The role of incentive-based instruments and social equity in conservation conflict interventions

• Abstract
• Introduction
• Methods
  • Study area
  • Game design
  • Data collection
  • Data analysis
• Results
  • Household socioeconomic and participant attitudinal characteristics
  • Effect of game treatments and household characteristics on farmers’ predisposition to kill elephants
  • Effect of game treatments and household characteristics on farmers’ predisposition to provide habitats for elephants
  • Effect of other game conditions on game outcomes
• Discussion
  • Predictors of farmer land-use decisions
  • Games as tools to predict and manage conservation conflicts
  • Implications for managing conservation conflicts
  • Conclusion
• Responses to this Article
• Author Contributions Statement
• Acknowledgments
• Data Availability
• Literature Cited

INTRODUCTION

Conflicts over conservation endeavors (or “conservation conflicts”) not only undermine effective conservation, but also hamper sustainable development (Redpath et al. 2013). Many such conflicts involve species of conservation concern that damage crops or prey on livestock, and are often killed in retaliation by affected farmers. Such problems are commonly framed as human–wildlife conflicts (Woodroffe et al. 2005). However, beneath the material manifestations of these impacts lie deeper and more complex social conflicts between different social groups (Dickman 2010, Peterson et al. 2010, Madden and McQuinn 2015, Hill et al. 2017). At the core of these conflicts is the involvement of multiple stakeholders with conflicting values and agendas (Redpath et al. 2013). If the non-material needs of affected stakeholders (e.g., farmers) are not also adequately considered, interventions to address wildlife impacts might fail to mitigate conservation conflicts through lack of engagement, uptake, and follow-through by farmers (Hill et al. 2017). For instance, increased concern over social equity among stakeholders has been associated with a decreased likelihood of finding solutions to biodiversity-related conflicts (Young et al. 2013, 2016a). For our purposes, equity may relate to: (1) recognition, i.e., the equitability of cost allocation across national conservation and development strategies; (2) procedural equity, which refers to participation in decision-making processes; and (3) distributive equity, which addresses the distribution of benefits and costs (McDermott et al. 2013). Given the complex nature of conservation conflicts, devising the best interventions to mitigate conflicts is a growing priority for policy makers (e.g., Young et al. 2016b, Mason et al. 2018).

We developed a highly interactive game to understand how farmers respond to alternative conflict intervention strategies in Gabon (Rakotonarivo et al. 2021b). Games have emerged as an effective means to engage stakeholders and enable player responses, mimicking real-world reactions through immersion (Redpath et al. 2018). They have been used in a wide range of contexts such as irrigation (Meinzen-Dick et al. 2016), fisheries and forests (Cardenas et al. 2013), and agriculture (Bell et al. 2016). Games can help to develop decision-making theory, to understand patterns in conflict, and to elucidate possible solutions for environmental issues (Redpath et al. 2018). Games have been used to foster more sustainable practices or transformative changes (e.g., Mayer et al. 2014, Rodela et al. 2019), as well as to test theoretical predictions of human behavior in various natural resource dilemmas (e.g., Cardenas et al. 2013, Janssen et al. 2010, Travers et al. 2011, Andersson et al. 2018, Rakotonarivo et al. 2021b). Here, we used a game as a low-cost and low-risk tool to engage farmers and investigate how they react to potential management strategies in a setting where real-life experiments would be impractical. Unlike many behavioral experiments, which commonly involve high levels of abstraction and simplified visual representation (Janssen et al. 2014, List and Price 2016), our game modeled ecologically relevant temporal and spatial dynamics at the landscape level using tablet computers and the Netlogo interface (Wilensky 1999).

Our game was framed around farmers’ land management decisions and crop-damaging elephants, a keystone and charismatic flagship species that symbolizes wildlife conservation in Asia and Africa. As iconic species, elephants attract tourists who contribute significantly to some range state economies (Naidoo et al. 2016). However, elephants can impose considerable social and financial costs on farmers by damaging crops, food stores, and water sources, thus impairing local farmers’ well-being (Mackenzie and Ahabyona 2012, Barua et al. 2013). In addition to poaching (Poulsen et al. 2017), land-use change, and habitat loss (Chartier et al. 2011), retaliatory killing of elephants poses a serious threat to the species’ survival. The increasing intensity of elephant-related conflicts highlights the pressing need to develop a better understanding of farmers’ decision-making and its underpinnings (Evans and Adams 2018, Shaffer et al. 2019).

Technical interventions to reduce agricultural damage by elephants at a local level tend to focus on physical and biological barriers such as fencing, guarding, and the use of repellents (Nyhus 2016, Pooley et al. 2017, Pozo et al. 2019). Economic instruments, either through compensation mechanisms for crop losses (Ravenelle and Nyhus 2017) or financial incentives that reward a specific conservation outcome, have also been suggested as effective solutions to conservation conflicts (White and Hanley 2016, Wilson et al. 2017). Incentive-based instruments also include agglomeration payments, which encourage spatial coordination of land set aside for conservation by offering additional payments to farmers enroling adjacent parcels in agri-environment schemes (Parkhurst and Shogren 2007). Little is known about the acceptability of various mitigation strategies to affected farmers or their effect on farmers’ decision-making about wildlife and land management.

Our aim was to examine the effects of three mitigation approaches on local farmers’ propensity to use lethal control or to support conservation interests through the provision of habitat for elephants: (1) support for elephant-deterrent techniques designed to offset costs constraining their adoption; (2) monetary incentives, a flat subsidy for pro-conservation land uses through the provision of elephant habitats; and (3) agglomeration bonuses, designed to encourage spatial coordination in the adoption of pro-conservation land uses. We explore the relationship between game outcomes and key socioeconomic and attitudinal factors, collected using accompanying household surveys. We expected participants who had stronger preferences for equity, those with positive perceptions of the well-being effects of elephants, and those who experienced lower levels of crop damage by elephants to be less likely to kill elephants and more likely to provide elephant habitats in the game. We also explored farmer motives using in-depth debriefing interviews with a subsample of participants and discuss how interactive games can help in addressing conservation conflicts across a wide range of settings.

METHODS

Study area

We conducted games in two rural areas of Gabon. These areas included all eight villages near Lopé National Park and within the World Heritage site associated with the park, which we refer to as “conservation-influenced villages”, and ten villages within production forests, which are referred to as “logging-influenced villages” (Fig. 1, Appendix 1). The two regions were chosen to cover a range of both exposure to crop damage and reliance on agriculture. Negative interactions between local farmers and forest elephants, Loxodonta cyclotis, are widespread at both sites. However, the protected area adjacent to the conservation villages might offer better protection for elephants, and more elephants in adjacent forests might lead to increased crop damage in these villages (Graham et al. 2010). The availability of alternative income from logging might also reduce reliance on agriculture in logging villages and hence lead to reduced capacity to protect fields from elephants. Elephants in Gabon, as in other African countries, are known to destroy an entire year’s worth of crops in a single visit and thus cause serious hardship to subsistence farmers (Fairet 2012). The rapid expansion of rural employment in logging concessions across Gabon (Laurance et al. 2006), compounded by high rural exodus (Fairet et al. 2014) and extremely low rural population density (0.2 inhabitants/km²; Laurance et al. 2006) have further led to a reduced capacity to protect fields.

To protect crops from elephants, local farmers use a range of traditional methods such as scarecrows, barriers, and cleared field perimeters (Fairet 2012, Walker 2012, Ngama et al. 2016). Shooting of problem elephants outside protected areas is implemented by the government if the village submits evidence of extensive crop damage (Fairet 2012). The legislation also includes the possibility of compensation for crop damage, but records are not available for the number of claims and compensations paid. Recently, the Gabonese government has provided funding to build electric fences around village farmlands near National Parks to deter elephants (Poole 2016). Only one village in the study area had benefited from community electric fencing at the time of the study, and a further three of the study villages have since received electric fencing.

Game design

We developed an interactive game played in groups of four participants using tablet computers linked via a mobile hotspot. The game was designed within Netlogo (Wilensky 1999), a multi-agent modeling environment, and adapted from NonCropshare, a coordination game for insect-based ecosystem services (Bell et al. 2013, 2016; Appendix 2). We incorporated both temporal and spatial dynamics: (1) resource availability at a given time t is dependent on decisions made previously (e.g., animal number decreases with killing effort), and (2) crop damage depends on the location or proximity of cropland to other land uses as well as neighboring farmers’ decisions (e.g., elephants are moving across the landscape, and intensive scaring in one farm might displace the problem elsewhere). These spatial and temporal dynamics positively influenced the game’s realism.

Game-play involves four participants (each representing one household) who make decisions on a digital farming landscape. Each participant acts on nine cells arranged in 3 × 3 contiguous square blocks (Fig. 2). Each game session consisted of six to eight rounds, intended to represent agricultural years. Communication between participants was permitted in all the sessions to mirror the conditions in which real-life incentive schemes operate. In each round, there were four options available to participants in each cell: (1) farm, (2) farm and scare elephants off the cell using non-lethal methods (e.g., physical or biological barriers, noise), (3) farm and shoot elephants in the cell (lethal control), or (4) lease the cell for elephant conservation (i.e., provide habitat for elephants). Each option had different costs and benefits and was assigned different parameter settings (Fig. 3).

At the start of each round, the default option on all 36 grid cells is farmland. Eighteen elephants are randomly distributed across the landscape cells with equal probability. Multiple elephants per cell are permitted. In each round, players select an option by tapping repeatedly on the cell and end their turn by “confirming” their choices when they are ready (Fig. 2). Damage occurs immediately on a cell if an elephant is present in a cell and is neither scared nor killed. If elephants are scared from a given cell, they reorient to other cells probabilistically based on cell weights. Elephant habitat cells have the highest weight and are nine times more likely to accommodate elephants chased from other cells than are farmed cells (Fig. 3). These habitats were described as buffer resources providing alternative food sources for elephants. However, providing elephant habitat means foregoing private yield for benefits shared by all four players, creating a public good dilemma. Scaring and killing have an immediate effect in the same round (e.g., if an elephant is killed, no damage is incurred) as well as a future effect because there are fewer elephants in future rounds (Fig. 3).

The minimum value score per cell is set to zero to avoid unrealistic negative values. Elephants left on any given cell decrease yield by 2 points. A habitat neighborhood effect is added to the game settings to reflect the likely increase in crop damage in farmlands that surround elephant habitats (Fig. 3). Similarly, non-lethal and lethal control methods are not equally effective: scaring displaces elephants with a probability of 0.8, and lethal shooting immediately removes elephants from the landscape with a probability of 0.3. Shooting costs much more than scaring to reflect the higher risks and dangers involved in killing elephants, as well as the costs of cartridges and guns. Killing systematically results in a lower payoff for any player given its lower cost-effectiveness ratio (0.3/2 vs. 0.8/1 for scaring; see Fig. 3), thus making it a dominated strategy in a one-shot game (i.e., shooting earns a player a smaller payoff than some other strategy, regardless of what others do; see Appendix 3 which provides a detailed explanation of the theory underlying the game design).

Participants’ overall score on their set of n = 9 squares each round is calculated as:

(1)

The parameter values (Fig. 3) were chosen to reflect the local social-ecological systems underpinning human–elephant interactions in Gabon, and their plausibility was carefully pre-tested with local farmers. For instance, losing 50% of the crop yield value to elephant damage was observed in similar contexts (Mackenzie and Ahabyona 2012).

Data collection

Each game session began with a short practice session of three rounds followed by four randomly ordered treatments of six to eight rounds each (Table 1). We thus used a within-subjects design with 65 groups (260 participants) per treatment. The number of rounds was randomized to prevent participants from anticipating the conclusion of the treatment.

We conducted 65 game sessions with 260 household farmers, of which 140 households were in conservation-influenced villages (N = 8), and 120 households were in logging-influenced villages (N = 10). Because of the low number of households within each village in the two study areas (2–30 households per village), we did not randomly select participants but instead invited all willing participants present in each village to participate in our study. Only one representative per household was invited to participate in the games and was preferably the person responsible for most agricultural activities; in most cases, this person was female.

Games were facilitated in April and May 2018 by two teams of two people each (including the lead author), randomly assigned to groups of four participants. The game instruction protocol (in French) was extensively piloted in nearby villages in February and March 2018 (Appendix 4). The research ethics committee of the University of Stirling approved this study. We told participants that results would be presented in aggregate form and would not be linked to their identity or the individual villages. We gained verbal informed consent from all participants before implementing the games. We dedicated sufficient time to the practice rounds before starting the treatments to ensure sufficient comprehension and to gain participants’ trust. The use of images and verbal explanations allowed accessibility to illiterate or innumerate participants (< 5% of participants; see Table A.1.2 in Appendix 1). The practice rounds lasted 30–60 min, and the whole game lasted 1.5–2.5 h. We offered gift items (e.g., a torch, food containers, and cutlery, amounting to $8 USD in total) to compensate participants for their time. Daily wages in the area were approximately $6 USD.

We also administered a questionnaire survey to all participants (N = 260) after the games to collect information on demographics, farming practices, losses to wildlife, and attitudinal variables on trust and equity (see Rakotonarivo et al. 2021a for the full survey and anonymized data). To understand motivations for broad decision strategies in the game, we further conducted unstructured individual debriefing interviews with 30 participants immediately upon completion of the game sessions and questionnaire survey. Interviewees were selected purposively, based on our observations of behavior in the game. We continued surveys until we believed that we had interviewed participants that had used the full spectrum of participant strategies in the games, i.e., those who engaged frequently in lethal control, those who mostly resorted to using non-lethal deterrents, and those who exhibited varying levels of willingness to provide elephant habitats. The interviews lasted 20–40 min and were not audio-recorded given the sensitive nature of the data (particularly crop losses to elephants and retaliatory killing by villagers, which is illegal). Instead, we took notes and direct quotes where appropriate to aid the contextualization of the game outcomes and provide additional insights into the participants’ stated motivations (Anderies et al. 2011).

Data analysis

We examined two main game outcomes measured at the individual household level (i.e., household unit): use of lethal control (i.e., kill decisions), and provision of elephant habitats. These outcomes draw from a larger number of actions (farm, kill, scare, provide habitat) and represent two diametrically opposed strategies in mitigating elephant crop damage. They are thus particularly relevant to the exploration of responses to conflict interventions.

We modeled these outcome variables as proportion data (proportion of cells with kill decisions or with habitat provided, respectively) using binomial generalized linear mixed-effect models with logit link function implemented within the lme4 statistical package (Bolker et al. 2009). Household identity was included as a nested random effect within group identity to account for household-specific and group-specific effects. We controlled for learning by including four game conditions: (1) rounds in the game, (2) rounds into session, (3) sum of kill decisions of the three other participants in the previous round (lagged kill decisions), and (4) sum of habitat decisions of the three other participants in the previous round (lagged habitat decisions).

To relate behavior in the game to the trust and equity attitudes, we included three explanatory variables in addition to the treatments and game conditions: (1) one aggregated measure of interpersonal trust among local communities, (2) one aggregated measure of institutional trust (trust toward conservation and government authorities), and (3) one aggregated measure of equity indices. Each of these aggregated measures is the weighted factor scores of three variables generated from exploratory factor analyses with the psych statistical package using oblimin rotation (Revelle 2018; Tables A1.1a–c in Appendix 1). The Chronbach’s alpha (Tables S1a–S1c) indicated strong internal consistency and showed that these aggregated measures were valid indicators of a single underlying factor. We also included participants’ perceptions of the positive and negative effects of elephants on their well-being.

To explore the associations between game outcomes and real-life farming practices and regions, we included as explanatory variables households’ reported experiences of crop losses (whether any of their fields had been damaged by elephants in the previous agricultural year) and the study location. In addition, we controlled for other socioeconomic variables such as participant age, gender, education, and two principal component vectors of a range of household wealth indicators extracted from a principal component analysis (PCA) using the psych package and promax rotation (Revelle 2018; Fig. A1.1 in Appendix 1). We also considered two-level interactions between the treatments and other household-related variables such as reported experiences of crop losses, and participant-related variables such as perceptions of elephants, trust, and equity indices. Table A1.2 (Appendix 1) provides a detailed summary of the explanatory variables included in our models.

To avoid multicollinearity, we checked for correlations between predictor variables. Model selection was then carried out using backward stepwise selection of fixed effects based on the corrected Akaike Information Criterion (AICc) value. We conducted all analyses in R version 3.5.1 (R Core Team 2018).

RESULTS

Household socioeconomic and participant attitudinal characteristics

On average, 47% of the surveyed households relied on agriculture as the primary source of income in both study sites (Table A1.2 in Appendix 1). Of the 140 and 120 households sampled in conservation-influenced villages and logging-influenced villages, respectively, 69% and 55% had received at least one visit by elephants in the previous agricultural year and experienced crop damage (Table A1.3 in Appendix 1). Of the affected households, 68% and 54% reported significant crop losses (> 60% of annual crops; Table A1.3 in Appendix 1). On average, participants had six years of schooling, and 95% were literate. Food security, as measured by the mean number of months per year for which families reported having enough to eat, was 7.6 (standard deviation [SD] = 3.6) and 8.5 (SD = 3.3) in the conservation-influenced and logging-influenced villages, respectively. The PCA of 10 measures of wealth resulted in two axes that explained 46% of the variation and revealed no systematic differences between the two groups of villages in terms of wealth (Table A1.3 and Fig. A1.1 in Appendix 1). These two axes were used as covariates in the statistical analyses using generalized linear mixed-effect models along with other key socioeconomic characteristics (Table 2).

Participants generally reported negative attitudes on key equity indices. The share of participants who felt able to influence decision-making regarding land use and wildlife management was < 13% in both village groups (Fig. 4). Most participants (> 88%) in both regions also perceived inequitable distribution of benefits among community members, as well as unbalanced conservation and development policy (Fig. 4). More than one-half of participants reported little trust toward governmental organizations such as the National Agency for National Parks and the Ministry for Water and Forests (Fig. 4).

The interviews revealed that although local farmers in the study areas have faced crop damage for generations, crop losses to elephants are perceived by some to have escalated after the former Wildlife Reserve (created in 1946) became a National Park in 2002. Nine of thirty interviewees believed this escalation was because of increased enforcement of the protected status of elephants. Five interviewees in the logging-influenced villages also blamed logging concessions for the increasing crop damage incidents in the region. Logging activities were perceived to disturb forest and push elephants to the periphery where farmers farm. These feelings have fueled resentment toward the park and other government entities. The interviews also uncovered that the park agency role was perceived by some participants as strictly repressive. Two interviewees articulated that the park agency’s only purpose was to tighten control over wildlife. Nevertheless, 41% and 39% of surveyed households in conservation-influenced and logging-influenced villages, respectively, had positive trust attitudes toward the park agency (Fig. 4), mostly because of their dedication to protect elephants, which are considered Gabon’s national pride.

If it were not for the park agency’s actions, Gabon’s elephants would have gone extinct, and we appreciate their efforts. (ID04, 31-year-old female, park village).

Proxies for community trust were high (> 55%) in both study sites (Fig. 4). Approximately 40% of surveyed farmers (41% and 39% in conservation-influenced and logging-influenced villages, respectively) perceived positive effects of elephants on their well-being (Fig. 4). These benefits were mostly described as pertaining to the roles of elephants in ecosystems, as well as their cultural importance. The share of participants who perceived negative effects of elephants on their well-being was 79% in both village groups, mostly because of crop losses.

Effect of game treatments and household characteristics on farmers’ predisposition to kill elephants

All three treatments significantly decreased farmer propensity to engage in killing compared to the control in the game (Figure A1.2 in Appendix 1); the agglomeration treatment had the greatest effect, reducing the odds for killing by 42% compared to the control in the main-effect- only model (Table A1.4 in Appendix 1). Participants with a higher equity index were significantly less likely to engage in killing; for a one-unit increase in equity index, the model suggested a 21% decrease in the odds of kill decisions (odds ratio 0.79, 0.95 confidence interval [CI]: 0.63–0.98; Table A1.4 in Appendix 1).

In the final model (Table 2), we observed a significant interaction between the treatments and equity indices; higher equity values significantly weakened the effects of the deterrent and agglomeration treatments in reducing farmers’ decisions to kill. Kill decisions were significantly higher in the logging villages than in the conservation villages (the odds for the former were 64% higher; Table 2). Likewise, positive perceptions of the well-being effects of elephants decreased participants’ propensity to engage in killing (odds ratio 0.89, 0.95 CI: 0.80–0.98). Trust indices did not affect participants’ decisions in the game (Table A1.4 in Appendix 1). Similarly, neither farmers’ experiences of crop losses (as measured by whether their farms had been damaged at least once by elephants) nor the perceived negative effects of elephants on their well-being affected game decisions (Table A1.4 in Appendix 1). These results were insensitive to alternative model specifications testing for the effect of elephant visit frequency or whether affected households have experienced high damage.

At lower equity levels, The effect of treatments on farmers’ propensity to kill were much more pronounced at lower than at higher equity levels, as were the differences between the conservation-influenced and logging-influenced villages (Fig. 5). The predicted mean proportion of kill decisions in the baseline treatment was almost two times higher in logging-influenced villages than in conservation-influenced villages at the low equity level (7.6%, 0.95 CI: 5.2–10.8 vs. 3.1%, 0.95 CI: 2.2–4.1, respectively). However, at the higher equity level, discrepancies among treatments became negligible, and the effect of conservation vs. logging villages was also much smaller (Fig. 5).

The qualitative interviews highlighted that most participants (23 of 30) felt they had very little opportunity to voice their views and concerns (Fig. 4). Their predisposition to killing in the game was as much to express their discontent as about protecting crops.

The authorities are not clearly interested in listening to our needs. If we are aggressive towards elephants, it is because we feel abandoned, we are angry. (ID56, 56-year-old male, logging village).

Nevertheless, farmers recognized the value of elephants and anchored their killing behavior in the game on the need to control their number, not to eradicate them altogether. Such rationale was also evidenced by the negative association between kill decisions and farmers’ perceptions about the positive effects of elephants on their lives (Table 2).

To test the robustness of our inferences, we fitted additional models testing each variable of interest one at a time. These models suggest that the magnitude and statistical significance of three key variables (equity index, region, and perceptions of the positive effects of elephants on well-being) were robust to alternative specifications (Table A1.7 in Appendix 1).

Effect of game treatments and household characteristics on farmers’ predisposition to provide habitats for elephants

Only the two monetary treatments generated a substantial increase in decisions to create elephant habitat across rounds compared to the baseline treatment. The percentage of habitat decisions was the highest under the agglomeration treatment (Figure A1.2 in Appendix 1). Agglomeration had the greatest effect on habitat decisions (with an odds ratio of 12.97, 1.8 times greater than that of subsidy, 0.95 CI: 11.18–15.05; Table 3). The predicted percentages of habitat decisions in the agglomeration and subsidy treatments were 21% (0.95 CI: 16–23) and 12% (0.95 CI: 11–14), respectively (Fig. 6).

The deterrent treatment had no significant effect on farmers’ willingness to provide habitats (Table 5). The interviews suggested a nuanced account of farmers’ motives for these results. The erection of electric fences was used as an illustration of external support for deterrents in the game instructions. Although participants were generally positive about the potential effect of such technology in reducing elephant crop damage, fencing around parks or designated conservation areas was perceived by 10 interviewees as more effective than fencing farmlands.

Hungry elephants will inevitably breach the fences; if not, they will come around our houses and feed on our fruit trees and gardens. The only solution is to keep them far away. (ID32, 34-year-old female, conservation village).

The maintenance costs of the community fences and a fair sharing of these costs among village members if government funds are ever withdrawn were also concerns for these interviewees. In the logging villages, participants foresaw space and soil fertility as major limitations of the fences.

Varying subsidy levels had no significant effects on the percentages of kill decisions made or habitats provided (Table A1.6, Fig. A1.3 in Appendix 1). The interviews corroborated such findings; eight interviewees felt that they could use any help with their livelihoods.

You cannot say no to money; the amount does not really matter when you do not have many choices. Anyway, we have already started giving up on farming because we can no longer fight elephants; entire villages are disappearing here, we are left on our own. (ID16, 62-year-old female, conservation village).

None of the socioeconomic or attitudinal covariates significantly affected decisions to provide habitats (Table A1.5 in Appendix 1).

Effect of other game conditions on game outcomes

At the treatment level, we did not observe any significant learning effect for both outcomes (as shown by the odds ratios of the “round in the game” variable in Tables 2 and 3). However, as participants played more rounds into the entire game session, they were less likely to kill and more likely to provide habitats for elephants (although the effect size was relatively small, odds ratio = 0.99, 0.95 CI: 0.98–0.99 and 1.02, 0.95CI: 1.02–1.03 for decisions to kill and provide habitats, respectively). Also, the decisions of other participants in previous rounds significantly affected the two outcomes, leading to higher kill and habitat decisions, other things being equal. This result indicates that participants took cues from the previous round and were more likely to use a strategy that others used. Higher numbers of elephants in the landscape also led to higher percentages of kill and habitat decisions.

DISCUSSION

Predictors of farmer land-use decisions

We examined the effects of deterrent support and financial incentives on farmer decisions using a temporally and spatially dynamic game. We found that monetary payments significantly increased local farmers’ decisions to provide designated areas for elephants and decreased their propensity to use lethal methods. The agglomeration treatment that pays individual households for the provision of contiguous habitats had the greatest effect on farmers’ behavior. Our results differ from those of Liu et al. (2019), who reported mixed findings on the performance of an agglomeration bonus in an auction setting among forest landowners in rural China.

Our study provides robust quantitative evidence that directly links equity issues (e.g., the degree to which local people perceive that they are involved in decision-making processes) with their behavior in the game. We found that farmers’ propensity to engage in killing is significantly reduced by more positive perceptions of equity indicators. Another key finding is that killing behavior is also strongly predicted by whether local people perceive positive effects of elephants on their well-being (such as the critical role of elephants in ecological processes). Neither material losses that farmers had incurred from elephant crop damage nor their socioeconomic characteristics affected their game decisions. In addition, the odds of killing behavior (in the games) were 64% higher in the logging villages than in villages influenced by conservation management policies (i.e., close to National Parks), although the rates of elephant encounters and crop damage were lower in the former (55% have experienced crop damage in previous agricultural year in logging villages compared to 69% in conservation villages). These results highlight the need to extend conflict interventions beyond protected areas. Because there were no significant differences in trust and equity perceptions between the two groups, these results could be explained by lower environmental law enforcement further from national parks or more positive attitudes toward conservation among participants in the conservation-influenced villages.

Unlike previous studies (e.g., Gneezy and Rustichini 2000, Handberg and Angelsen 2019), we found that increasing subsidy levels in the monetary treatments did not generate a positive response in habitat provision. The interviews suggested that some farmers felt unable to negotiate compensations or to participate effectively in decision-making processes. There seems to be an urgent need for any forms of recognition of the considerable costs that farmers incur from elephant crop damage, as documented elsewhere in Africa (Noga et al. 2018).

Games as tools to predict and manage conservation conflicts

The results presented here are from a game rather than pilot or real-world interventions, and despite our efforts to encourage participants to state their true preferences, we cannot guarantee that they are accurate reflections of the complexity of human–elephant interactions. Thus, our findings might not correspond with how participants would behave in real life (Roe and Just 2009, Jackson 2012). In particular, the value parameters (Fig. 3) used in the games, such as the relative weight of different land uses and the scale of crop damage by elephants, might not perfectly mirror elephant behavior (Mumby and Plotnik 2018). However, although our game settings were necessarily simplified, they were perceived by participants as a safe and realistic decision-support tool to voice their preferences and needs. The incorporation of the temporal dimension and animal movements also enhanced motivation and plausibility. Studying such a sensitive topic with conventional methods is often difficult (Nuno and St. John 2015), but the game provided a relaxed atmosphere to explore local farmers’ propensity to engage in lethal control.

Although our incentive structure differed from common practices in experimental economics (rewarding players based on their scores), there is precedence in the experimental literature for being flexible with the incentive structure to ensure compatibility with local concerns (e.g., Bell et al. 2015, Meinzen-Dick et al. 2016, Rakotonarivo et al. 2021b). Our priority was to create a safe sphere for participants to engage fully and state their preferences for various interventions. We also wanted to avoid participants’ fixation on the rewards (Hur and Nordgren 2016) and were careful not to introduce monetary rewards in a sensitive and emotionally charged context such as human–elephant conflicts.

We also draw upon qualitative data to validate and contextualize our results; the discussions that followed the games gave critical insights into the game behavior and suggested that the game was salient to participants. A follow-up question asking participants about their main goal in the games further suggested that 180 participants (69%) aimed to maximize their utility by playing as in real life (Fig. A1.4 in Appendix 1). By better understanding how farmers, and not a perfectly rational Homo economicus, make decisions when facing different options, we are better able to understand what drives people’s decisions and uncover novel solutions invisible to conventional tools such as questionnaire surveys (Murnighan and Wang 2016).

Implications for managing conservation conflicts

There is increasing evidence that incentive-based instruments that are directly linked to conservation objectives can be valuable tools for encouraging human–wildlife coexistence (Dickman et al. 2011, Nyhus 2016). Our findings suggest that incentive-based instruments are conducive to pro-conservation behavior. Performance payments for habitat provision can be made contingent on wildlife populations by rewarding farmers for wildlife species inventoried in these habitats. Such a mechanism has been successfully trialed in other countries such as Scotland, where farmers are paid to maintain and feed protected geese on their lands (McKenzie and Shaw 2017). Likewise, in Sweden, farmers are paid for each certified lynx and wolverine in village grazing lands (Zabel and Holm-Müller 2008). Incentive-based instruments might also outperform the damage compensation approach by reducing issues of “moral hazards” prevalent in compensation schemes whereby farmers increase the likelihood of crop losses (Ravenelle and Nyhus 2017).

Nevertheless, monetary incentives might suffer from many of the same problems faced by compensation schemes, such as the timing of payments and determining the appropriate payment level (Hanley et al. 2012). Monitoring might also be challenging where there is an issue of scale and mobility, especially when schemes involve large mammals. Real-time monitoring technology such as GPS collars and drones that provide near-instantaneous observation of animals can help to address these challenges (Wall et al. 2014, but see Shrestha and Lapeyre 2018, who discuss the drawbacks of using modern technologies).

In the context of Gabon, where rural exodus and low rural population density have considerably weakened agricultural production (Fairet et al. 2014) and where wildlife habitat availability is not a concern, incentivising the allocation of more lands to elephants might not be appropriate. Instead, because our findings show that positive perceptions of the well-being effects of elephants can reduce farmers’ propensity to kill, redistributing financial incomes from national parks to local development might help to increase local support for conservation and have a greater effect on pro-conservation behavior (McDermott et al. 2013). National government strategies such as the Gabonese National Park Agency management plan include the provision of benefits to surrounding communities through tourism revenues and direct financial aid leveraged from conservation funding (Leduc et al. 2016). Interviewed participants, however, felt that the effectiveness of these revenues is limited.

Our study further shows that conflict interventions in rural Gabon are more likely to succeed where levels of social equity are higher. Our findings imply that conflict interventions might also be more effective if they seek ways and means of addressing social equity. For instance, beyond the current focus on reducing elephant crop damage, greater involvement of communities in decision-making processes would help to build trust toward conservation agencies and build genuine receptivity to, and ownership of, conflict interventions (Madden and McQuinn 2014, Hill et al. 2017, Noga et al. 2018). Such ownership, in turn, can help to foster community commitment to maintain technological deterrents such as electric fences in the long term. Stakeholder engagement that leads to genuine participation can be achieved by developing dialogue (Redpath et al. 2017), by building local people’s capacity and abilities to negotiate their needs (McDermott et al. 2013), or by empowering local people in leadership roles during decision-making and implementation processes (Madden and McQuinn 2014). Because equity concerns are complex and evolving (Dawson et al. 2018), efforts to engage local stakeholders will also need to be adaptive and sustained over time.

Conclusion

We used a dynamic interactive game framed around farmer land-use decisions to examine farmer responses to conflict interventions such as support for elephant deterrent techniques and innovative economic instruments. Our findings suggest that incentive-based payments are conducive to pro-conservation behavior, and agglomeration schemes will achieve the greatest conservation outcomes. Our study also shows that positive perceptions of social equity can advance the acceptability of conflict mitigation strategies. Our findings imply that addressing the material manifestations of such conflicts might not tackle underlying social conflicts; conflict interventions might be more effective if they also address social equity. The strong regional differences in elephant killing behavior further highlight the need to extend conflict interventions beyond protected areas. Interactive games such as the one we describe here offer a low-risk tool for testing novel approaches to understanding, managing, and, where possible, preventing conservation conflicts.

LITERATURE CITED

Anderies, J. M., M. A. Janssen, F. Bousquet, J.-C. Cardenas, D. Castillo, M.-C. Lopez, R. Tobias, B. Vollan, and A. Wutich. 2011. The challenge of understanding decisions in experimental studies of common pool resource governance. Ecological Economics 70(9):1571-1579. https://doi.org/10.1016/j.ecolecon.2011.01.011

Andersson, K. P., N. J. Cook, T. Grillos, M. C. Lopez, C. F. Salk, G. D. Wright, and E. Mwangi. 2018. Experimental evidence on payments for forest commons conservation. Nature Sustainability 1:128-135. https://doi.org/10.1038/s41893-018-0034-z

Barua, M., S. A. Bhagwat, and S. Jadhav. 2013. The hidden dimensions of human–wildlife conflict: health impacts, opportunity and transaction costs. Biological Conservation 157:309-316. https://doi.org/10.1016/j.biocon.2012.07.014

Bell, A., W. Zhang, F. Bianchi, and W. vander Werf. 2013. NonCropShare: a coordination game for provision of insect-based ecosystem services. IFPRI Biosight Program. Version 2. International Food Policy Research Institute, Washington, D.C., USA. [online] URL: http://ifpri.org/publication/noncropshare-coordination-game

Bell, A., W. Zhang, and K. Nou. 2016. Pesticide use and cooperative management of natural enemy habitat in a framed field experiment. Agricultural Systems 143:1-13. https://doi.org/10.1016/j.agsy.2015.11.012

Bell, A. R., M. A. A. Shah, A. Anwar, and C. Ringler. 2015. What role can information play in improved equity in Pakistan’s irrigation system? Evidence from an experimental game in Punjab. Ecology and Society 20(1):51. http://dx.doi.org/10.5751/ES-07368-200151

Bolker, B. M., M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen, M. H. H. Stevens, and J.-S. S. White. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3):127-135. https://doi.org/10.1016/j.tree.2008.10.008

Cardenas, J.-C., M. Janssen, and F. Bousquet. 2013. Dynamics of rules and resources: three new field experiments on water, forests and fisheries. Pages 319-345 in J. A. List and M. K. Price, editors. Handbook on experimental economics and the environment. Edward Elgar, Cheltenham, UK. https://doi.org/10.4337/9781781009079.00020

Chartier, L., A. Zimmermann, and R. J. Ladle. 2011. Habitat loss and human–elephant conflict in Assam, India: Does a critical threshold exist? Oryx 45(4):528-533. https://doi.org/10.1017/S0030605311000044

Dawson, N., A. Martin, and F. Danielsen. 2018. Assessing equity in protected area governance: approaches to promote just and effective conservation. Conservation Letters 11(2):e12388. https://doi.org/10.1111/conl.12388

Dickman, A. J. 2010. Complexities of conflict: the importance of considering social factors for effectively resolving human–wildlife conflict. Animal Conservation 13(5):458-466. https://doi.org/10.1111/j.1469-1795.2010.00368.x

Dickman, A. J., E. A. Macdonald, and D. W. Macdonald. 2011. A review of financial instruments to pay for predator conservation and encourage human–carnivore coexistence. Proceedings of the National Academy of Sciences 108(34):13937-13944. https://doi.org/10.1073/pnas.1012972108

Evans, L. A., and W. M. Adams. 2018. Elephants as actors in the political ecology of human–elephant conflict. Transactions of the Institute of British Geographers 43(4):630-645. https://doi.org/10.1111/tran.12242

Fairet, E. 2012. Vulnerability to crop-raiding: an interdisciplinary investigation in Loango National Park, Gabon. Dissertation. Durham University, Durham, UK. [online] URL: http://etheses.dur.ac.uk/6399/

Fairet, E., S. Bell, K. Remanda, and J. M. Setchell. 2014. Rural emptiness and its influence on subsistence farming in contemporary Gabon: a case study in Loango National Park. Society, Biology and Human Affairs 78(1-2):39-59.

Gneezy, U., and A. Rustichini. 2000. Pay enough or don’t pay at all. Quarterly Journal of Economics 115(3):791-810. https://doi.org/10.1162/003355300554917

Graham, M. D., B. Notter, W. M. Adams, P. C. Lee, and T. N. Ochieng. 2010. Patterns of crop-raiding by elephants, Loxodonta africana, in Laikipia, Kenya, and the management of human–elephant conflict. Systematics and Biodiversity 8(4):435-445. https://doi.org/10.1080/14772000.2010.533716

Handberg, Ø. N., and A. Angelsen. 2019. Pay little, get little; pay more, get a little more: a framed forest experiment in Tanzania. Ecological Economics 156:454-467. https://doi.org/10.1016/j.ecolecon.2016.09.025

Hanley, N., S. Banerjee, G. D. Lennox, and P. R. Armsworth. 2012. How should we incentivize private landowners to ‘produce’ more biodiversity? Oxford Review of Economic Policy 28(1):93-113. https://doi.org/10.1093/oxrep/grs002

Hill, C. M., A. D. Webber, and N. E. C. Priston, editors. 2017. Understanding conflicts about wildlife: a biosocial approach. Berghahn, New York, New York, USA. https://doi.org/10.2307/j.ctvw04h12

Hur, J. D., and L. F. Nordgren. 2016. Paying for performance: performance incentives increase desire for the reward object. Journal of Personality and Social Psychology 111(3):301316. https://doi.org/10.1037/pspa0000059

Jackson, C. 2012. Internal and external validity in experimental games: a social reality check. European Journal of Development Research 24:71-88. https://doi.org/10.1057/ejdr.2011.47

Janssen, M. A., R. Holahan, A. Lee, and E. Ostrom. 2010. Lab experiments for the study of social-ecological systems. Science 328(5978):613-617. https://doi.org/10.1126/science.1183532

Janssen, M. A., A. Lee, and T. M. Waring. 2014. Experimental platforms for behavioral experiments on social-ecological systems. Ecology and Society 19(4):20. https://doi.org/10.5751/ES-06895-190420

Laurance, W. F., A. Alonso, M. Lee, and P. Campbell. 2006. Challenges for forest conservation in Gabon, Central Africa. Futures 38(4):454-470. https://doi.org/10.1016/j.futures.2005.07.012

Leduc, S., M. Starkey, J.-B. Squarcini, and M. Bosch. 2016. Plan de gestion 2016–2020: programme d’implications des communautés locales et d’éducation de l’environnement. Agence Nationale des Parcs Nationaux, Libreville, Gabon.

List, J. A., and M. K. Price. 2016. The use of field experiments in environmental and resource economics. Review of Environmental Economics and Policy 10(2):206-225. https://doi.org/10.1093/reep/rew008

Liu, Z., J. Xu, X. Yang, Q. Tu, N. Hanley, and A. Kontoleon. 2019. Performance of agglomeration bonuses in conservation auctions: lessons from a framed field experiment. Environmental and Resource Economics 73:843-869. https://doi.org/10.1007/s10640-019-00330-1

Mackenzie, C. A., and P. Ahabyona. 2012. Elephants in the garden: financial and social costs of crop raiding. Ecological Economics 75:72-82. https://doi.org/10.1016/j.ecolecon.2011.12.018

Madden, F., and B. McQuinn. 2014. Conservation’s blind spot: the case for conflict transformation in wildlife conservation. Biological Conservation 178:97-106. https://doi.org/10.1016/j.biocon.2014.07.015

Madden, F., and B. McQuinn. 2015. Conservation conflict transformation: the missing link in conservation. Pages 257-270 in S. M. Redpath, R. J. Gutiérrez, K. A. Wood, and J. C. Young, editors. Conflicts in conservation: navigating towards solutions. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9781139084574.019

Mason, T. H. E., C. R. J. Pollard, D. Chimalakonda, A. M. Guerrero, C. Kerr-Smith, S. A. G. Milheiras, M. Roberts, P. R. Ngafack, and N. Bunnefeld. 2018. Wicked conflict: using wicked problem thinking for holistic management of conservation conflict. Conservation Letters 11(6):e12460. https://doi.org/10.1111/conl.12460

Mayer, I., G. Bekebrede, C. Harteveld, H. Warmelink, Q. Zhou, T. van Ruijven, J. Lo, R. Kortmann, and I. Wenzler. 2014. The research and evaluation of serious games: toward a comprehensive methodology. British Journal of Educational Technology 45(3):502-527. https://doi.org/10.1016/j.procs.2012.10.075

McDermott, M., S. Mahanty, and K. Schreckenberg. 2013. Examining equity: a multidimensional framework for assessing equity in payments for ecosystem services. Environmental Science and Policy 33:416-427. https://doi.org/10.1016/j.envsci.2012.10.006

McKenzie, R., and J. M. Shaw. 2017. Reconciling competing values placed upon goose populations: the evolution of and experiences from the Islay Sustainable Goose Management Strategy. Ambio 46:198-209. https://doi.org/10.1007/s13280-016-0880-8

Meinzen-Dick, R., R. Chaturvedi, L. Domènech, R. Ghate, M. A. Janssen, N. D. Rollins, and K. Sandeep. 2016. Games for groundwater governance: field experiments in Andhra Pradesh, India. Ecology and Society 21(3):38. https://doi.org/10.5751/ES-08416-210338

Mumby, H. S., and J. M. Plotnik. 2018. Taking the elephants’ perspective: remembering elephant behavior, cognition and ecology in human-elephant conflict mitigation. Frontiers in Ecology and Evolution 6:122. https://doi.org/10.3389/fevo.2018.00122

Murnighan, J. K., and L. Wang. 2016. The social world as an experimental game. Organizational Behavior and Human Decision Processes 136:80-94. https://doi.org/10.1016/j.obhdp.2016.02.003

Naidoo, R., B. Fisher, A. Manica, and A. Balmford. 2016. Estimating economic losses to tourism in Africa from the illegal killing of elephants. Nature Communications 7:13379. https://doi.org/10.1038/ncomms13379

Ngama, S., L. Korte, J. Bindelle, C. Vermeulen, and J. R. Poulsen. 2016. How bees deter elephants: beehive trials with forest elephants (Loxodonta africana cyclotis) in Gabon. Plos One 11(5):e0155690. https://doi.org/10.1371/journal.pone.0155690

Noga, S. R., O. D. Kolawole, O. T. Thakadu, and G. S. Masunga. 2018. ‘Wildlife officials only care about animals’: farmers’ perceptions of a Ministry-based extension delivery system in mitigating human–wildlife conflicts in the Okavango Delta, Botswana. Journal of Rural Studies 61:216-226. https://doi.org/10.1016/j.jrurstud.2018.06.003

Nuno, A., and F. A. V. St. John. 2015. How to ask sensitive questions in conservation: a review of specialized questioning techniques. Biological Conservation 189:5-15. https://doi.org/10.1016/j.biocon.2014.09.047

Nyhus, P. J. 2016. Human–wildlife conflict and coexistence. Annual Review of Environment and Resources 41:143-171. https://doi.org/10.1146/annurev-environ-110615-085634

Parkhurst, G. M., and J. F. Shogren. 2007. Spatial incentives to coordinate contiguous habitat. Ecological Economics 64(2):344-355. https://doi.org/10.1016/j.ecolecon.2007.07.009

Peterson, M. N., J. L. Birckhead, K. Leong, M. J. Peterson, and T. R. Peterson. 2010. Rearticulating the myth of human–wildlife conflict. Conservation Letters 3(2):74-82. https://doi.org/10.1111/j.1755-263X.2010.00099.x

Poole, O. 2016. Gabon launches elephant fences in pioneering conservation move. The Independent 25 August 2016. [online] URL: https://www.independent.co.uk/voices/campaigns/giantsclub/gabon-launches-elephant-fences-pioneering-conservation-move-a7181116.html

Pooley, S., M. Barua, W. Beinart, A. Dickman, G. Holmes, J. Lorimer, A. J. Loveridge, D. W. Macdonald, G. Marvin, S. Redpath, C. Sillero-Zubiri, A. Zimmermann, and E. J. Milner-Gulland. 2017. An interdisciplinary review of current and future approaches to improving human–predator relations. Conservation Biology 31(3):513-523. https://doi.org/10.1111/cobi.12859

Poulsen, J. R., S. E. Koerner, S. Moore, V. P. Medjibe, S. Blake, C. J. Clark, M. E. Akou, M. Fay, A. Meier, J. Okouyi, C. Rosin, and L. J. T. White. 2017. Poaching empties critical Central African wilderness of forest elephants. Current Biology 27(4):R134-R135. https://doi.org/10.1016/j.cub.2017.01.023

Pozo, R. A., T. Coulson, G. McCulloch, A. Stronza, and A. Songhurst. 2019. Chilli-briquettes modify the temporal behaviour of elephants, but not their numbers. Oryx 53(1):100-108. https://doi.org/10.1017/S0030605317001235

R Core Team. 2018. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: http://www.R-project.org/

Rakotonarivo, O. S., A. Bell, J. Cusack, A. B. Duthie, I. Jones, A. Kipchumba, J. Minderman, R. Pozo, and N. Bunnefeld. 2021a. Experimental games and questionnaire survey for understanding conservation conflicts in Gabon, Scotland, Madagascar and Kenya 2018–2019. [Data set]. UK Data Service, Colchester, UK. https://dx.doi.org/10.5255/UKDA-SN-854068

Rakotonarivo, O. S., I. L. Jones, A. Bell, A. B. Duthie, J. Cusack, J. Minderman, J. Hogan, I. Hodgson, and N. Bunnefeld. 2021b. Experimental evidence for conservation conflict interventions: the importance of financial payments, community trust and equity attitudes. People and Nature 3(1):162-175. https://doi.org/10.1002/pan3.10155

Ravenelle, J., and P. J. Nyhus. 2017. Global patterns and trends in human–wildlife conflict compensation. Conservation Biology 31(6):1247-1256. https://doi.org/10.1111/cobi.12948

Redpath, S. M., A. Keane, H. Andrén, Z. Baynham-Herd, N. Bunnefeld, A. B. Duthie, J. Frank, C. A. Garcia, J. Månsson, L. Nilsson, C. R. J. Pollard, O. S. Rakotonarivo, C. F. Salk, and H. Travers. 2018. Games as tools to address conservation conflicts. Trends in Ecology and Evolution 33(6):415-426. https://doi.org/10.1016/j.tree.2018.03.005

Redpath, S. M., J. D. C. Linnell, M. Festa-Bianchet, L. Boitani, N. Bunnefeld, A. Dickman, R. J. Gutiérrez, R. J. Irvine, M. Johansson, A. Majić, B. J. McMahon, S. Pooley, C. Sandström, A. Sjölander-Lindqvist, K. Skogen, J. E. Swenson, A. Trouwborst, J. Young, and E. J. Milner-Gulland. 2017. Don’t forget to look down – collaborative approaches to predator conservation. Biological Reviews 92(4):2157-2163. https://doi.org/10.1111/brv.12326

Redpath, S. M., J. Young, A. Evely, W. M. Adams, W. J. Sutherland, A. Whitehouse, A. Amar, R. A. Lambert, J. D. C. Linnell, A. Watt, and R. J. Gutiérrez. 2013. Understanding and managing conservation conflicts. Trends in Ecology and Evolution 28(2):100-109. https://doi.org/10.1016/j.tree.2012.08.021

Revelle, W. 2018. Psych: procedures for personality and psychological research. Version 1.8.12. R package. [online] URL: https://CRAN.R-project.org/package=psych

Rodela, R., A. Ligtenberg, and R. Bosma. 2019. Conceptualizing serious games as a learning-based intervention in the context of natural resources and environmental governance. Water 11(2):245. https://doi.org/10.3390/w11020245

Roe, B. E., and D. R. Just. 2009. Internal and external validity in economics research: tradeoffs between experiments, field experiments, natural experiments, and field data. American Journal of Agricultural Economics 91(5):1266-1271. https://doi.org/10.1111/j.1467-8276.2009.01295.x

Shaffer, L. J., K. K. Khadka, J. Van Den Hoek, and K. J. Naithani. 2019. Human-elephant conflict: a review of current management strategies and future directions. Frontiers in Ecology and Evolution 6:235. https://doi.org/10.3389/fevo.2018.00235

Shrestha, Y., and R. Lapeyre. 2018. Modern wildlife monitoring technologies: conservationists versus communities? A case study: the Terai-Arc landscape, Nepal. Conservation and Society 16(1):91-101. https://doi.org/10.4103/cs.cs_16_83

Travers, H., T. Clements, A. Keane, and E. J. Milner-Gulland. 2011. Incentives for cooperation: the effects of institutional controls on common pool resource extraction in Cambodia. Ecological Economics 71:151-161. https://doi.org/10.1016/j.ecolecon.2011.08.020

Walker, K. L. 2012. Labor costs and crop protection from wildlife predation: the case of elephants in Gabon. Agricultural Economics 43(1):61-73. https://doi.org/10.1111/j.1574-0862.2011.00565.x

Wall, J., G. Wittemyer, B. Klinkenberg, and I. Douglas-Hamilton. 2014. Novel opportunities for wildlife conservation and research with real-time monitoring. Ecological Applications 24(4):593-601. https://doi.org/10.1890/13-1971.1

White, B., and N. Hanley. 2016. Should we pay for ecosystem service outputs, inputs or both? Environmental and Resource Economics 63:765-787. https://doi.org/10.1007/s10640-016-0002-x

Wilensky, U. 1999. NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, Illinois, USA. [online] URL: http://ccl.northwestern.edu/netlogo/

Wilson, G. R., M. W. Hayward, and C. Wilson. 2017. Market-based incentives and private ownership of wildlife to remedy shortfalls in government funding for conservation. Conservation Letters 10(4):485-492. https://doi.org/10.1111/conl.12313

Woodroffe, R., S. Thirgood, and A. Rabinowitz. 2005. People and wildlife: conflict or coexistence? Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511614774

Young, J. C., A. Jordan, K. R. Searle, A. Butler, D. S. Chapman, P. Simmons, and A. D. Watt. 2013. Does stakeholder involvement really benefit biodiversity conservation? Biological Conservation 158:359-370. https://doi.org/10.1016/j.biocon.2012.08.018

Young, J. C., K. Searle, A. Butler, P. Simmons, A. D. Watt, and A. Jordan. 2016a. The role of trust in the resolution of conservation conflicts. Biological Conservation 195:196-202. https://doi.org/10.1016/j.biocon.2015.12.030

Young, J. C., D. B. A. Thompson, P. Moore, A. MacGugan, A. Watt, and S. M. Redpath. 2016b. A conflict management tool for conservation agencies. Journal of Applied Ecology 53(3):705-711. https://doi.org/10.1111/1365-2664.12612

Zabel, A., and K. Holm-Müller. 2008. Conservation performance payments for carnivore conservation in Sweden. Conservation Biology 22(2):247-251. https://doi.org/10.1111/j.1523-1739.2008.00898.x