Authors: Jacopo Cerri, Giovanni Batisti, Marco Ferretti, Marco Zaccaroni, Sandro Bertolino. European Journal of Wildlife Research (2018) 64: 21
Title: Hunters' preferences for engaging in control programmes of introduced Eastern cottontails in Italy: a factorial survey approach.
Authors:
Jacopo Cerri1, Scuola Superiore Sant’Anna, Istituto di Management, Piazza Martiri della Libertà 33, 56127 Pisa, Italy;
Giovanni Batisti2, Università degli Studi di Firenze, Scuola di Agraria, Piazzale delle Cascine 18, 50144 Firenze, Italy;
Marco Ferretti3, Regione Toscana, Corso Gramsci 110, 51100 Pistoia, Italy;
Marco Zaccaroni2, Università degli Studi di Firenze, Dipartimento di Biologia, Via Madonna del Piano 6, 50019 Sesto Fiorentino, Italy;
Sandro Bertolino4, Università degli Studi di Torino, Dipartimento di Scienze della Vita e Biologia dei Sistemi, Via Accademia Albertina 13, 10123 Torino, Italy;
Corresponding author:
Jacopo Cerri, Scuola Superiore Sant’Anna, Istituto di Management, Piazza Martiri della Libertà 33, 56127 Pisa, Italy; Telephone: (+39)3395692346. E-mail address: j.cerri@santannapisa.it. Orcid: 0000-0001-5030-0376.
Abstract
In Europe invasive alien mammals are often controlled by voluntary hunters. However, no research investigated if control operations could be entirely performed by volunteers, nor how to maximize their recruitment. By using the Eastern cottontail (Sylvilagus floridanus) in Central Italy as a case study, we carried out a factorial survey over a sample of hunters (n=134), exploring which attributes influenced their hypothetical participation to control programs for cottontails.
Hunters were more willing to engage in control schemes if these included shooting sessions, not trapping. Moreover, they would have been more prone to control cottontails if they had received evidence of cottontail impacts over native wildlife or local crops. The geographical scale of the management plan, its goals and required efforts did not have any effect over the evaluation of management scenarios. As many European control schemes for invasive alien mammals are carried out in contexts where shooting is unfeasible, like urbanized neighbourhoods, our findings show that agencies cannot entirely rely on volunteers and they should complement their limitations with full-time staff. Moreover, if wildlife agencies want to recruit volunteers, they should provide adequate information about the environmental and social impacts of invasive alien mammals.
Keywords: invasive mammals; biological invasions; Sylvilagus floridanus; factorial surveys; human-dimensions; hunters 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
Introduction
Invasive Alien Species (IAS) are those species that arrive and establish viable populations in new areas, becoming major pests with considerable socio-economic and environmental impacts. (Pimentel 2011; Pejchar and Mooney 2009; Vilà and Hulme 2017). Many mammal species provide textbook examples of IAS (Barrios-Garcia and Ballari 2012; Capizzi et al. 2014; Nimmo and Miller 2007; Parker et al. 2012). However, managing their populations is particularly complex, because of their iconicity (McNeely 2001), some ethical ambiguities (Eggleston et al. 2003; Cowan and Warburton 2011), and because control programs require prolonged efforts and a vast geographical scale implying trans-mandate and trans-boundary policies (DiTomaso et al. 2017; Pluess et al. 2012).
These aspects are critical for Europe, where a dedicated Regulation (EU Regulation n. 1143/2014) was recently approved to counteract biological invasions (Tollington et al. 2017), obliging member states to control IAS. The Regulation did not receive any specific funding for its implementation and faced many practical obstacles, caused by national differences in the legislation about animal welfare and the numerical control of wildlife. The lack of adequate funding, coupled with the current economic crisis, had a general detrimental effect: making impossible for most national environmental agencies to recruit qualified full-time professionals in their staff, to kill invasive mammals according to a top-down planning. Agencies reacted to this limitation by delegating these tasks to hunters, despite this choice is shortsighted, because of their numerical decline in Europe (Massei et al. 2015). In some member states (e.g. Norway, Stien and Hausner 2017), hunting can be either a leisure or a profession, as hunters could receive a monetary reward for their participation to the numerical control of wildlife. This enabled policymakers to set up bounty-based mechanisms for incentivizing professional hunting of pest species, like in Northern America (Witmer et al. 2007) or Australia (Saunders et al. 2010). In other states, like Italy, professional hunting does not exist and hunters who take part to numerical control schemes cannot be rewarded for their volunteering. Therefore, two challenges are emerging in the management of invasive mammals in Europe. Firstly, environmental agencies must be able to design management programs attractive to voluntary hunters. Agencies have to understand how to trigger hunter participation to control programs and how to foster their long-term commitment, by designing programs endorsed bu hunters. This is of uttermost importance in those member states, like Italy, where rewarding hunters is not possible, but also in the rest of Europe: given the current economic downturn, the use of unpaid volunteers will probably increase. Without accounting for hunters preferences and their recreational needs, voluntary-based initiatives will not succeed. The second challenge is a consequence of the first one: while the use of voluntary hunters is likely to increase, wildlife agencies should also evaluate to whether they could replace full-time staff in numerical control activities, without undermining long-term management goals.
This research aims to investigate the first of these two challenges and to provide useful suggestions about the second one, by considering voluntary hunter engagement in the management of the Eastern cottontail (Sylvilagus floridanus), a North American lagomorph, in Italy. We focused on the province of Pistoia, a neo-colonization area for cottontails, analyzing hunters preferences for various hypothetical control scenarios, in the attempt to identify those factors that could make a numerical control program for cottontails able to recruit volunteers among the hunting community, as well as the potential drawbacks of the various management options.
Methods Study Area 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103
The study area includes the province of Pistoia, located in the Tuscany region, Italy (Fig. 1). In the study area, a total 5457 hunters purchased a license for the 2016 season and XXXX of them have a permit for carrying out numerical control of wildlife. Professional hunting does not exist in Italy and, during the hunting season, hunters can access public and private properties over the majority of regional agroforestry surface, with the exception of protected areas and private hunting estates. Hunting is regulated by the Regional wildlife agency and management actions are implemented by local hunting districts, which represent hunting associations and have a sub-provincial scale. Traditional hunting in the study area targets migratory birds and small game, however, it is declining in favor of wild ungulate hunting and stalking. Control schemes for pest species are carried out both in protected and in non-protected areas. Most of them are carried out through shooting. but trapping followed by euthanasia is also locally adopted when shooting is unfeasible (e.g. urbanized areas). The Regional wildlife agency does not possess enough personnel to undergo numerical control operations, and almost the totality of them are performed by voluntary hunters, who obtained a special permit after having attended a course, plus a written and an oral examination about wildlife management. At the time of the study, the Regional agency did not disclose any information about the age, nor about the preferred form of hunting, of these hunters, due to privacy reasons. However, practical evidence suggests that those they are an heterogeneous group, including both young small game hunters, as well as deer stalkers and wild boar hunters. It is plausible that they might constitute a particular, highly-motivated, segment of the hunting community. Voluntary hunters do not receive any economic reward for their volunteering, but can keep the killed animals for private consumption. Four different permits for numerical control exist, as the jurisdictional nature of the cause behind the numerical control might differ. However, in practice, numerical control initiatives do not differ in their modality, their causes often overlap and hunters generally possess more than one of these permits at the same time.
Cottontails were introduced in North-Western Italy in the 1960’s, as a game species, then they progressively increased their distribution in Northern and Central Italy, due to a combination of deliberate introductions and natural dispersal (Bertolino et al. 2011), arriving in the study area during the late 1990s and colonizing rural and urbanized environments. Hunters are the main vector for cottontail dispersal, as some of them illegally breed and release cottontails in nature, deeming cottontails to be a subspecies of the European rabbit (Oryctolagus cuniculus). However, cottontails are regarded as a secondary game species and are not replacing native European hares (Lepus europaeus) in hunter preferences (Cerri et al. 2016). Recreational cottontail hunting is authorized from September to December, outside of protected areas, and it is carried out with the help of hounds. Moreover, as cottontails affect native European hare populations by increasing the predatory impact of foxes, and transmit zoonoses (Cerri et al. 2017; Zanet et al. 2013), the Regional wildlife agency authorizes their numerical control at specific sites, both within and outside of protected areas. Cottontails are the only invasive mammal species, which is subjected to numerical control in the study area. Control schemes generally include nocturnal shooting from a vehicle or diurnal shooting with the help of dogs. Shooting sessions are organized by local hunting districts, who ask voluntary hunters to participate. In urbanized areas, cottontails could also be trapped and euthanized.
Survey administration and design
In our research a factorial survey approach was adopted (Auspurg and Hinz 2014; Wallander 2009). In factorial surveys, each respondent is asked to rate several hypothetical scenarios, the “vignettes”, depicting some hypothetical situations. These scenarios are defined by some characteristics, the “factors”, each one having a certain number of levels, expressing different attributes. Vignettes are generated through the cartesian product of the various factor levels and are randomly assigned to respondents. A typical vignette appeared as follows:
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“A control plan aimed at limiting the population of Eastern cottontail in the province of Pistoia is approved. The plan has the following attributes: At the end of the plan, the species has been mildly reduced in its numbers. Control operations are carried out every two weeks. The species transmits diseases to humans. The control plan contemplates the suppression of animals through shooting. The plan lasts three years. Control operations are carried out at a single hotspot. Would you participate to such of a plan?”
In this vignette, factors included: the goal of the management plan, the frequency of its control operations, the impact of cottontails justifying the management plan, the modality through which cottontails were killed, the duration of the plan and its geographical scale. Vignettes were characterized by 6 factors each one having a maximum of 4 levels, to minimize recall bias and cognitive effort during completion, meanwhile maximizing internal validity (Auspurg and Hinz 2014).
To maximize vignette plausibility, factors and their levels were chosen based on available literature and practical experience from available control programs in the study area. Moreover, to achieve our research goals, we selected those attributes that could characterize a management program for the Eastern cottontail and that were deemed to be important to influence hunter participation (Table 1). The first factor was the goal of the management plan, an important aspect of invasive mammals control, characterizing the potential long-term benefits as well as the costs of a certain management option. The second factor was the impact of the target species, a necessary characteristic to define stakeholders’ risk perception (Estévez et al. 2015; García-Llorente et al. 2008) and the benefits deriving from a management control program (Leung et al. 2002; Courchamp et al. 2017; DiTomaso et al. 2017), as well as the information that should be adopted in stakeholder engagement (Ford-Thompson et al. 2012). The third factor was the method adopted for numerical control of cottontails, which have different implications in terms of animal welfare and also affects the leisure experience of voluntary hunters (Treves and Naughton-Treves 2005; Littin et al. 2004; Courchamp et al. 2017; Liordos et al. 2017; Dubois et al. 2017). The fourth factor was the duration of the management plan, a crucial aspect affecting stakeholder engagement in wildlife management, by acting on the credibility of the management program itself as well as on its costs (Santo et al. 2015; Holmes et al. 2015). The fifth factor was the effort required to volunteers to perform control operations, notably their frequency, which might constrain their long-scale engagement in wildlife management program. The sixth factor was the geographical scale of the management plan, a major attribute affecting its credibility among volunteers and required efforts (Robertson et al. 2017).
In a factorial survey, respondents do not have to evaluate the entire spectrum of vignettes but a sample of vignettes could be evaluated instead, allowing researchers to make inference about the vignette universe. In this research, a D-efficient sample (Dülmer 2007, 2016) of 804 vignettes was evaluated by a sample of hunters who had a permit for volunteering in numerical control schemes or were about to obtain one, after having attended the course (n=134). As no information about the demographics, nor the hunting preferences, of voluntary hunters was available, we could not formally assess the statistical representativeness of our sample. However, as our sampling procedure did not adopted self-selecting approaches, like snowballing, but captured different age cohorts of voluntary hunters, and covered a significant proportion of those existing in the study area (XX%), we deemed it to be suitable for our research purpose. Paper and pencil surveys were administered at the local wildlife office or during the training courses and they took approximately 5 minutes to complete. Before the final wave of questionnaires, factorial surveys were piloted over a small subsample (n=10) of respondents. Each survey contained a short introductory section, explaining the aims of the research and reminding that the questionnaire was anonymous, then asking respondents to provide some personal details to characterize our sample: age, gender, the level of education, their hunting district, the game species they hunt and which of the four Regional authorization for wildlife control programs they had. After this initial section, hunters evaluated 6 vignettes, indicating whether they would have participated to it, through a dichotomous answer. 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206
Statistical analysis
Various types of contrasts were adopted to compare factor levels for the various vignette attributes (Table 1). In factorial surveys, as multiple vignettes are evaluated by each respondent, observations are not independent and require multilevel modeling to analyze the effect of the various attributes over respondents’ evaluation (Auspurg and Hinz 2014). Therefore, we fit a Generalized Linear Mixed Model with binomial distribution of the error, a log link and a random intercept term (Hox et al. 2010). Our full model included all the factors characterizing a vignette, three interaction terms expressing the interplay between the frequency of control operations, the duration of the program and the impact of cottontails, and the two-way interaction between these three factors.
Backwise selection based on likelihood-ratio testing was adopted to select the best candidate model and visual inspection of model residuals was adopted to assess any potential violation of model assumptions. Kolmogorov-Smirnov test compared model residual against a uniform distribution, to detect heteroscedasticity. Furthermore, a nonparametric simulation-based test, comparing the interquantile range of scaled model residuals against the interquantile range expected under uniform approximate model deviance, was adopted to detect overdispersion (Hartig 2016). The proportion of variance in the data, explained by our model, was measured through Nakagawa’s R2 (Nakagawa and Schielzeth 2013). Statistical analyses were carried out with the software R (R Core Team 2017), notably with the packages lme4, DHARMa and MuMIn (Bates et al. 2014; Hartig 2016; Bartoń 2013).
Results
Piloting did not reveal any issue in factorial survey understanding by respondents and we retained the pilot questionnaires for data analysis. Our final model explained 72% of total variance in the data, no anomalous pattern emerged from residual analysis (S1), the Kolmogorov-Smirnov test did not reveal heteroscedasticity in model residuals (D = 0.026, p = 0.66), and the nonparametric test did not detect overdispersion (dispersion = 0.977, p = 0.75). P-values and likelihood ratio testing discarded both the scale and the management goal of the control program, not deeming them to be significant variables.
Our findings (Table 2) show that the various impacts of cottontails stimulated hunters to engage in control initiatives, and also that they differed in their effect over hunter participation, at least compared to a ‘no damage’ condition. A situation where cottontails impacted native wildlife made hunters more prone to engage with respect to a ‘no damage’ scenario, than a situation where cottontails damaged crops and than a situation where cottontails transmitted disease to humans. Moreover, hunters were more favorable to participate to the control program, if this would enable them to shoot cottontails, rather than trapping them.
Two other factors were retained in the final model, despite they were not significant, suggesting their importance. The frequency of control operations had a marginal, non-linear, effect over hunting participation: biweekly interventions were preferred over monthly ones, but participation declined n case of one or more interventions per week. A similar non-linear pattern was observed for the duration of the management program: hunters preferred 3-years long programs, over 1-year long ones, yet their preferences dropped again when program duration reached 5-years.
All the three one-way interaction terms, as well as the two-way one, had some significant combination of levels. For reasons of clarity, we did not report the entire number of factor combinations in Table 2, but yet only significant ones.
Discussion
This research shows how the various characteristics of a numerical control program for an invasive alien mammals could affect its endorsement by voluntary hunters. It also discusses the potential consequences of a low endorsement by voluntary hunters in a context, like Italy, where no 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258
economic incentive can be offered for the numerical control of wildlife, and where wildlife agencies are using them to replace full-time professionals.
In the study area, while a proportion of the hunting community carries out the numerical control schemes for invasive cottontails, another one deliberately releases the species in nature. We believe that our sample of hunters did not include those who release cottontails in nature, for various reasons. This segment of the hunting community is likely to hold a conservative perspective about small game management, based on restocking, rather than a perspective based on a proactive approach. This will not motivate them to engage in the numerical control of cottontails, which is demanding and lacks of socialization with other hunters, not providing an alternative to small game hunting. On the other hand, motivated hunters who engage in the numerical control of wildlife are likely to support the control of cottontails, as their voluntary engagement might be partly caused by individual beliefs about the decline of small game species, like the European hare. These beliefs were found to be correlated with a general support towards the eradication of invasive cottontails (Cerri et al. 2016).
The impact of our invasive target species, the Eastern cottontail, seems to be fundamental in influencing hunter endorsement of a control program. This aspect is important because, while much research about IAS management measured the effect of awareness about the species impact over stakeholder engagement, few of it actually explored how the various impacts could motivate stakeholders differently in carrying out their control. Our findings seem to suggest that hunters are more prone to control cottontails when these have an impact over game species. Alternatively, they can also be engaged in IAS control when the species is likely to cause crop damaging. We therefore recommend wildlife managers and environmental agencies to invest on information provisioning when designing control or eradication campaigns for invasive mammal species that engage voluntary hunters, as being aware of IAS impacts in their introduction environments seems to be a strong driver of their participation to control initiatives. While this agrees with previous evidence from human dimension and IAS management studies, from our experience we can tell that in many cases Italian hunters are recruited for wildlife control initiatives, even when involving invasive mammals, in a top-down process, without being provided any clear information about the impacts of the targeted species. Future control initiatives should avoid this practice. Of course, the way we measured the effect of the different impacts of cottontails had some limitations. For example, we compared the various impacts against a ‘no damage’ situation. While this approach was very useful to show to what extent the various impacts affected hunters evaluation of the scenarios, it did not tell us, for example, why the risk of disease transmission to humans had a somehow weaker effect compared to the other impacts. Again, it was unclear whether the effect of cottontails harming native wildlife motivated hunters to support a management scenario by acting on their self-interest, their emotions or their beliefs about wildlife conservation. Future research adopting qualitative interviews aimed at mapping individual beliefs and attitudinal scales, measuring both individual evaluations about the various impacts and their perceived likelihood, could be helpful to answer these questions.
Our findings also indicate that voluntary hunters in the study area cannot entirely replace full-time staff of the Regional wildlife agency, at least for controlling invasive cottontails. The first reason behind this assumption is that hunters simply prefer to shoot cottontails, rather trapping them: when a vignette described a control scheme based on trapping, respondents had a lower probability to declare their participation to that scenario. The main reason for preferring shooting could lie in the fact that trapping is not part of the hunting tradition of the study area, and therefore, shooting could be more satisfying for hunters, even when volunteering. This is a considerable limitation to the numerical control of cottontails, which colonized urbanized environments where shooting is unfeasible for safety reasons. In these contexts the participation of voluntary hunters to numerical control initiatives is likely to drop, undermining the complete removal of cottontails and creating source-sink dynamics (Travis and Park 2004). The second reason is the non-linear effect of control frequency and duration over hunters preferences. Hunters stated participation to vignettes peaks at intermediate levels of control frequency and when control programs are reasonably long: when they 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310
are asked to engage in control operations more than one time per week, or when control programs exceed three years of duration, endorsement drops. This is a major issue
for the numerical control of invasive cottontails in the study area, which might last for some years due to their geographical distribution and their low detectability.
Our findings also claim for some further research. A fine-grain interplay seems to occur between the required effort of numerical control, in terms of frequency of interventions, the duration of numerical control schemes and the impact of cottontails. Factorial surveys might not be the best approach to address it, as interpreting interaction terms between factors is quite complex, especially when their number of levels is not binary. Probably, analyzing attendance data about control schemes might be more helpful in this regard. Secondly, as we noticed that both potential impacts of cottontails on native wildlife and crops had a similar marginal effect over hunters engagement, we believe that hunters might not differentiate between the native or invasive nature of mammal species (Cerri et al. 2016), rather they would regard more to their impacts. Because the invasive status of many alien mammals is the main cause advocated by conservationists and policymakers to justify large-scale and long-term management programs (Bertolino and Viterbi 2010; Gurnell et al. 2014) and because value change about invasive species could foster enduring and effective hunter engagement (Owens 2017), we recommend future social science research assessing if information about IAS and biological invasions can be provided to hunters, how this information can be tailored to the various segments of the hunting community and to what extent providing information could lead to behavioral change.
Conclusions
This research pinpoints various important aspects related to the use of voluntary hunters for the numerical control of invasive cottontails in Italy. By adopting a flexible and robust quasi-experimental approach, this research shows that control schemes for cottontails were more likely to be endorsed when information about cottontail impact over native croplands was provided, compared to a ‘no damage’ scenario. Endorsement was even stronger for those scenarios where cottontails affected native wildlife. This indicates that providing information about IAS and their impacts over introduction ecosystems is helpful to engage hunters in control initiatives.
Our findings also indicate that replacing full-staff professionals of wildlife agencies with voluntary hunters could undermine the long-term management of invasive mammals in Italy. In our case study, voluntary hunters were less likely to endorse those control schemes based on trapping, as they preferred to shoot cottontails, in line with their hunting tradition. This could hamper numerical control of cottontails in urbanized areas, where shooting is unfeasible. Moreover, hunters did not endorse those management schemes characterized by a very long timespan, or imposing them a high frequency of interventions.
Since IAS eradication and control projects are expected to increase in the future, while the economic resources are not due to the current economic downturn, the direct involvement of voluntary groups, both in control operations and in other phases of the management plan, could be fundamental. However, to maximize the effectiveness of voluntary-based management of invasive alien mammals, we recommend wildlife managers to assess the level of engagement of volunteers, via factorial surveys, to complement them with available staff and to provide adequate information about IAS and their impacts, increasing hunter commitment.
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Table 1. Response variable and factor levels adopted in the factorial surveys, altogether with the terminology adopted for each factor level.
Approximate final position: after the “Statistical analysis” subsection, before the results (line 226). Variable Levels Terminology adopted in the vignette Rationale Response variable
Participation to the control program
Would you participate to such of a plan?
-Vignette factors
Impact of cottontails
No clear
impact The species does not cause any damaging.
Bertolino et al. 2011; Bertolino, Cordero di Montezemolo and Perrone 2013; Vidus-Rosin et al. 2011; Cerri, Ferretti and Bertolino 2017; Williams and Short 2013; Zanet et al. 2013 Native
wildlife The species threatens native wildlife. Crop
damaging The species damages croplands. Disease
transmission The species transmits diseases to humans.
Method of numerical control
Trapping and euthanasia
The control plan contemplates the capture of animals through trapping, then the anesthesia and their suppression. Courchamp et al. 2017; Dubois et al. 2017; Littin et al. 2004; Liordos et al. 2017; Treves and Naughton-Treves 2005; Shooting
The control plan contemplates the suppression of animals through shooting.
Effort required by control operations (frequency)
Monthly Control operations are carried out once per month.
Practical evidence about the study area.
Biweekly Control operations are carried out every two weeks. Weekly Control operations are carried out once per week. 2 times per
week Control operations are carried out two times per week. Duration of
the
management plan
1 year The plan lasts one year.
Holmes et al. 2015; Santo et al. 2015
3 years The plan lasts three years. 5 years The plan lasts five years. Scale of the
management plan
Single
hotspot Control operations are carried out in a single hotspot.
Robertson et al. 2017
Municipality Control operations are carried out at the municipal 470
471 472 473 474
scale. Management district
Control operations are carried out at the management district scale.
Province Control operations are carried out at the provincial scale. Goal of the management plan Mild numerical reduction
At the end of the plan, the species has been mildly reduced in its numbers.
Parkes et al. 2017; Parkes and Panetta 2009
Strong numerical reduction
At the end of the plan, the species has been strongly reduced in its numbers.
Eradication At the end of the plan, the species is not present anymore in the area.
Table 2. Effect of the various factors over the respondents’ participation to the hypothetical scenarios, espressed in log-odds. Reference levels are specified, for treatment contrasts and first levels for Helmert contrasts. Non-significant interaction terms are not displayed, to avoid redundancy.
Approximate final position: after the “Results” subsection, before the Discussion (line 252).
Variable Levels Coef. S.E (95%) p-value Contrasts
Intercept - -2.722 0.417 ***
-Impact of cottontails
No clear impact (reference level)
Native wildlife reduction 1.281 0.358 *** Treatment
Crop damaging 1.172 0.371 *** Treatment
Disease transmission 0.884 0.356 ** Treatment
Method of numerical control
Trapping and euthanasia (reference level)
Shooting 1.330 0.251 *** Treatment
Effort required by control operations (frequency)
Monthly (first level)
Biweekly 0.144 0.331 n.s Helmert
Weekly -0.537 0.236 ** Helmert
2 times per week 0.037 0.140 n.s Helmert
Duration of the management plan
1 year (first level)
3 years 0.386 0.288 n.s Helmert
5 years -0.350 0.214 n.s Helmert
Native wildlife reduction :
Weekly 0.676 0.303 **
-Crop damaging : Weekly 0.533 0.306 *
-Weekly : 3 years -0.488 0.224 **
-Crop damaging : Weekly : 3
years 0.722 0.347 **
-Disease transmission :
Weekly : 5 years 0.442 0.248 *
-N. vignettes =
802 N. respondents = 134 AIC=889.868 BIC=1124.223 ICC=0.69 475 476 477 478 479 480 481
R2(fixed
term)=0.11 R2(random term)=0.72 LogLik=-394.934 Deviance=789.9 df.resid=752 482
