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Bartelet, H. A., M. L. Barnes, L. A. A. Bakti, and G. S. Cumming. 2025. Operationalizing and measuring climate change adaptation success. Ecology and Society 30(1):14.ABSTRACT
In a context of rapid global change, understanding whether and how adaptation to climate change can be considered successful has become an important research gap within the climate change adaptation literature. Although definitions of adaptation success have been formulated, it remains unclear how they can be operationalized and tested empirically. To address this gap, we operationalized one of the most prominent definitions of successful adaptation within the academic literature, which describes success as adaptations that support reductions in risk and vulnerability without compromising sustainability. Specifically, drawing on data collected from 209 coral reef tourism operators across 28 locations and eight countries in the Asia-Pacific, we explored how the risk, vulnerability, and sustainability outcomes that operators experienced one year after experiencing a severe climate disturbance (either coral bleaching or a cyclone) related to the types of adaptation they adopted in response to the disturbance. We used chi-squared tests and multivariate regression to explore the relationships between adaptive responses, adaptation outcomes, and contextual conditions. Compared to a control group with non-affected operators, operators affected by a climate disturbance were significantly more likely to have experienced an increase in perceived climate risk and reduced economic and environmental sustainability. However, our findings indicate that at least some adaptation responses were effective in promoting desirable outcomes, such as reductions in risk and vulnerability. Spatial diversification of reef site use supported economic outcomes despite environmental impacts, while reef restoration measures reduced perceived climate risks for some operators. Moreover, seeking support from others reduced vulnerability to coral bleaching, while also having positive economic outcomes. Our findings suggest scientific needs for further research on the causal relationships between adaptation measures and their outcomes, experimentation with different statistical methods, and empirical tests of the generalizability of our findings in different contexts over space and time.
INTRODUCTION
Adaptation to climate change has become a topic of increasing scientific and public interest as the initial impacts of contemporary warming manifest themselves (Berrang-Ford et al. 2021, IPCC 2023). Given that the world will most likely exhaust the 1.5 °C carbon budget by the year 2029 (DNV 2024), further warming and warming-related climate impacts are to be expected over the coming decades. These impacts will have an increasing impact on society in terms of human health, well-being and economic activity (IPCC 2023). Local and regional differences in their magnitude will depend on both the extent to which people are affected and their ability to adapt (Kahn 2016, Auffhammer 2018).
The latest global stocktake of evidence on human adaptation to climate change found that while the number of studies documenting implemented adaptations is increasing, little is known about whether these adaptations can be considered effective, for example, in reducing climate-related risks and vulnerabilities (Berrang-Ford et al. 2021). Measuring climate adaptation effectiveness begins with defining desirable outcomes (e.g., risk or vulnerability reduction; IPCC 2023). When a combination of these outcomes are achieved, adaptation may be judged as being “successful” (Moser and Boykoff 2013). However, adaptation success can be incredibly challenging to measure in practice, and there has been disagreement about whether a generic and universal operationalization of adaptation success is even a desirable and reachable goal (Moser and Ekstrom 2010). Some experts have argued that adaptation is grounded in a local context, and thus metrics and indicators of adaptation success can only be determined in relation to local stakeholders (Dilling et al. 2019, Owen 2020, Piggott-McKellar et al. 2020). Others have argued for a more generic operationalization of adaptation success through evaluation criteria, targets, and indicators to enable broad-scale tracking of adaptation outcomes (Tompkins et al. 2010, Bierbaum et al. 2013, Singh et al. 2022).
Adger et al. (2005) proposed a conceptualization of adaptation success that included effectiveness, efficiency, equity, and legitimacy as dynamic success criteria. Building on Adger et al. (2005), Doria et al. (2009) proposed the first formal definition of climate change adaptation success (Fig. 1): “... any adjustment that reduces the risks associated with climate change, or vulnerability to climate change impacts, to a predetermined level, without compromising economic, social, and environmental sustainability.” (Doria et al. 2009:817). A similar definition was later proposed by Reckien et al. (2023:908) who argued that successful adaptation “reduces climate risk by either reducing vulnerability or exposure to climate-change-related impacts,” while also defining six system-level and equity-related outcome criteria. Such adaptation success definitions recognize that not all forms of adaptation will have beneficial outcomes for all actors (Eriksen et al. 2011); success is related to the fairness and distributional consequences of adaptation (Adger et al. 2005). Indeed, adaptation that compromises the economic, social, and/or environmental sustainability of other actors or groups in a community or society has been described as “maladaptation” (Barnett and O’Neill 2010, Schipper 2020, Reckien et al. 2023). The question of whether all the dimensions of the adaptation success definition (Doria et al. 2009) can be achieved in parallel remains untested. There is evidence of trade-offs between reductions in risk and vulnerability, and different forms of sustainability (Bartelet et al. 2022a). Thus, it could be argued that adaptation success should be considered as a non-binary continuum because there are many intermediate outcomes between success and failure (Tubi and Williams 2021, Reckien et al. 2023).
Doria et al.’s (2009) foundational definition of climate change adaptation success was formulated 15 years ago and has been widely used, but has yet to be operationalized and tested empirically in a real-world climate change adaptation setting. Our paper fills this gap, thereby responding to calls to better assess the effectiveness of adaptation responses (Berrang-Ford et al. 2021, Bartelet et al. 2022a). Specifically, we explored how the outcomes that coral reef tourism operators in the Asia-Pacific (APAC) Region experienced one year after a severe climate disturbance related to the types of adaptation they adopted. Our work advances understanding of climate change adaptation outcomes by demonstrating how measuring adaptation success can be formalized, operationalized, and quantified in a local climate change adaptation context.
Background and study sites
We operationalized and tested Doria et al.’s (2009) adaptation success definition in a coral reef tourism context. Coral reefs have already come under severe threat from elevated water temperatures and changes in disturbance regimes due to climate change (Hughes et al. 2018, Hoegh-Guldberg et al. 2019). Both the frequency and severity of coral bleaching (occurring when the thermal tolerance of corals and their symbiotic algae is exceeded; Hoegh-Guldberg 1999, Lough et al. 2018) and tropical cyclones (Kossin et al. 2020) are driven by increasing sea temperatures and can lead to significant loss of coral reefs. The degradation of coral reefs can affect the tourism industry in a direct and immediate way. For example, an increasing trend in visitor numbers to the Great Barrier Reef in Australia levelled off after the severe mass bleaching event in 2016, with visitor numbers beginning a slow decline thereafter (Bartelet et al. 2022b). The impacts of coral bleaching in particular, especially the mass bleaching event of 2016 that affected reefs across the globe (Hughes et al. 2017), offer a useful case study because they were geographically extensive while still being sufficiently homogenous for comparative purposes (Hughes et al. 2018).
METHODS
Summary of empirical strategy
We conducted 209 surveys with representatives of reef tourism companies (operators) to obtain information about the actions they took in response to a specific climate disturbance and the outcomes they experienced one year after the climate disturbance. Most surveys were undertaken between October 2020 and December 2021, while some of the surveys in Fiji were undertaken in the first half of 2022. We used the definition of successful adaptation by Doria et al. (2009) to develop and quantify indicators for risk and vulnerability, as well as economic, social, and environmental sustainability. These data were then used to explore the relationships between adaptive responses, adaptation outcomes, and relevant contextual variables (Fig. 2).
Sampling
We focused our sampling on the APAC Region, which contains 80% of coral reefs globally (Spalding et al. 2001). We deliberately selected locations where high reef tourism density (Spalding et al. 2017) coincided with high bleaching severity (Hughes et al. 2018). We selected study locations in countries spanning a range of human and institutional development so that we could interrogate the assumption that people in countries with lower living standards have lower capacity to adapt than their counterparts in more affluent countries (Brooks et al. 2005, Hughes et al. 2012, Fankhauser and McDermott 2014). Except for the Maldives and Taiwan (these were affected by mass coral bleaching in 2016, but we could not sample there because of logistical constraints), our sample included representative operators from all APAC reef tourism locations known to be severely affected by coral bleaching between 2014 and 2019. We also implemented a separate survey for reef tourism operators from Fiji and Australia that had been subject to cyclone impacts (Cyclone Winston in 2016, Cyclones Yasi in 2011 and Debbie in 2017, respectively) to explore the influence of the type of climate disturbance on the relationship between adaptive responses and outcomes. A detailed description of our sampling strategy is available in Appendix 1.
Our analytical design included a priori treatment and control groups of tourism operators based on whether their reef sites had been directly affected by a specific climate disturbance. For example, in Australia we included tourism operators from the southern Great Barrier Reef (GBR) that were less directly affected by coral bleaching in 2016 and 2017 (Bartelet et al. 2023a). We included a question about disturbance severity in our surveys to check whether the treatment/control divide was consistent with operators’ personal experiences.
Adaptive responses to climate disturbances
Because of the lack of empirical knowledge on adaptation to climate change by coral reef tourism operators, our classification drew on existing empirical evidence of adaptive responses to climate change by microeconomic actors according to the adaptation typology developed by Bartelet et al. (2022a), expert consultation with reef tourism industry experts in Australia (e.g., tourism research institutes, local reef tourism industry associations, and reef management agencies), and pilot interviews with tourism operators (Bartelet et al. 2023a). Through this process we identified 10 potential adaptive responses to climate disturbances on coral reefs. These were subsequently included in our surveys with reef tourism operators (Table 1). We also gave operators the opportunity to specify “other” responses. We note that the “climate action” response is more mitigative than adaptive; because of the scale mismatch between local reef restoration and global carbon dynamics (Cumming et al. 2006, Bellwood et al. 2019), individual reef operators cannot significantly influence the global heating that drives climate-induced coral loss (Cheng et al. 2020), or the coral’s ability to adapt to this heating (Hughes et al. 2017, Logan et al. 2021). All the other adaptations focus on actions at a local scale that may produce local, direct, and potentially positive effects even at the scale of a single microeconomic actor.
Respondents were asked: (1) whether they had used each of the 10 adaptive responses identified in Table 1; (2) whether they had implemented any response that was not included in our list; and (3) to select their most important (primary) response to the climate disturbance out of all responses taken. We asked tourism operators who were affected by two consecutive bleaching events to describe responses that were implemented at any time over a two-year period since the first bleaching event. For operators that were affected by a single climate disturbance, we asked for responses at any time over a one-year period since the event. We decided to use an adaptation period of one year after a disturbance because using a longer period would make it harder to attribute responses to specific climate events rather than other causes. For some adaptive responses, restoration measures in particular, the positive effects on environmental sustainability may take more than a year to materialize. We also did not know exactly when an operator adopted restoration measures, or other adaptive responses. For example, if restoration measures were adopted six months after a disturbance, the effective evaluation period for that response would have been six months instead of one year. Our findings, therefore, should not be judged as a direct evaluation of the longer-term environmental success or failure of restorative responses.
Dimensions and indicators of adaptation success
We developed five key actor-specific indicators (Table 2) to capture the three broad dimensions (risk, vulnerability, and sustainability) of adaptation success as identified by Doria et al. (2009) and as displayed in Figure 1. We used subjective indicators for risk and vulnerability, under the assumption that operators would have an understanding of the factors that contribute to the risks and vulnerability associated with specific climate disturbances (Jones and Tanner 2017). Quantifying objective climate risk and vulnerability indicators that could identify which reef sections and operators are most likely to be affected by (and vulnerable to) bleaching and/or tropical cyclones is difficult because even for experts, local climate risks are hard to predict as climate outcomes depend on environmental factors, such as topography and the stochastic processes involved in cloud formation (Freeman et al. 2015), as well as socioeconomic factors related to vulnerability and people’s capacity to adapt (Cinner et al. 2018).
Microeconomic reef business sustainability depends on many factors, including market demand, operational costs, debt ratios, and profit margins. Because many of these factors contain privacy-sensitive information, we focused on each company’s number of daily visitors (on reef activities / tours) as a proxy for economic sustainability. A reduction in visitor numbers could be judged as a direct sign of negative economic impacts, although this assumes constant prices and operational costs. By using a one-year period after the disturbance, we accounted for any seasonality effects. Because of our focus on visitor numbers, we were not able to capture changes in economic sustainability that came from diversification outside of tourism and/or by adaptations that led to higher market values (prices paid by tourists) for their tourist activities. Both these factors could have increased absolute and/or relative revenue without having experienced increased visitor numbers. We used coral cover as our proxy for the environmental sustainability of the coral reef (Bellwood et al. 2004), although we acknowledged that for tourists, fish and other marine life may be more important (Grafeld et al. 2016). Finally, we used the strength of social ties between reef operators (a measure of social cohesion and social capital; Barnes-Mauthe et al. 2013) as our proxy for social sustainability, under the assumption that changes in these ties could affect people’s propensity for collective action to address shared challenges (Partelow and Nelson 2020, Hasanov and Zuidema 2022). Negative changes in the ties between operators could indicate potential maladaptation if adaptive responses by some operators are negatively affecting others (Barnett and O’Neill 2010, Eriksen et al. 2011).
We collected data on the indicators in our surveys using multiple-choice categories to provide a consistent and directly comparable level of detail in the answers. All data on outcome indicators were derived from participant responses, and we did not have access to other sources of information to independently verify the provided answers. Our results should therefore be interpreted as derived from subjective measures of outcomes, informed by the people most closely involved with the considered climate issue (Hayek 1945, Béné et al. 2016, Jones 2019).
Analysis
Because we measured five outcome indicators (Table 2) for each operator, we began our analysis by testing for the pair-wise correlation between our post-disturbance outcome indicators in R software (v.4.2.1; R Core Team 2013) using the GGally package (Schloerke et al. 2021) and the Spearman’s rank coefficient for non-parametric distributions. We conducted this test because in the case where multiple outcomes are measured and the outcomes are significantly correlated, it has been proposed that multivariate methods should be used instead of analyzing each outcome separately (Teixeira-Pinto et al. 2009). However, we found that none of the correlations between the multiple outcomes we measured exceeded the threshold of (rho >) 0.4, which was proposed for deciding whether to analyze outcomes together or separately (Vickerstaff et al. 2021). We therefore followed the recommendation of Vickerstaff et al. (2021) to analyze each outcome separately. Next, we analyzed the association between tourism-specific adaptive responses to climate disturbances (Table 1) and the actor-specific outcome indicators (Table 2) using three consecutive steps that built up our understanding from the specific to the general, which we describe in detail in the following sub-sections.
Treatment versus control group (independence of outcomes)
We first divided our sample (n = 209) into a posterior treatment and control group based on each operator’s answer to a question in our survey about the fraction of reef sites that they were using on their tours before the disturbance that were severely (> 30% of reef area being bleached; Hughes et al. 2018) affected by a climate disturbance (Table 3). Operators that perceived that none of their reef sites were severely affected were placed in our posterior control sample (n = 60, 29%), while operators that had at least some (25%) of their reef sites severely affected (n = 149, 71%) were included in our posterior treatment sample. We first tested whether the actor-specific outcomes experienced one year after a climate disturbance were significantly different between our posterior treatment and control.
We classified each outcome indicator (Table 2) under one of three potential outcomes. An outcome either (1) remained the same; (2) decreased; or (3) increased as calculated one year after compared to just before a climate disturbance. We thus did not evaluate the absolute level of change (whether an outcome decreased by one or multiple levels). Because both the outcomes (same, decrease, or increase) and sample type (treatment or control) were categorical, we used Pearson’s (1900) Chi-square test of independence. Chi-square analyses were performed in Microsoft Excel using contingency tables and the CHISQ.DIST.RT() function. We applied a significance threshold (p-value) of 5%. If we found an outcome (Δ) that was dependent on having been affected, we ran two pairwise tests where we compared the fraction of operators that experienced a decrease in the outcome versus the fraction of operators that experienced no change, and similarly the fraction of operators that experienced an increase in the outcome versus the fraction of operators that experienced no change.
Adaptive responses to climate disturbances (independence of outcomes)
Next, we analyzed the association between the adaptive responses implemented by individual operators in response to a climate disturbance and the outcomes they experienced. In other words, for operators that were affected by a climate disturbance, we tested whether the adaptive response(s) they adopted led to a change in climate risk and vulnerability, without compromising sustainability (Doria et al. 2009). Here we first divided the posterior treatment sample by disturbance type. We had one sample with operators that were affected by severe impacts from coral bleaching (n = 123) and one with operators that were affected by severe impacts from tropical cyclones (n = 26). We distinguished between bleaching and cyclones because we expected them to exhibit a different qualitative and quantitative nature. Bleaching can destroy reefs, but there is a potential time lag of years between when a reef is bleached and declines in fish biomass (if the reef does not recover); whereas cyclones can turn reefs to rubble in a few hours, although the effects are patchy (Cheal et al. 2017, Dietzel et al. 2021). Additionally, for economic sustainability, cyclones affect not only the coral reef but can also damage boats, buildings, and communal tourism infrastructure.
Because both the outcomes (same, decrease, or increase) and adaptive responses (implemented or not) were categorical, we used Pearson’s (1900) Chi-square test of independence. We did not run chi-square analyses for any of the adaptive responses that were implemented by fewer than 10% of the operators in the respective bleaching and cyclone treatment samples. Including responses that are rarely adopted would have led to low expected values within the chi-square contingency tables and this could have led to invalid statistical results (Campbell 2007). We did not run chi-square analyses for any of the adaptation outcomes of which less than 5% of the operators experienced a decrease or increase.
Logistic regression models
Finally, in the third step we analyzed our combined sample including both the posterior treatment and control sample and including both impacts from coral bleaching and tropical cyclones. Here we used binary logistic regression models to test which adaptive responses and contextual variables had most predictive power in terms of explaining adaptation outcomes. Although multivariate regressions are poor at identifying effects of individual variables, they offer insights into combined effects and can be used to control for disturbance type and severity as well as for different baseline values on the outcome indicators.
We used binary logistic regression models for all outcomes, mostly because of skewedness in the outcomes data (Fig. 3), as explained in more detail in Appendix 4 (Table A7). We used as dependent variable a one-sided change in an outcome (e.g., a decrease in environmental sustainability), and compared the likelihood of that outcome against the rest of the sample that experienced any other outcome. Logistic regression models were fitted in the R modeling software (R Core Team 2013) using the glm function. The logistic regression models were validated via DHARMa residuals (Hartig 2018). All predictors had a variance inflation factor (VIF) below 2, calculated using the performance package in R (Lüdecke et al. 2021), indicating no collinearity in our model. For all outcome models we used the same set of explanatory variables (Table 3) although the baseline indicators differed based on which outcome indicator we modeled. Non-binary contextual variables (baseline value and experienced severity) were standardized using z-scores, by subtracting the mean and dividing by twice the standard deviation (Gelman 2008), to make their effect sizes directly comparable to binary variables.
Through our government effectiveness indicator, we tested whether operators located in countries with higher levels of institutional development experienced significantly different outcomes compared to operators located in countries with lower levels of institutional development, while accounting for our other predictor variables. We used the government effectiveness indicator from the Worldwide Governance Indicators initiative (Kaufmann et al. 2011, The World Bank 2021) to control for differences in institutional development. This indicator reflects perceptions of the quality of public services, the quality of civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. The government effectiveness indicator was based on data for the year 2014 for all countries because the first climate disturbance we included started in 2014. Our sample consisted of countries in two clusters, and we classified countries with a rating below 0.5 as having lower institutional development (48% of sample, including Indonesia, Fiji, and the Mariana Islands) and countries with a rating above 0.5 as having higher institutional development (52% of sample, including Australia, French Polynesia, Japan, and the Hawaiian Islands).
RESULTS
Observed changes in outcomes
The data indicate that perceived climate risk and environmental sustainability changed over time for the largest fraction of operators in the sample, while social sustainability for the most part did not change at all (Fig. 3). Reductions in environmental and economic sustainability were more common in the bleaching treatment sample compared to the bleaching control sample, and even more common in the cyclone treatment sample.
Treatment versus control group (independence of outcomes)
Almost a third (29%, n = 60) of the operators had none of their reef sites severely affected by a climate disturbance (these operators were thus included in our posterior control sample). The fraction of operators in the posterior control sample was relatively even across locations, ranging from 19% of the sampled operators in Fiji to 36% in Australia (Appendix 2, Table A2). Outcomes for perceived climate risk, environmental sustainability, and economic sustainability were significantly different in our posterior treatment and control samples (Appendix 3, Table A3). Climate vulnerability outcomes were not significantly different between our treatment and control samples. Post-hoc analyses showed that operators in our treatment sample were significantly more likely than operators in our posterior control sample to experience an increase in perceived climate risk (26% vs. 8%, p = 0.001), decreased environmental sustainability (54% vs. 12%, p = 0.000), and decreased economic sustainability (21% vs. 0%, p = 0.000) one year after being affected by a climate disturbance.
Adaptive responses by bleaching-affected operators (independence of outcomes)
In our treatment sample of operators who were affected by coral bleaching, support-seeking was the only adaptive response that was significantly associated with experienced adaptation outcomes. Specifically, operators that sought support (n = 25/123, or 20% of the bleaching treatment sample) were significantly (p = 0.025) more likely to have experienced a decrease in (perceived) climate vulnerability one year after being affected by coral bleaching (Appendix 3, Table A5). Operators affected by coral bleaching that sought support were also significantly (p = 0.006) more likely to have experienced increased economic sustainability one year after the disturbance.
Adaptive responses by cyclone-affected operators (independence of outcomes)
Three of the adaptive responses undertaken because of severe impacts from tropical cyclones were significantly associated with experienced climate vulnerability outcomes (Appendix 3, Table A6). Specifically, operators that adopted measures to improve the health of the coral reef (n = 14/26), used relief measures (n = 11/26), and/or sought support (n = 7/26) in response to cyclone impacts were also significantly (respective p-values of 0.048, 0.007, and 0.035) more likely to have experienced an increase in (perceived) climate vulnerability one year after being affected. Operators that spatially diversified their reef sites were significantly more likely to have also experienced a decrease in environmental sustainability (p = 0.037, n = 18/26). Finally, operators that used relief measures, and/or diversified their products (n = 9/26) were significantly (respective p-values of 0.048 and 0.039) more likely to have also experienced a decrease in economic sustainability.
Logistic regression models (full sample analysis)
We found that operators that were affected by tropical cyclone impacts, rather than coral bleaching, were significantly more likely to experience (1) an increase in (perceived) climate vulnerability (β = 1.67, p = 0.013); (2) a decrease in environmental sustainability (β = 1.50, p = 0.014); and (3) a decrease in economic sustainability (β = 1.58, p = 0.005; Appendix 4). Operators that had a higher fraction of their reef sites affected (experienced severity) were significantly more likely to experience (1) an increase in perceived climate risk (β = 1.22, p = 0.006); (2) a decrease in environmental sustainability (β = 2.18, p = 0.000); and (3) a decrease in economic sustainability (β = 1.67, p = 0.001). Operators that undertook measures to improve the health of the coral (restoration) were significantly more likely to experience either a decrease (β = 1.05, p = 0.048) or an increase (β = 1.34, p = 0.011) in perceived climate risk. Operators that changed their mode of operating were significantly (β = -2.00, p = 0.011) less likely to experience a decrease in perceived climate risk. Finally, operators that changed their reef sites (spatial diversification) in response to a climate disturbance were significantly (β = -1.11, p = 0.055) less likely to experience a decrease in economic sustainability.
DISCUSSION
Our results show that climate impacts from coral bleaching and tropical cyclones led to changes in actor-specific outcomes, most notably to reductions in environmental sustainability and changes in perceived climate risk (Fig. 3). Changes in outcomes (except perceived climate vulnerability) were significantly more likely for operators who had been directly affected by a climate disturbance. Almost half of the operators we surveyed (including the control group, with non-severely affected operators) lost at least 10% of coral cover one year after a climate disturbance. Unsurprisingly, none of the adaptive responses taken by operators were able to significantly reduce the likelihood of coral cover loss one year after the event. Although measures to improve the health of the coral reef (restoration measures) were adopted by more than half (56%) of the operators in our combined sample (Appendix 2, Table A2), these did not significantly reduce likelihood of coral loss one year after a climate disturbance (Appendix 4).
Despite severe climate disturbances and their impacts on coral reefs (Hughes et al. 2017), the majority (85%) of operators did not experience a substantial reduction in economic sustainability (as measured by visitor numbers). Most surveyed operators did not experience severe reductions in visitor numbers after the climate disturbances we focused on here, but those for whom a larger fraction of reef sites was affected did have a higher chance of negative economic outcomes. Our results provide evidence for feedback between environmental change and economic output (Bartelet et al. 2022b, Lin et al. 2023) and illustrate both economic damage from climate change (Auffhammer 2018) and the disproportionately high effect of this damage on a small number of actors within the system (Ilosvay et al. 2022).
Operationalizing adaptation success
Our study is, to our knowledge, the first to operationalize and test Doria et al.’s (2009) foundational definition of successful adaptation. We found that all dimensions of adaptation success could be measured and applied to a micro-level where we specifically focused on risk, vulnerability, and sustainability outcomes as perceived by an individual reef tourism operator. We identified two major challenges associated with measuring adaptation success. First, while chi-square analyses of specific responses clarified the individual relationships between adaptive responses and outcomes, they did not fully capture the fact that most operators used multiple adaptive responses to the same disturbance. Second, the relationship between adaptive responses and outcomes is likely mediated by other factors such as the baseline outcome levels (e.g., perceived risk pre-disturbance) and/or the experienced severity of climate effects. We did not account for whether some operators might have been better prepared for the impacts from climate disturbance because our analysis focused on changes in outcomes. Because of their preparedness, particular operators might have experienced lower risk and/or vulnerability before a disturbance and still had lower levels after a disturbance. Future research could distinguish between the success of and interaction between anticipative and reactive responses to climate change.
Supplementing our chi-square analyses for individual climate disturbances and individual responses with multiple-predictor regression models (where we tested for the effect of all adaptive responses on individual outcomes within the same model) provided useful, but different insights. Most notably, the combined model revealed significant associations between responses and outcomes that were consistent over our three separate samples (control, bleaching, and cyclone), such as the association between spatial diversification and economic sustainability and the association between restoration and perceived risk. However, pooling the samples obscured some effects, such as the association between support-seeking and vulnerability; support-seeking decreased vulnerability in our bleaching sample, but increased vulnerability in our cyclone sample. The relationships between adaptive responses and outcomes can be complex and multi-faceted (Adger et al. 2005, Tubi and Williams 2021), and will need to be investigated from multiple angles using large sample sizes and a range of alternative statistical approaches.
The interpretation of the statistical results and specifically separating cause and effect was also challenging. Because we measured outcomes at two points in time, we could not describe the behavior of each indicator in the intervening period. For example, economic sustainability (visitor numbers) might have decreased one month after the disturbance without recovering in the following 11 months. Thus, some adaptive responses (implemented within one year after a disturbance) might have been (partly) caused by a change in the outcome, rather than the outcome being caused by the adaptive response. For example, we found that relief measures and product diversification were significantly associated with reduced economic sustainability for cyclone impacts. However, it remains unclear whether a loss of economic sustainability was the cause or the consequence of these adaptive responses. Similar questions could be asked for some of our other findings, as discussed below. Qualitative research approaches could contribute to a better understanding of causality within the climate change adaptation process (Bennett et al. 2016, Simpson et al. 2021), although people might not consciously understand all the ways in which their values, background, and other attitudes (e.g., risk perceptions) influence their behavior (Simon 1990, Schlüter et al. 2017).
In sum, our findings suggests that Doria et al.’s (2009) adaptation success framework can be operationally and empirically helpful to track the effectiveness of implemented climate change adaptations, which has been identified as a major knowledge gap within the climate change adaptation discourse (Berrang-Ford et al. 2021, Bartelet et al. 2022a). Our methodology can be applied in other regions and climate change contexts. Doing so requires a case-specific operationalization of the outcome indicators and adaptive responses, and an evaluation of the suitability of our statistical approach. Further improvements would include using panel rather than recall data, tracking outcomes and adaptations over longer time periods, triangulation with objective risk and vulnerability indicators, and the inclusion of preparative responses and their effect on baseline outcome levels complementing an analysis of experienced changes in outcomes. Where applicable, future studies could also try to integrate the system-level and equity-related outcome criteria recently proposed by Reckien et al. (2023).
Successfulness of specific adaptive responses in a reef tourism context
We did not find a strong association between most of the adaptive responses implemented in response to coral bleaching and the outcomes experienced by individual operators (Appendix 3, Table A5). The one exception was that operators who sought support from others were more likely to experience a reduction in (perceived) climate vulnerability and an increase in economic sustainability. Operators in our control sample that sought support (10% of control sample) in response to bleaching were also significantly associated with higher economic sustainability (Appendix 3, Table A4). We initially thought the increase in economic sustainability could have been caused by the location of the operator rather than by the support-seeking response because our chi-square analyses did not control for location. Our sample description indicated that operators we sampled in Indonesia and the Mariana Islands were more likely to seek support in response to bleaching while more operators on these locations also experienced increases in visitor numbers (Appendix 2, Table A2). However, further analysis indicated that even within these locations, operators that sought support were more likely to experience increases in visitor numbers. For example, in our sample with operators in Indonesia, 33% of the operators that sought support in response to bleaching experienced an increase in visitor numbers, compared to 13% of operators who did not seek support. It is unclear why support-seeking was a “successful” response in a coral bleaching context. One possible explanation is that collective action, with actors collaborating to address common problems, contributes to successful climate change adaptation (Rodima-Taylor 2012, Karlsson and Hovelsrud 2015, Barnes et al. 2016, Carr and Nalau 2023). Speculatively, actors that sought support from others in response to bleaching might also cooperate on other topics such as finding ways to increase visitor numbers to their reef locations (Hotelling 1929, Partelow and Nelson 2020). Spatial diversification was also associated with beneficial economic outcomes in the bleaching-affected sample (Appendix 3, Table A5) as hypothesized by Bartelet et al. (2022b), but this relationship only became significant in our combined regression model as discussed below.
For impacts from tropical cyclones, support-seeking was associated with an increase in perceived climate vulnerability one-year post-disturbance. This indicates that the support-seeking response may be qualitatively different between bleaching and cyclones, more relief-based for cyclones and perhaps more focused on collective action for bleaching (Bartelet et al. 2023b). Operators that took relief measures and diversified their products in response to impacts from tropical cyclones were more likely to have experienced a loss in visitor numbers on their tours (our economic sustainability measure) one year after the event. This could, for example, refer to operators who sold their reef boat (relief measure) and switched to land-based tour activities (product diversification). Land-based tour activities might have had a lower carrying capacity because operators could transport less visitors per day in a tour van as compared to their former reef boats. This finding raises the question of whether diversifying in response to climate change will necessarily be associated with positive outcomes (Goulden et al. 2013, Islam et al. 2021, Mohammed et al. 2021). Thus, from a public policy perspective, a better understanding of the strengths and weaknesses of different kinds of response diversity would support targeted financial support that provides a greater return on investment (Grêt-Regamey et al. 2019, Bartelet and Mulder 2020, Walker et al. 2023).
Predicting adaptation outcomes
The combined regression model that controlled for disturbance type, severity, the operator’s country government effectiveness, and baseline outcomes, indicated that spatial diversification (changing reef sites used on tours) can alleviate negative economic outcomes within a reef tourism sector affected by severe climate disturbances (Appendix 4). Spatial diversification might thus be a key adaptation response associated with social resilience (Tengö and Belfrage 2004, Goulden et al. 2013, Gonzalez-Mon et al. 2021). In our case, it helped operators to retain visitor numbers despite coral loss. Adaptation is a critical component of resilience (Walker et al. 2004, Janssen et al. 2007, Folke et al. 2010, Nelson 2011), and operationalizing it is necessary for understanding social-ecological resilience (Cumming et al. 2005, Allen et al. 2016). Our findings show that spatial diversification responses contributed to operator resilience to climate-related disturbances. Longer-term resilience will depend partially on ecological redundancy in the social-ecological system (Allen et al. 2011, Biggs et al. 2015), on whether operators have the flexibility to relocate their activities to other reef areas (Hoegh-Guldberg et al. 2019, Lin et al. 2023), and on the scale and frequency of the adverse climate phenomenon (Hoegh-Guldberg et al. 2019, Emslie et al. 2024).
Measures to improve the health of the coral reef (restoration) were strongly associated with perceived risk outcomes. Operators that implemented restoration measures were significantly more likely to see either an increase or a decrease in the perceived risk of being affected again by a similar climate disturbance in the next year, showing that human responses to climate change can potentially affect risk thresholds (Berrang-Ford et al. 2021, Carr and Nalau 2023). Although spatial diversification helped to absorb disturbance (resilience), restoration-focused responses were associated with attempts to influence and manage resilience by reducing disturbance risks (adaptability; Walker et al. 2004, Folke et al. 2010). Restoration-focused responses, however, were also significantly associated with an increase in perceived climate risk after a disturbance. Thus, similar responses might have different outcomes on perceived risks, or the restorative response consisted of different actions that affected risk outcomes in opposite ways. A more detailed understanding of people’s restoration-focused responses as well as their causal relationship with (perceived) climate risks is therefore required.
We note also that our broad study area (Asia-Pacific) contains a diversity and heterogeneity of cultural, economic, environmental, legislative, and social contexts. Although research that spans these contexts provides robust generalities, further analysis within specific case study settings could provide additional insights about context-specific relationships between adaptations and outcomes that were beyond the scope of this study. Such inquiries could, for example, draw on our data (Bartelet 2024) and/or collect additional context-specific quantitative indicators and qualitative information.
CONCLUSION
Evaluating the outcomes, effectiveness, and success of climate change adaptation measures and programs implemented around the world has become a topic of increasing academic and public interest. We operationalized and tested whether different adaptation measures, adopted by reef tourism operators in response to climate change impacts on coral reefs, had a positive or negative association with different indicators of adaptation success (risk, vulnerability, and sustainability). We found several meaningful relationships. Seeking support from others helped reef tourism operators to reduce their vulnerability to future impacts and was positively associated with economic sustainability. Taking relief measures in response to cyclone impacts was associated with increased vulnerability and reduced economic sustainability one year post-disturbance, although pinpointing the cause-effect relationships between this response and the experienced outcomes will require further in-depth research. Reef restoration measures were associated (both positively and negatively) with perceived risk levels, indicating that climate risks can potentially be addressed by adaptation measures (adaptability, managing resilience). Spatial diversification (of reef sites) was positively associated with resilience, i.e., the ability to retain visitor numbers despite environmental change. Our findings thus suggest that human adaptation to climate change addresses several elements of adaptation success, in particular perceived climate risks and vulnerabilities, and economic sustainability. Further improvements in the operationalization and measurement of adaptation success, empirical testing in different contexts, longer time series, and integration with social-ecological systems theory, could further contribute to attempts to better understand whether and how climate change adaptation can be considered successful.
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AUTHOR CONTRIBUTIONS
HB conceived the manuscript and developed the methodological approach with input from MB and GC. HB collected data in Australia, the Hawaiian Islands, the Mariana Islands, Japan, Fiji, and French Polynesia; HB and LB collected data in Indonesia; HB ran the analyses and wrote the first draft; MB and GC helped write and revise the manuscript.
ACKNOWLEDGMENTS
This research was funded by the ARC Centre of Excellence for Coral Reef Studies at James Cook University and a James S. McDonnell Foundation complexity scholar award to GSC. MLB was supported by an ARC Discovery Early Career Researcher Award (DE190101583). We are grateful to all tourism operators that participated in this research project during the extremely challenging pandemic times. We also thank those operators that expressed their interest in participating but did not find the time to do so. We acknowledge the assistance of Mallory Morgan (Bureau of Statistics and Plans, Government of Guam), Nana Watanabe, Mao Furukawa (University of the Ryukyus), and Kaylin Strauch (University of Hawaii) in collecting survey data in Guam, Saipan, Japan, and the Hawaiian Islands, respectively. We thank Yetti Rochadiningsih (Ministry of Research, Technology and Higher Education, Government of Indonesia, secretariat) for her efforts to facilitate our research permit process to collect data in Indonesia. Infographic and figure design by Eileen Siddins.
Use of Artificial Intelligence (AI) and AI-assisted Tools
Not applicable.
DATA AVAILABILITY
Input data available open-access in our data record (Bartelet 2024): https://doi.org/10.25903/6cr5-dx39
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Fig. 1

Fig. 1. Climate change adaptation success. Successful climate change adaptation measures contribute to reducing climate risk and/or vulnerability to a predetermined level without compromising economic, social, and environmental sustainability (Doria et al. 2009).

Fig. 2

Fig. 2. Infographic to show summary of our empirical strategy.

Fig. 3

Fig. 3. Observed changes in outcomes as measured one month before a climate disturbance and one-year post-disturbance. Outcomes are shown for the combined sample (Panel A) and for each respective subgroup (Panels B–D) and include climate risk (CR), climate vulnerability (CV), environmental sustainability (Env. S), economic sustainability (Ec. S), and social sustainability (SS). Results are based on our sample with n = 209 reef tourism operators (some indicators have lower sample size because of missing data). Outcomes, measured as the number of operators that experienced a change in each indicator, are presented as either no change (0), a decrease (-), or an increase (+).

Table 1
Table 1. Tourism-specific adaptive responses to climate disturbances on coral reefs. Responses are based on existing knowledge regarding microeconomic adaptation (Bartelet et al. 2022a), reef tourism industry expert consultation, and pilot interviews with tourism operators. Types of adaptation are sorted alphabetically.
Adaptive response | Description | ||||||||
Climate action | Enacting or participating in measures to reduce CO2 emissions of company, customers, and/or community. | ||||||||
Livelihood diversification | Diversifying to products/services outside of tourism. | ||||||||
Monitoring | Beginning monitoring climate and/or reef conditions. | ||||||||
Operational change | Making changes to the way the company is running its day-to-day operations (e.g., logistics, personnel, marketing, and/or sales). | ||||||||
Product diversification | Changing the type of tours or activities company was offering to tourists. | ||||||||
Relief measures | Selling of property (e.g., boats, equipment, and/or office space), reduction of workforce, and/or relying on savings. | ||||||||
Restoration | Enacting or participating in measures to improve the health of the coral reef (e.g., coral restoration aimed at transforming reefs from degraded to fully functional). | ||||||||
Risk protection | Seeking or purchasing protection from risks (e.g., insurance). | ||||||||
Spatial diversification | Changing reef sites company was visiting on tours. | ||||||||
Support-seeking | Seeking support from government, local community, and/or relatives. | ||||||||
Table 2
Table 2. Multidimensional actor-specific outcome indicators. Indicators were measured using recall data in two time periods: one month before and one year after a climate disturbance occurred. All variables were measured as ordinal variables using multiple-choice categories.
Outcome dimension | Outcome indicator | Description | Variable units | ||||||
Risk | (perceived) Climate risk | Perception of how likely it was that the company would be affected by specific (either bleaching or cyclone) climate disturbance in the next 12 months.† | (1) 0–20% (2) 20–40% (3) 40–60% (4) 60–80% (5) 80–100% |
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Vulnerability | (perceived) Climate vulnerability | Perception of how much the company’s revenue would be affected if specific (either bleaching or cyclone) climate disturbance were to occur in the next 12 months. | (-1) Somewhat positively (0) Not affected (1) Somewhat negatively (2) Very negatively |
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Sustainability | Environmental | Coverage of live coral at the reef sites the company is using. | 10 multiple-choice options ranging from 0–10% to 90–100%. |
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Economic | Company’s number of daily customers going on tourism activities. | 16 multiple-choice options ranging from 0–3 to > 500.‡ | |||||||
Social | Company’s ties to surrounding reef tourism operators. | (1) Weak ties (did not interact much with other operators) (2) Moderate ties (had some interaction with other operators, mainly through formal events) (3) Strong ties (frequently interacted and collaborated with other operators) |
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† For our risk indicator, we realized that asking about the probability of being affected by a climate disturbance a month before it happened might have been influenced by forecasts of the disturbance. We therefore included in our pre-disturbance question the following reservation: “We know that there were reports and forecasts (for example, from NOAA) some weeks before the bleaching event that warned for it to happen. But before any official forecast came out, how likely did you think it was for your company to be affected by coral bleaching in the next 12 months?” ‡ Because of differences in scales between reef operators in different locations (e.g., in Indonesia there are more and smaller operators, while in Australia there are fewer and larger operators), we added some additional detail in the smaller multiple-choice categories, while for larger operators we mainly used increments of 50 visitors. We decided not to use an open-ended question because respondents would have to look up their (exact) visitors’ data from years back, and this might have had decreased specific answer or overall participation rates in the survey. |
Table 3
Table 3. Explanatory variables used to explain adaptation outcomes in logistic regression models.
Variable | Units of measurement | ||||||||
Baseline (pre-disturbance): Actor-specific outcome indicator† |
See Table 2, MS: variable units |
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Government effectiveness: Level of institutional development in the country where reef operator is based |
(0) Lower (< 0.5) (1) Higher (> 0.5) |
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Disturbance type: Cyclone vs. bleaching |
(0) Bleaching impacts (1) Cyclone impacts |
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Experienced severity: Fraction of reef sites used on tours before disturbance that had more than a third of their area affected by climatic impact.‡ |
(0) None of reef sites (1) 25% of reef sites (2) 50% of reef sites (3) 75% of reef sites (4) All of reef sites |
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Adaptive responses: Monitoring, reef restoration, climate action, spatial diversification, product diversification, support-seeking, operational change |
(0) Adaptive response not adopted (1) Adaptive response adopted |
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† We standardized the non-binary contextual variables (baseline value and experienced severity) using z-scores, by subtracting the mean and dividing by twice the standard deviation (Gelman 2008). Dividing by twice the standard deviation standardizes each variable to have a mean of “0” and a standard deviation of “0.5”; this technically standardizes these predictors on a binary scale to make their coefficients directly comparable to binary variables and should be interpreted as the effect of a one-standard deviation change in the predictor variable on the response variable. ‡ We followed previous research that identified severe bleaching as more than a third of a reef being affected (Hughes et al. 2018). For locations where we studied adaptive responses to two consecutive bleaching events, e.g., Great Barrier Reef 2016 and 2017, we asked operators to score disturbance severity for each year separately and used the highest severity value in our analyses. |