2 286
species and ES are .00072 and .0031, respectively). 287
288
Next we ask: How do the outcomes of successful ballot box measures compare to the set of sites 289
selected by a top-down biodiversity planner? For each comparison of the RSS to referenda 290
outcomes, we consider the overlap in terms of the counties and the species covered. We present 291
results here for our base budget, which is consistent with that used in Ando et al. (1998). In 292
terms of the counties, when the objective is to maximize G1G2 species covered, 170 counties are 293
selected via RSS, of which only 10 counties (~7%) are also in the set of counties with prior 294
successful referenda (Figure 1). These RSS-selected counties are more concentrated in the west, 295
are not as concentrated in the northeast, and have a denser distribution in Appalachia than the 296
counties with prior successful referenda. 297
298
In terms of species covered, we find that 2,719 G1G2 species (~69%) are covered by RSS 299
selected counties compared to 846 G1G2 species in counties with past successful referenda 300
(~21%). Not surprisingly, the average number of G1G2 species and ES is higher in counties 301
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selected via RSS and the farmland and housing prices are lower (see Table 1). The RSS is by 302
design selecting counties with greater diversity at lower cost. 303
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Prospective Analysis 305
While the focus of the retrospective analysis is on how well direct democracy has done at 306
passing land set-aside referenda in important locations for preserving species, the prospective 307
analysis asks how well it likely will do by comparing local citizen preferences for land 308
conservation measures with G1G2 species and ES data. To develop the predicted probabilities 309
associated with any county holding and passing a land vote referenda, we estimate a multivariate 310
cross-sectional regression (probit) model for 1998-2006 – the period over which we have a full 311
suite of covariates. We find a pseudo-R2 of .4735 (column 1 in Table SI-1), which is an 312
acceptable fit for a cross-sectional analysis. 313
314
We also run two sets of robustness checks. First, we do an out of sample test (see Methods) 315
where we compare the predicted probabilities for the 13 counties that hold successful referenda 316
from 2007-2011 to those for counties that never have a successful referendum from 1988-2011. 317
We find the average predicted probability for the counties with successful referenda from 2007-318
2011 is 24.92% compared to an average of 2.31% for the counties that never have a successful 319
referenda. Second, we omit the referenda that occurred in a given year and repeat the 320
estimation. We do this omitting each year one at a time for 1998-2006. The average predicted 321
probabilities for the counties that previously were designated as having had a successful 322
referendum before we dropped the year’s data are, on average, higher (24.1% average predicted 323
probability of holding a successful referendum for counties with prior successful referenda 324
versus 2.4% for counties without prior successful referenda). 325
326
In terms of statistically significant covariates, we find that voter turnout in the election, the % 327
voting for Bush in the 2000 presidential elections (proxy for Republican voters), and % without a 328
high school degree in the county are statistically significant at the 1% level and negatively 329
correlated with the probability of holding a successful referenda. Median income (1% level), 330
home rule index (1% level), and % living in an urbanized area (10% level) are all positively 331
correlated. 332
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To examine the potential contribution to biodiversity conservation from counties that are more 334
likely to hold successful ballot measures in the future, we examine species coverage over varying 335
thresholds of probabilities for both the comparison to G1G2 species and ES and the RSS 336
benchmark. That is, we assume that all counties with a predicted probability greater than the 337
threshold eventually will pass referenda that protect lands (and cover the species) in the county. 338
In this analysis we only generate predictions for counties that have not had prior successful 339
referenda. 340
341
We first examine the number of species that are present in counties with varying predicted 342
probabilities of having successful referenda (see Figure 2 for the G1G2 results and Figure SI – 7 343
for the ES results, which are similar). While most of the predicted probabilities are clustered 344
near zero, there are, however, 16 counties with a predicted probability of having a successful 345
referendum greater than 50%. Approximately 3% of G1G2 species and 8% of ES are covered by 346
this set of counties. If we assume all species in counties that had successful referenda in the past 347
are also conserved, then 23% of G1G2 species would be expected to be conserved overall. 348
Under the same assumption 37% of ES would be expected to be conserved overall. The number 349
of species covered varies with the probability threshold, for example, there are 82 counties with 350
predicted probabilities of having a successful referendum greater than 20%. These 82 counties 351
cover 11% of G1G2 species and 29% of ES. These results suggest that past and predicted future 352
referenda conserve lands in counties that overlap with the presence of species of concern. 353
354
Finally, we compare the potential contribution of direct democracy toward biodiversity to our 355
biodiversity planner benchmark. We do this by comparing the predicted probability of having a 356
successful referendum with our RSS sets. The sets of sites selected via RSS are identical to the 357
sets described previously. We focus on the overlap between counties with past successful 358
referenda and various thresholds for the predicted referendum success, and sites selected via 359
RSS. Figure 3 shows the distribution of the predicted probability of success of referenda in 360
counties in the contiguous United States (darker colors indicate higher predicted probability) 361
overlaid with the results from the base case RSS (black-hashed counties). 362
363
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We see evidence, for example in the Western United States, of overlap between counties with 364
high predicted probabilities and the RSS set. However, we also observe counties, for example, 365
inland counties in the Great Plains and Appalachia, which are selected via RSS but have a low 366
probability of successful referenda. We also find a number of places where the referenda occur in 367
counties neighboring those chosen by the biodiversity planner, implying that there might be 368
agglomeration benefits especially for species with ranges that cover multiple counties 369
(something we are not considering in this analysis; see e.g. Önal & Briers (2002)). These results 370
assume a budget for the RSS consistent with Ando et. al (1998), which is 14% of the total 371
necessary to conserve all G1G2 species; as we increase the budget the number of counties in the 372
RSS increases as does the overlap (see Figure SI-12). 373
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Potential Efficiency Gains 375
376
Here we illustrate one possible way a national conservation organization might use ballot box 377
measures to utilize their budgets more efficiently. Specifically, we rerun for a range of budgets 378
the previous RSS (MCP) analysis assuming land in counties with prior successful referenda is 379
free (zero cost) to the central planner. 380
381
Figure 4 panel A illustrates the relationship between a national conservation group’s budget and 382
species (see Figure SI-9 for ES). The additional coverage, in terms of species, from taking into 383
account sites covered via prior successful referenda is substantial, especially at low budgets. For 384
example, if we focus on the base case budget, which is marked in the figure by the red dashed 385
line and based on updating budgeting assumptions used in Ando et al.’s (1998) top-down 386
planning study, we find that the base RSS budget could be reduced by 45% while still protecting 387
the same number of G1G2 species (horizontal green line in the Figure; 47% with ES). Looked at 388
another way, at the same budget level, the planner can protect 14% more G1G2 species (vertical 389
blue line; 12% more ES). 390
We also find that the location of conservation priorities change when accounting for the ancillary 391
benefits available from successful referenda in national scale conservation planning (see Fig. 4 392
panel B). For example, at the base budget, the national-scale planner omits 27 counties from the 393
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optimal solution, 10 of which had past successful referenda but 17 of which did not. These 17 394
share species with counties protected by referenda and thus become a lower priority, as 395
evidenced for example, by the new RSS no longer selecting counties in peninsular Florida. With 396
the savings from not having to allocate funds to counties with past referenda, the planner adds 34 397
new counties to their optimal set. When comparing sets of species in the omitted and new 398
counties, we find that the planner substitutes toward counties that have a higher concentration of 399
species not in referenda sites. 400
401
Discussion 402 403
The purpose of this paper is to examine the potential for the direct democracy process to 404
contribute to biodiversity conservation in the United States. To our knowledge the ancillary 405
benefits of citizen-supported initiatives have yet to be considered in this light. The values local 406
residents derive from living near land set aside via referenda is analogous to the “human amenity 407
value” that Fuller et al. (2010) suggested could be incorporated into top-down planning objective 408
functions. Rather than requiring top-down planning to account for human amenity value though, 409
the referenda process in the United States allows local residents to express their support for 410
particular amenities and local biodiversity by voting in favor of conservation initiatives directly. 411
Therefore, unlike other processes outside the control of the top-down planner that may serve as a 412
source of inefficiency, such as political pressure (Pressey 1994; Margules & Pressey 2000), we 413
demonstrate that the direct democracy process might actually enhance the efficiency of 414
conservation budgets. 415
416
While we illustrate one way in which conservation planners might be able to interact more 417
systematically with the land vote process, our results also hint at other ways conservation 418
planners might be able to interact more systematically with the land vote process. For example, 419
a conservation organization may find it cost-effective to allocate resources to help referenda with 420
a low probability of occurrence but a relatively high predicted percent voting yes to get on the 421
ballot. Resources could also be allocated to help referenda already on the ballot pass. 422
423
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424
To inform such efforts, organizations could investigate the role of demographic, political, 425
economic, and other factors in predicting the probability of referenda occurring and the percent 426
voting yes as a means to better inform the allocation of their resources. Some groups including 427
The Conservation Fund and The Trust for Public Land already have manuals regarding how to 428
support conservation through ballet measures (see SI for more detail). As an illustration of how 429
statistical models could enhance current conservation efforts, we estimated a probit regression to 430
predict the probability of referenda occurring and a log-odds regression to predict the percent 431
voting yes (see Table SI-1, Figure SI-2, Figure SI–3). Our results, for example, suggest that 432
counties with higher percentages of the population with at least a bachelor’s degree have larger 433
number of voters voting yes. Previous econometric work related to open space referenda has 434
identified additional characteristics of a jurisdiction associated with having and passing a 435
referendum using a variety of different models (Kline 2006; Kotchen & Powers 2006; Nelson et 436
al. 2007; Banzhaf et al. 2010; Wu & Cutter 2011). 437
438
Given the diversity of RSS approaches in the literature, we use our formulations as a benchmark 439
for comparison and as a representation of “idealized” top-down planning, and do not view our 440
results as offering a management prescription (see the SI for a discussion of various elements to 441
account for when choosing an RSS-set). Some recent advances in RSS literature, for example, 442
formulate a return on investment approach that combines multiple attributes, such as 443
vulnerability to development, land prices, spatial contiguity and/or complementarity benefits (see 444
e.g., Murdoch et al. (2007), Polasky (2008), Underwood et al. (2009), Murdoch et al. (2010), 445
and Withey et al. (2012)). While our benchmark considers only the role of land prices, our 446
analysis can be tailored by organizations to particular conservation objectives and datasets to 447
make management decisions. 448
449
A possible concern about the potential use of ballot box measures for conservation is the 450
presence of taxonomic bias in the species covered. In RSS planning efforts, for example, the 451
species to use as a surrogate for biodiversity and the weights used in the objective are chosen 452
based on the objectives of the conservation exercise (Margules & Pressey 2000). In our RSS 453
analysis, we give all species, regardless of their taxa, equal weight. To check for potential 454
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mismatches between the set of species conserved in the two processes, we calculate the 455
percentage of each taxa-type covered in the RSS (base budget) and the ballot measures. Our 456
results suggest that any taxonomic bias may be small (~10% for plants and vertebrates, see 457
Figure SI-19) at least for county-level referenda. 458
459
In this paper, we focus on the potential role of county-level ballot initiatives. Further work 460
should integrate data on land set aside via municipal and state level initiatives and other 461
protected lands into RSS type planning exercises to get a more holistic view of all of the 462
conservation activities being undertaken in the U.S. A possible hypothesis for such an analysis 463
might be that the potential gains from ballot measures are lower, after taking into account all of 464
these other types of protection. In our efficiency gains experiment, for example, we do not 465
account for land held by Federal and state governments. Using information from the Protected 466
Area Database (US Geological Survey 2012), we test the robustness of our findings by 467
conducting auxiliary analysis with protected area data. We assume that species in a county are 468
covered if greater than 25% or 50% of the county land area is protected (achieves GAP 1 or GAP 469
2 status). We find there are still large efficiency gains available through consideration of the 470
land vote process. For example, the budget gains when holding the number of species constant 471
and when only considering land vote are ~45%, when only considering protected areas are 472
~13%, and when both past referenda and protected areas are considered jointly are ~52%. (See 473
SI for additional details of the robustness check.) 474
475
We leave for future work incorporating complexities such as spatial configurations of reserves, 476
institutional arrangements such as partnering with local land trusts, and interactions between 477
conservation efforts such as attraction and repulsion of new reserves to current reserves and 478
crowding out by government (Albers et al. 2008a; Albers et al. 2008b; Parker & Thurman 2011). 479
We also do not consider the anti-growth/development ballot measures, which are the other side 480
of the coin to the land conservation referenda (see, e.g., Gerber & Phillips (2005)). 481
482
483
484
485
17
Acknowledgements 486 487
We thank Andrew Chua for research assistance; Lynn Kutner, Data Management Coordinator at 488 NatureServe, for NatureServe species data; several colleagues, including Michael Bode, Martin Smith, 489 Alex Pfaff, Chris Timmins, Susan Harrison, and Andy Sih, as well as four anonymous referees, for 490 helpful comments and suggestions. Sanchirico acknowledges support from Agricultural Experimentation 491 Station project CA-D-ESP-7853-H. Kroetz acknowledges support of the National Institute for 492 Mathematical and Biological Synthesis at The University of Tennessee, Knoxville where she was a short-493 term visitor. 494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511 512
513
514 515
516
517
518
519
520
18
References 521 1. 522 Abbitt R.J.F., Scott J.M. & Wilcove D.S. (2000). The geography of vulnerability: incorporating species 523
geography and human development patterns into conservation planning. Biological 524 Conservation, 96, 169-175. 525
2. 526 Albers H.J., Ando A.W. & Batz M. (2008a). Patterns of multi-agent land conservation: Crowding in/out, 527
agglomeration, and policy. Resource and Energy Economics, 30, 492-508. 528 3. 529 Albers H.J., Ando A.W. & Chen X. (2008b). Spatial-econometric analysis of attraction and repulsion of 530
private conservation by public reserves. Journal of Environmental Economics and Management, 531 56, 33-49. 532
4. 533 Ando A., Camm J., Polasky S. & Solow A. (1998). Species Distributions, Land Values, and Efficient 534
Conservation. Science, 279, 2126-2128. 535 5. 536 Armsworth P.R., Fishborn I.S., Davies Z.G., Gilbert J., Leaver N. & Gaston K.J. (2012). The size, 537
conservation, and growth of biodiversity-conservation nonprofits. BioScience, 62. 538 6. 539 Balmford A., Gaston K.J., Blyth S., James A. & Kapos V. (2003). Global variation in terrestrial conservation 540
costs, conservation benefits, and unmet conservation needs. Proceedings of the National 541 Academy of Sciences, 100, 1046-1050. 542
7. 543 Banzhaf H.S., Oates W.E. & Sanchirico J.N. (2010). Success and design of local referenda for land 544
conservation. Journal of Policy Analysis and Management, 29, 769-798. 545 8. 546 Bode M., Probert W., Turner W.R., Wilson K.A. & Venter O. (2011). Conservation Planning with Multiple 547
Organizations and Objectives. Conservation Biology, 25, 295-304. 548 9. 549 Camm J.D., Polasky S., Solow A. & Csuti B. (1996). A note on optimal algorithms for reserve site 550
selection. Biological Conservation, 78, 353-355. 551 10. 552 Chang K. (2011). The 2010 National Land Trust Census Report. In: (eds. Aldrich R & Soto C). Land Trust 553
Alliance. 554 11. 555 Church R.L., Stoms D.M. & Davis F.W. (1996). Reserve selection as a maximal covering location problem. 556
Biological Conservation, 76, 105-112. 557 12. 558 Deacon R. & Shapiro P. (1975). Private preference for collective goods revealed through voting on 559
referenda. American Economic Review, 65, 943–955. 560 13. 561 Erasmus B.F.N., Freitag S., Gaston K.J., Erasmus B.H. & van Jaarsveld A.S. (1999). Scale and conservation 562
planning in the real world. Proceedings of the Royal Society of London. Series B: Biological 563 Sciences, 266, 315-319. 564
14. 565 Fuller R.A., McDonald-Madden E., Wilson K.A., Carwardine J., Grantham H.S., Watson J.E.M., Klein C.J., 566
Green D.C. & Possingham H.P. (2010). Replacing underperforming protected areas achieves 567 better conservation outcomes. Nature, 466, 365-367. 568
19
15. 569 Gerber E.R. & Phillips J.H. (2005). Evaluating the Effects of Direct Democracy on Public Policy California’s 570
Urban Growth Boundaries. American Politics Research, 33, 310-330. 571 16. 572 Heckman J.J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the 573
econometric society, 153-161. 574 17. 575 Jordan N., Boody G., Broussard W., Glover J.D., Keeney D., McCown B.H., McIsaac G., Muller M., Murray 576
H., Neal J., Pansing C., Turner R.E., Warner K. & Wyse D. (2007). Sustainable Development of the 577 Agricultural Bio-Economy. Science, 316, 1570-1571. 578
18. 579 Kark S., Levin N., Grantham H.S. & Possingham H.P. (2009). Between-country collaboration and 580
consideration of costs increase conservation planning efficiency in the Mediterranean Basin. 581 Proceedings of the National Academy of Sciences, 106, 15368-15373. 582
19. 583 Kline J.D. (2006). Public Demand for Preserving Local Open Space. Society & Natural Resources: An 584
International Journal, 19, 645 - 659. 585 20. 586 Kotchen M.J. & Powers S.M. (2006). Explaining the appearance and success of voter referenda for open-587
space conservation. Journal of Environmental Economics and Management, 52, 373-390. 588 21. 589 Mace G.M., Balmford A., Boitani L., Cowlishaw G., Dobson A., Faith D., Gaston K., Humphries C., Vane-590
Wright R. & Williams P. (2000). It's time to work together and stop duplicating conservation 591 efforts…. Nature, 405, 393-393. 592
22. 593 Margules C.R. & Pressey R.L. (2000). Systematic conservation planning. Nature, 405, 243-253. 594 23. 595 McCarthy D.P., Donald P.F., Scharlemann J.P.W., Buchanan G.M., Balmford A., Green J.M.H., Bennun 596
L.A., Burgess N.D., Fishpool L.D.C., Garnett S.T., Leonard D.L., Maloney R.F., Morling P., Schaefer 597 H.M., Symes A., Wiedenfeld D.A. & Butchart S.H.M. (2012). Financial Costs of Meeting Global 598 Biodiversity Conservation Targets: Current Spending and Unmet Needs. Science, 338, 946-949. 599
24. 600 Meretsky V.J., Maguire L.A., Davis F.W., Stoms D.M., Scott J.M., Figg D., Goble D.D., Griffith B., Henke 601
S.E. & Vaughn J. (2012). A State-Based National Network for Effective Wildlife Conservation. 602 BioScience, 62, 970-976. 603
25. 604 Murdoch W., Polasky S., Wilson K.A., Possingham H.P., Kareiva P. & Shaw R. (2007). Maximizing return 605
on investment in conservation. Biological Conservation, 139, 375-388. 606 26. 607 Murdoch W., Ranganathan J., Polasky S. & Regetz J. (2010). Using return on investment to maximize 608
conservation effectiveness in Argentine grasslands. Proceedings of the National Academy of 609 Sciences, 107, 20855-20862. 610
27. 611 Naidoo R., Balmford A., Ferraro P.J., Polasky S., Ricketts T.H. & Rouget M. (2006). Integrating economic 612
costs into conservation planning. Trends in Ecology & Evolution, 21, 681-687. 613 28. 614
20
Nelson E., Uwasu M. & Polasky S. (2007). Voting on open space: What explains the appearance and 615 support of municipal-level open space conservation referenda in the United States? Ecological 616 Economics, 62, 580-593. 617
29. 618 Önal H. & Briers R.A. (2002). Incorporating spatial criteria in optimum reserve network selection. 619
Proceedings of the Royal Society of London. Series B: Biological Sciences, 269, 2437-2441. 620 30. 621 Parker D.P. & Thurman W.N. (2011). Crowding Out Open Space: The Effects of Federal Land Programs on 622
Private Land Trust Conservation. Land Economics, 87, 202-222. 623 31. 624 Polasky S. (2008). Why conservation planning needs socioeconomic data. Proceedings of the National 625
Academy of Sciences, 105, 6505-6506. 626 32. 627 Press D. (2002). Saving open space: The politics of preservation in California. University of California 628
Press, Berkeley, CA. 629 33. 630 Pressey R.L. (1994). Ad Hoc Reservations: Forward or Backward Steps in Developing Representative 631
Reserve Systems? Conservation Biology, 8, 662-668. 632 34. 633 Sarkar S., Pressey R.L., Faith D.P., Margules C.R., Fuller T., Stoms D.M., Moffett A., Wilson K.A., Williams 634
K.J. & Williams P.H. (2006). Biodiversity conservation planning tools: present status and 635 challenges for the future. Annu. Rev. Environ. Resour., 31, 123-159. 636
35. 637 Stein B.A., Kutner L.S. & Adams J.S. (2000). Precious Heritage: The Status of Biodiversity in the United 638
States. In. Oxford University Press, Inc. New York, New York. 639 36. 640 Sundberg J.O. (2006). Private Provision of a Public Good: Land Trust Membership. Land Economics, 82, 641
353-366. 642 37. 643 Trust for Public Land (2012). Landvote Database. Trust for Public Land. 644 38. 645 Underhill L.G. (1994). Optimal and suboptimal reserve selection algorithms. Biological Conservation, 70, 646
85-87. 647 39. 648 Underwood E.C., Klausmeyer K.R., Morrison S.A., Bode M. & Shaw M.R. (2009). Evaluating conservation 649
spending for species return: A retrospective analysis in California. Conservation Letters, 2, 130-650 137. 651
40. 652 US Geological Survey G.A.P.G. (2012). November 2012 Protected Areas Database of the United States 653
(PADUS), version 1.3 Combined Feature Class. 654 41. 655 Wilson K.A., Carwardine J. & Possingham H.P. (2009). Setting Conservation Priorities. Annals of the New 656
York Academy of Sciences, 1162, 237-264. 657 42. 658 Withey J.C., Lawler J.J., Polasky S., Plantinga A.J., Nelson E.J., Kareiva P., Wilsey C.B., Schloss C.A., 659
Nogeire T.M., Ruesch A., Ramos J. & Reid W. (2012). Maximising return on conservation 660 investment in the conterminous USA. Ecology Letters, 15, 1249-1256. 661
43. 662
21
Wu X. & Cutter B. (2011). Who votes for public environmental goods in California?: Evidence from a 663 spatial analysis of voting for environmental ballot measures. Ecological Economics, 70, 554-563. 664
665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709
22
Tables 710
711
Table 1: Summary statistics 712
Median Levels
All
counties
No successful
referenda
At least one
successful
referendum ES RSS G1G2 RSS
Federal Endangered Species 3 2 4 8 6
NatureServe G1G2 Species 1 1 3 10 11
Median Household Income ($1,000s) 33.69 33.23 48.66 31.79 31.54
Median Home Value ($1,000s) 71.80 70.40 140.00 80.10 78.50
Percent in Poverty 15.08 14.78 26.96 12.64 11.98
Percent Age > 65 14.43 14.54 11.38 13.55 14.13
Percent Age < 18 25.34 2534 25.06 25.57 25.66
Percent No High School Degree 20.80 21.10 13.80 22.15 21.80
Percent Bachelor's Degree 14.40 14.10 28.50 15.15 15.20
Population Density 42.21 39.73 435.82 20.50 11.08
Farmland Price per Acre $1,668 $1,611 $4,485 $1,183 $763 Note: Counties are categorized according to whether the county has held at least one successful referendum from 713 1988-2006 and whether or not it was selected via RSS at the base budget that is ~14% of the amount needed to 714 cover all G1G2 species and ~33% of the amount need to cover all ES species (see Methods and SI). 715 716
717
718
719
720
721
722
723
724
725
726
727
728
23
Figures 729
730
Figure 1: Comparison of counties with past successful referenda and optimal RSS with 731
G1G2 species. This figure shows the overlap between the counties with prior successful referenda between 1988 732
and 2006, and the results of the RSS algorithm. The RSS results are based on maximizing the number of G1G2 733
species covered subject to a budget. We use a base budget similar to that of Ando et al. (1998) but adjusted to 734
account for differences in the cost of farmland. The base budget represents ~14% of the total budget needed to 735
cover all G1G2 species (see Methods). 736
737
738
739
740
741
742
743
744
745
24
Figure 2: Relationship between G1G2 species covered and predicted probability of 746
referendum success. We calculate, for counties with a predicted probability of a successful referendum greater 747
than the threshold probability, the number of G1G2 species covered under two assumptions: (1) species covered by 748
counties with prior successful referenda are included in the count; and (2) species covered by counties with prior 749
successful referenda are not included in the count. 750
751
752
753
754
755
756
757
758
25
Figure 3: Comparison of counties predicted to have future successful referenda and our 759
benchmark (RSS using G1G2 species data and base budget). The figure shows an overlay of the 760
RSS results on the predicted probability of having a successful referendum, by percentile group, along with the 761
counties that have had successful referenda from 1988-2006. The probabilities of holding a successful referendum 762
associated with the percentile groups are as follows: 75th percentile (1% or less), 76th-80th
(1-2%), 81st-85th (2-763
3%), 86th-90th percentiles (3-5%), 91th-95th (5-13%), 95th-100th (13% or greater). 764
765
766
767
768
769
770
771
772
26
Figure 4: Efficiency gains for G1G2 species covered. We solve a series of RSS problems maximizing 773
the number of species covered, but varying the budget. In panel A, we plot the species covered for each budget 774
under two scenarios. In our Without Ballot Measures scenario, a site must be purchased to preserve it. In our With 775
Ballot Measures scenario species in sites with successful referenda from 1988-2006 are preserved for free and all 776
other sites must be purchased to be preserved. We present the species as a proportion of the total number of G1G2 777
species and the budget as a percentage of the total budget required to cover all G1G2 species. Panel B represents the 778
changes in the set of sites chosen by the RSS planner when the ballot measures are incorporated directly into the site 779
selection problem at the base budget. In particular, the map corresponds to the gains in species covered labeled in 780
panel A. 781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
27
A.
B.
803