How does social capital shape the response to environmental disturbances at the local level? Evidence from case studies in Mexico

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How does social capital shape the response to environmental disturbances at the local level? Evidence from case studies in Mexico Daniel Cohen-Salgado a, Eduardo García-Frapolli a, *, Francisco Mora a, Florence Crick b a

Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Aut´ onoma de M´exico, Antigua Carretera a P´ atzcuaro 8701, Col. Ex-Hacienda San Jos´e de La Huerta, CP 58190, Morelia, Michoac´ an, Mexico b International Institute for Environment and Development, 80-86 Gray’s Inn Road, WC1X 8NH, London, UK

A R T I C L E I N F O

A B S T R A C T

Keywords: Disasters Hurricanes Network analysis Social capital Adaptation

Even though there is an increasing recognition of the importance of supporting local level adaptation to disasters associated to climate change, it is still not fully understood how communities cope with them. In particular, a key dimension that is less well understood is the importance of social capital and the role it plays in enabling communities to respond. This research aims to address this gap through a case study with two semi-coastal communities in Mexico that have being hit by hurricanes recently. Structured and semi-structured interviews were carried out to collect data, which was analyzed through an interpretive approach. Network analysis was also carried out to explain the role of the different actors in the response to disturbances as hurricanes and the possible relationship with social capital. Results showed that social capital is relevant for coping with hurricanes; although bonding relationships were acknowledged as the most relevant for supporting when these events occur; linking relationships were also recognized as fundamental, particularly for responses on the long term (pro­ ductive diversification, improvements in infrastructure). These findings are useful for designing better-fitted schemes towards adaptation, as one of the communities showed more vulnerability due to an absence of links with external actors. Also, it demonstrates that climate change research with different approaches, such as network analysis, is fundamental to get more in-depth data to design context specific strategies to cope with disasters associated to this phenomenon.

1. Introduction Social capital has largely been acknowledged as a key component of resilience to stressors triggered by climate change, particularly at the local level [1,2]. Nevertheless, the elements behind it, such as networks, norms and trust [3] are yet to be fully understood in the way they shape actions to respond to the shocks prompted by climate change. When confronting any environmental disturbance, the way people relate, perceive and trust each other is fundamental to mold the possible pathways given after any environmental disturbance [4]. However, literature has mixed results [5]; in some cases, social relationships contribute to improve coping strategies, but sometimes mistrust and power imbalances can change the game [6,7]. This paper aims to address this issue by analyzing the relevance of social capital in coping strategies through a case study approach. We particularly looked into how two semi-coastal communities in Mexico

have responded to hurricanes, focusing on the role that social relation­ ships have had before, during and after these disturbances occurred. This research stands out from others as it delves into the dimensions of social capital, in a general sense, but also gains in-depth understanding about the reasoning behind the success or failure of certain strategies aiming at counteracting the effects of climate change, particularly with hurricanes. Through analyzing the mechanisms of response and the strategies on the short and long term, it puts forward empirical evidence for a more accurate perspective and exemplifies how people decide, relate, and understand the risks posed by hurricanes and climate change. Also, this study adds a new perspective to shed light into the mecha­ nisms of response, through network analysis. This approach provides an interesting understanding on the importance of relying on each type of social capital and how different social relationships can affect the perception of resilience of individuals, as well as the way and pace of recovery.

* Corresponding author. E-mail addresses: [email protected] (D. Cohen-Salgado), [email protected] (E. García-Frapolli), [email protected] (F. Mora), florence.crick@ gmail.com (F. Crick). https://doi.org/10.1016/j.ijdrr.2020.101951 Received 30 April 2020; Received in revised form 5 October 2020; Accepted 3 November 2020 Available online 14 November 2020 2212-4209/© 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Daniel Cohen-Salgado, International Journal of Disaster Risk Reduction, https://doi.org/10.1016/j.ijdrr.2020.101951

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All in all, this research 1) contributes to the literature on the aspects of social capital that are relevant for policy prescriptions; 2) recognizes in-depth characteristics underlying the types of social capital useful for coping with hurricanes; and finally 3) incorporates local actors through their own voices in the science of adaptation.

focusing on how inequalities can be accentuated after disasters, in places where unequal access to this capital already occurred [9,31]; this situ­ ation can be stressed by ‘bad practices’, such as bribery and patronage relationships, which are determinant to define the ultimate role of this capital [30]. Considering this, Brouwer and Nassengo [32] propose that social capital should be assessed at the household level, so that differ­ entiated opportunities and vulnerability are recognized more clearly within particular contexts.

2. Social capital and risk management The importance of social capital in risk and disaster management has been widely studied [7–9]. Although social capital has been used with different connotations in different fields of research [10,11], for the purpose of this study social capital is understood as the ‘features of social life – networks, norms and trust – that enable participants to act together more effectively to pursue shared objectives’ [3] cited in Ref. [10]: 112). Following this definition, social capital also includes sense of belonging, networks and support, trust, solidarity, and reciprocity as factors regu­ lating collective relationships [12]. There are different classifications for social capital; however, for the purposes of this research, we use the classification of structural social capital by Woolcock [13] and focus on the categories of bonding, bridging and linking social capital. Bonding social capital refers to ‘re­ lations between family members, close friends, and neighbors’, while bridging social capital refers ‘to more distant friends, associates, and colleagues’ [13]:10). The last type is linking social capital, which refers to the ‘capacity to leverage resources, ideas, and information from formal institutions beyond the community’ [13]:11). Although it might seems simple to classify something as complex as social capital, it is useful to undercover the possible relationship between the types of so­ cial capital and the responses identified. Moreover, these categories have proven useful in understanding the effects of social capital on risk management [14]. Scholars have also recognized the relevance of social capital as one of the key tools used by households and communities for coping with risks and uncertainty [15,16]. Following this, social capital influences the way an individual is prepared to cope with extreme weather events, such as hurricanes, as it provides ‘material and intellectual resources, disaster alerts, evacuation routes, support networks, and connection within the very same community, and with external actors’ [17]: 51). Historically, research has focused on general indicators/character­ istics of communities as a way to approach their preparedness [17]. For example, it has been shown that social capital is relevant in situations in which communities are neglected financially, and are confronted with phenomena such as floods [18]. Nevertheless, in-depth studies regarding the levels of influence of the actors within the local level are still to be carried out [18,19]. According to the literature on disaster risk management and hazards, social capital influence on disasters relief is mostly positive [7,20–23], as it is considered the base of collective response [24]. Discerning on the different types of social capital, bonding has been recognized as relevant in the short term, and linking as necessary on the long term [25–28]. In the context of collective influence, bonding was found to be more important in communities with more homogeneous characteristics [29], being useful to majorities rather than minorities. Bridging and linking, on the opposite, were more important on heterogeneous communities, serving mainly to minorities or ‘people with connections’ [29]. How­ ever, Monteil and colleagues [9] recognized that bridging and linking can be fundamental to build social cohesion depending on the particular context. Bonding has also been identified as particularly sensitive to disasters, if social configurations and organization are not restored rapidly aftershocks occur [30]. Also, there has been recent interest on social networks analysis as a way to explore how the different types of social capital integrate and have effects on the way people cope with disturbances ([8,19]; however, a straightforward comparison among networks of affiliation and networks of response to disturbances is a new insight that this research brings about. There is also a growing literature on the ‘dual’ role of social capital,

3. Methodology and case study sites 3.1. Mexico: the national context The Latin American region experiences ‘more climate-related haz­ ards per capita than any other region in the world’ [18]:19). Within this region, Mexico stands out having the highest number of storm-related hazards incidences –about 25 hurricanes per year– [18,33], which are expected to increase due to climate change [34]. Over the last decades, the number of hurricanes impacting the country has increased, gener­ ating economic damages of over $230 M (USD) for the period 1980–1999 [34]. There are not just economic losses but also human deaths, which usually affect not only coastal areas, but also inland ter­ ritories [35]. They are also a great hazard for low-income regions within the country, due to the vulnerability of their living conditions [36]. Community resilience in coastal areas, thus, is a fundamental challenge to be pursuit from different realms, in which social networks, commu­ nity engagement and participation (all elements of social capital), contribute greatly [23]. 3.2. Case study: two semi-coastal communities Two semi-coastal communities were selected to study their responses to hurricanes and to identify possible patterns among them. These communities are Los Ranchitos (LR), located in the Pacific coast of the State of Jalisco, and Tesoco Nuevo (TN), which is located in the State of ´n, on the limits of the Caribbean coast and the Gulf of Mexico (see Yucata Fig. 1). These communities were considered appropriate due to inter­ esting similarities and common characteristics. First, they are on semicoastal areas located around 8 kms (LR) and 20 kms (TN) inland, a sit­ uation that makes them particularly vulnerable to hurricanes, as was the case with hurricanes Jova in 2011 (LR) and Wilma in 2005 (TN). These regions stand out as strongly affected by tropical cyclones, compared with other regions in the country [37–39]. Both communities are located in tropical ecosystems, with strong differences between rain and dry seasons, relying heavily on water as a limiting factor [40,41]. In this type of socio-ecological systems, hurri­ canes can alter societies and ecological processes, having a strong impact on the economic activities, which make them particularly vulnerable [33,42]. Also, these communities are located in regions that are ecologically important. LR is adjacent to the Chamela-Cuixmala Biosphere Reserve (CCBR), and TN is located in the influential area of the Ría Lagartos Biosphere Reserve (RLBR) (see Fig. 1). According to Ferro-Azcona et al. [43]; protected areas in coastal areas have been largely overlooked from research on adaptive capacity and resilience, which is also a gap aimed to be filled by this work. Furthermore, both communities share a similar socio-economic context. They are ejidos, which is a communal form of land tenure that emerged as an outcome of the land redistribution and restitution policies following the Mexican Revolution [44]. In LR, most of the people carry out productive activities such as livestock and shifting agriculture (traditional milpa –maize, bean, and different vegetables– and fodder; [45], whilst in TN beekeeping and agriculture (milpa) are the main productive activities [46]. Other relevant economic and social indicators of the communities are shown in Supplementary Material- Appendix 1. 2

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Fig. 1. Location of the study sites.

distribution for the resilience indicators “Preparedness for constant events” and “Preparedness for future events” (binary variables). For the resilience indicator “Perceived level of help” (an ordinal variable), we fitted an ordered logistic regression model. We fitted one model for each combination of resilience indicator and social capital (35 variables, Supplementary Material- Appendix 3). All the models included the community and its interaction with the predictor of social capital. Wald tests of each model parameter were used to assess the relevance of each of the predictors. Models were fitted using the functions ‘glm’ and ‘polr’ in the R language [49]. Second, we applied network analysis to assess the structure of both affiliation and support networks within each community. Networks were defined as bipartite hierarchical networks, with houses as the lower hierarchy level and affiliation or support groups as the higher hierarchy level. We particularly assessed the degree of connectivity between the lower and the higher level using connectance and links per households indicators, as well as asymmetry [50]; these metrics were useful to describe the networks in a general sense. We also assessed the resilience of such networks to the loss of one of its components (particularly those in the higher level), as a measure of network resil­ ience using extinction slope and generality indicators (see Supplemen­ tary Material- Appendix 4 to revise the meaning of each metric). Network metrics were calculated using the ‘networklevel’ function in the ‘bipartite’ library for R [50]. We tested for differences between networks from the two communities using permutation test following Anjos et al. [51]. Such tests allow for comparison of pairs of networks after taking into account the size of the network (i.e. the differences in the number of nodes between the two networks).

3.3. Methodology Given the objectives of the research, we selected a mixed approach with qualitative and quantitative analyses. We carried out structured and semi-structured interviews with 31 households, (15 in LR and 16 in TN). This sample represents 60% and 41% of the total households of the communities [47], respectively. All the respondents were selected ac­ cording to their availability but without following any particular order (randomly). Each interview was given by the head of the household (mostly men) but included questions for considering the perspectives of the other members. The structured interviews had the purpose to characterize social capital in a broad sense, particularly with close-ended questions, whilst semi-structured interviews were used to gain in-depth descriptions on the different aspects associated with the impact of hurricanes (questions were asked regarding the latest event on each of the communities); both tools were rather complementary to identify the main findings of this research. The duration of each inter­ view (for both of the questionnaires) was between 60 and 90 min (See Supplementary Material-Appendix 2 for details on fieldwork logistics and questionnaires). For the analysis, we focused on an interpretive approach. Under this perspective, social objects and phenomena can only be understood within their particular context, from the vision of the actors involved [48]. Following this, semi-structured interviews were analyzed in a qualitative way through coding, by using Atlas.ti (version 1.6.0). Structured interviews were analyzed in two ways: for the open-ended questions, the same qualitative approach was used; for the close-ended questions, statistical analysis was carried out. Statistical analyses were performed to get complementary insights to the qualitative findings on the association between the capacity to cope with hurricanes and the social capital, particularly belonging to partic­ ular groups or the number of connections to different groups. We used two matching approaches. First, to test for the association between perceived resilience to hurricanes and the variables used for assessing social capital, we fitted generalized linear models with binomial

4. Results 4.1. Sense of belonging, support and trust One of the elements of social capital that has been traditionally used for assessing the level of organization within communities is the sense of 3

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belonging [12]. Even though this element is sometimes considered su­ perficial, in the sense that it does not delve into qualitative differences among members, it is a good indicator on the level of organization and social cohesion [12]. In LR, respondents recognized their affiliation to 6 different groups: Ejido group (80%), Religious group (60%), Livestock association (53%), Homegarden production group (20%), Local com­ mittee (“comisariado”, 13%), and School-related activities (6%). It is important to note that three of these associations can be considered as compulsory groups, as they are part of a legal structure: the ejido group (agrarian figure), livestock association (to get permits for trading live­ stock), and the local committee (representatives of the government). A very revealing fact is that most of the households (60%) only participate in 1 or 2 groups, whereas the remaining 40% participate in 3 or 4 groups at most. In contrast, inhabitants of TN recognized their participation in 14 different groups. The most important groups mentioned were the col­ lective cleaning group (or faena, 88%), the ejido group (81%), the local committee, and the religious group, both with 50% of the mentions. These types of groups are similar to the ones present in LR, with the exception of the collective cleaning group, which is in charge of cleaning the streets of the town and was established voluntarily among the members of the community. Contrasting with LR, in TN most households participate in 3 or more groups (88%), with some households (12%) participating in as much as 7 groups at the same time. In terms of the level of involvement and the dynamics within the groups, in both communities the respondents identified groups associ­ ated with productive activities as the most important for the household (60% in LR and 62% in TN), followed by religious groups in LR (20%) and collective cleaning (faena) groups in TN (25%). When it comes to the actual participation within the group, most of the respondents in LR considered that they participate actively or as leaders (60%), while a fewer percentage mentioned that they are only slightly active (40%). On the other side, in TN all of the people consider themselves as leaders (25%) or active participants (75%); this demonstrates a significant de­ gree of commitment to their groups. Support and personal connections are also common ways to approach social capital. In general, we found that both of the commu­ nities have strong local relationships to support each other, especially in case of emergencies (health issues, unemployment, safety). When it comes to the number of friends each household has, most of people consider that everyone or almost everyone within the community are their friends (73% in LR and 81% in TN). However, in terms of personal connections with organizations outside the community, most of the people in LR mentioned that they have connections outside (60%) —mainly with productive associations (67%)—, compared with only 44% in TN, which is mostly with non-governmental organizations (NGOS, 71.5%). Nevertheless, in TN most of the people recognized that they have more relationships with the locals than with outsiders (81%), which may be the reason behind the large number of groups found within TN compared with LR. When asked about money loans in case of emergency, most of the people in LR (67%) and TN (63%) considered that they would get help from someone (excluding family). Among the factors of social capital that are extremely difficult to measure, we find trust, solidarity and reciprocity [52]. Nevertheless, we assessed them through scales (level of agreement) about generic state­ ments utilized to assess social capital. In this regard, in TN most of the people generally perceive that people in the community have the will­ ingness to help you in case you need it (88%), as they feel accepted as part of the community (88%), compared with LR, where these per­ centages reach only 60% and 67%, respectively. Honesty (80% in LR and 75% in TN) and selfishness levels (53% in LR and 63% in TN) were very similar between the communities. Despite the generally positive opinion regarding support, when the questions were directly asked about some practical (and very common) scenarios, we found that trust and support would decrease in both cases; this can be exemplified in the question about trust for leaving a baby in charge of someone you trust, reaching

only 33% in LR and 25% in TN (positive response). 4.2. Coping strategies and stakeholders involved As the first stage of disaster risk management, prevention and pre­ paredness are essential. In both communities, the inhabitants described different strategies for preparing prior to the arrival of a hurricane. It stands out that there were more mentions about actions recognized in TN, meaning that more respondents implement more actions. Tradi­ tional recommended actions for preparation such as protecting docu­ ments and clothes (63% in TN and 40% in LR) or storing food and water were mentioned (56% in TN and 13% in LR). However, a difference that changed the whole panorama between the two communities was that in TN all the households had a section of their houses constructed as an “anti-hurricane shelters”. These shelters were built with financial assistance from the Federal Government (Natural Disasters Fund [FONDEN]), which came after hurricane Wilma (according to the re­ spondents). This external intervention was considered to be the most effective protection in case a hurricane occurs in TN (56%). In LR, most of the houses are primarily constructed with wood, cardboard, and sometimes galvanized sheets. This difference is also clear when in TN respondents mentioned that evacuation is just getting inside the house, while in LR evacuation means looking for a shelter. Within this stage, emergency alerts are also fundamental; however, only TN inhabitants recognized the existence of a warning system (88%), which is nonexistent in LR. Regarding the damages of hurricanes (see Table 1 for details), there were significant differences among the communities. Interestingly, people in TN perceived not only more negative effects of the hurricane, but also the effects were recognized in different realms by its own in­ habitants (health issues, economy, ecological issues). Another difference was that in LR, 67% of the respondents mentioned not only negative impacts but also positive effects of the hurricane (i.e., rain), recalling the relevance that water has in this region, mostly because they consider it the limiting factor to develop any productive activity (cattle raising, mainly). For each problem, respondents indicated the way of solving it and the possible support they received from different stakeholders. TN stands out as a place with generally more support from different stake­ holders (7 in total) to respond to and recover from hurricanes. The neighbors, the government, and NGOS are even more important than family, indicating that bridging and linking relationships are funda­ mental. This clearly relates with the information of the previous section, as the close bonds within the community and the links of each household showed through affiliation were relevant. As for LR, government is the only relationship with outsiders (linking), strongly relying on bonding relationships. This finding is related with the sense of belonging and the level of trust they recognized, mainly with the family. Consequently, among all the stakeholders involved, the government Table 1 Damages/problems caused by hurricanes Jova in LR and Wilma in TN. Los Ranchitos (LR) Type of damage Damages to my house (ceilings, mainly) Damages to the fences of my work land Fallen trees

4

Tesoco Nuevo (TN) Mentions (%) 100 67 67

Broken appliances Collapsed roads

53 33

Lost Crops

20

Type of damage Flooding (streets and roads) Lost Crops Damages to my house (ceilings, mainly) Lost apiaries Source of food for bees damaged Human health Fallen trees Backyard animals lost

Mentions (%) 75 69 44 38 31 19 12 12

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(33%) and the community (33%) in LR and the NGOS (44%) and the community (38%) in TN were ranked as the most helpful. Despite this, in case of a similar event occurs in the future, most of the people consider that the government should be responsible for helping them (LR: 33%, and TN: 81%). Looking at the type of support from all the different stakeholders, most of the activities mentioned are related with short and rapid re­ sponses. In total, interviewees in LR mentioned 9 support activities, while interviewees in TN mentioned 13 different activities; all of them are summarized in Table 2. As Table 2 shows, most of the support is aimed at rapid responses, which is primarily given by the government and the community. How­ ever, it is worthy to note that in TN some of the support activities, such as the concrete houses built by the government, or the microcredits program implemented by an NGO were mentioned as responses to the hurricane, and they represent long-term strategies. Generally, in TN most people were satisfied with the amount of help they received, considering it was enough or more than enough (63%), compared with LR (40%), where a lack of support is evident. The other way around, respondents considered they helped other members of the community sufficiently (73% in LR and 94% in TN). All in all, the influence of the different supporters did have an impact if we consider the perception on preparation to confront such a phe­ nomenon in the future. In the case of LR, less than half of the re­ spondents (47%) considered themselves ready to deal with a hurricane in the future, compared with 88% in TN. When asked why, in LR the most common answer was that they lack of strong infrastructure (i.e. shelter: houses to pass through strong winds and rain of the hurricanes; 12 mentions). Meanwhile, in TN they recognized their own experience on previous disasters as their main strength (7 mentions). However, when asked about the perception of preparedness in case hurricanes intensify their strength, this percentage drops dramatically to 25% in TN, due to people considered that they would lose their jobs (4 men­ tions) and because of the short recovery time in the forest and the economic implications this may have (3 mentions). In the case of LR, this percentage keeps almost the same as before (33%), quoting the lack of infrastructure as the main issue (7 mentions). The risk on having hur­ ricanes more constantly is highly recognized by both communities, as stated by one of the respondents in the following quote: ‘In that case you are reminding me a saying that says that you ‘climb the three to avoid being gored by a bull’, but imagine if the bull also climbs the tree, then you are screwed’ (H1C1).

4.3. Relationship between belonging to groups and the support network Statistical analyses indicate that membership to particular groups or the number of groups to which households are linked may have an impact on the level of perceived help, but not on the level of pre­ paredness for present or future events (Table 3). In LR, the level of perceived help is positively associated with belonging to the Ejido, but negatively associated with belonging to the Ejido Council and the School group. This would probably reflect the impact that this collective figure possesses on the territory, as a way to deal with difficult situations. The Ejido group can be helpful to access to information and resources, from peers and the government, to design preparation strategies and to obtain aid more easily; on the other side, ejido council or school groups are usually groups with conflicts and power imbalances that may be perceived as negative. As for the descriptive variables related with social capital, such as the feeling of a lack of support when there is sickness, or perceived selfishness, they also showed a negative effect in the level of perceived help in LR. This effect could be explained as they are negative aspects of social capital per se. Most of these effects seem to be absent in TN, as suggested by the value of the interaction effects in the models. However, it is worth noting that in TN households seem to perceive both higher preparedness for future events, and higher levels of having received enough help, as suggested by some of the models. To complement this assessment, we used network analysis to explore the possible relationship between the stakeholders involved in the coping strategies and the affiliation to groups in each household, recalling its relevance as a measurement of social capital and being the most obvious and direct relationship (see Supplementary Material- Ap­ pendix 4 for selected statistics on the networks). We also carried out this analysis considering that it relies on the assumption that ‘relationships between individuals (or groups) are of critical importance and are the dominant driver of information flows, resource exchange, collaboration, and action’ [53]. Fig. 2 shows the networks for LR. As it can be observed, it is clear that in the case of affiliation, some households concentrate more affiliations to groups (1.71 links in average), compared with the second network (support), which shows a more even support from the different actors (2.15 links in average). At the same time, due to the few number of groups/actors on the superior level, generality indicator is different among the networks (8.10 and 11.59, respectively), which means that the actors on the network of support have on charge more households and that could lead to the community to be more vulnerable in case one of them disappears. This is also shown by extinction slope, which reaches 13.36 on the second network (support), compared with 4.62 for the first one (affiliation), meaning that if a support actor (su­ perior level) disappears, the households could be more affected (inferior level) . As for the case of TN (see Fig. 3), both of the networks seem to have a more even record for the households (2.23 average links on the first network and 2.76 on the second one). Generality indicator varies in a very similar way as in LR (8.26 and 10.37); however, extinction slope is rather different, as the first network scores 2.81 and the second one 6.92, both values below the ones we found in LR. Still, in both of the com­ munities the networks for support are more vulnerable than affiliation networks. Another stark difference between both of the communities is regarding the asymmetry of their networks. Despite all the networks are unbalanced (as all the values are negative), the scores for web asym­ metry for TN (− 0.0642 in the affiliation network and − 0.28 in the support network) were smaller than the ones for LR (− 0.42 in the affiliation network and − 0.57 in the support network) meaning that networks on LR are more unbalanced than in TN. It is also remarkable that in LR most of the actors of support are associated with bonding and bridging relationships (neighbors, family, friends), being the government the only linking type of actor (outsider); at the same time, all the seven groups identified on the affiliation network are related with bonding and bridging relationships, as they are

Table 2 Support after a hurricane activities mentioned in Los Ranchitos and Tesoco Nuevo. Los Ranchitos (LR) Support activity Monetary contributions for appliances Cleaning aid Food/Water supply Cleaning roads Shelter Construction materials

Tesoco Nuevo (TN) No. Mentions 8

Support activity

7 7 6 5 5

Concrete Houses Cleaning aid House repairs Securing house Blanket/Clothing supply Emergency transportation Cleaning roads Shelter Construction materials Grains Microcredits Health support

House repairs

4

Fences repairs Blanket/clothing supply

2 1

Food/Water supply

No. Mentions 46 14 13 11 5 3 2 2 2 2 2 1 1

5

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Table 3 Summary of models assessing the perceived capacity to cope with hurricanes (Response) in relation to belonging to particular groups or the number of connections to different groups (Model). Only those models with significant predictors (P < 0.05) are presented. For Preparedness future events response, generalized linear models with a binomial distribution were fitted. For Enough help level, proportional odds logistic regression were fitted. In this case, the terms 3|2 and 2|1 represent the log odds of having enough help level <3 and < 2, respectively. Statistice is Student’s t for Enough help level and z for Preparedness future events. The term Community always refer to how Tesoco Nuevo compares to Los Ranchitos. Response

Model

Term

Estimate

SE

Statistic

P value

Preparedness future events

Community

Enough help level

Ejido

Intercept Community Community Ejido Community × Ejido 3|2 2|1 Community Ejido council Community × Ejido council 3|2 2|1 Community School Community × School 3|2 2|1 Community Borrower locals Community × Borrower locals 3|2 2|1 Community Supp sick None Community × Supp sick None 3|2 2|1 Community Type local support agric Community × Type local support agric 3|2 2|1 Community Level selfishness (Linear) Level selfishness (Quadratic) Level selfishness (Cubic) Community × Level selfishness (Linear) Community × Level selfishness (Quadratic) 3|2 2|1

− 0.13 2.08 28.68 27.21 − 28.6 26.94 28.95 0.6 − 17.22 17.1 − 0.07 1.82 0.67 − 25.72 26.08 0.09 1.95 2.81 0.55 − 2.73 0.52 2.63 0.58 − 15.97 16.41 0.09 1.96 0.7 − 16.21 16.21 0.09 1.95 13.44 − 5.95 − 25.96 − 1.36 7.14 27.6 12.19 14.6

0.52 0.92 0.62 0.6 0.73 0.64 0.67 0.84 0.48 0.48 0.57 0.68 0.74 0.82 0.82 0.55 0.68 1.36 1.11 1.62 0.88 1.03 0.78 0.53 0.53 0.55 0.68 0.87 0.47 0.47 0.55 0.68 1.01 1.2 1.27 0.75 4.19 1.11 0.67 0.86

− 0.26 2.27 46.25 45.07 − 39.32 42.06 43.09 0.72 − 35.69 35.44 − 0.12 2.67 0.9 − 31.22 31.64 0.16 2.88 2.07 0.49 − 1.69 0.59 2.55 0.75 − 29.95 30.77 0.16 2.89 0.81 − 34.26 34.26 0.16 2.88 13.25 − 4.94 − 20.37 − 1.8 1.7 24.77 18.27 16.99

0.80 0.02 <0.001 <0.001 <0.001 <0.001 <0.001 0.47 <0.001 <0.001 0.91 0.01 0.37 <0.001 <0.001 0.87 <0.001 0.04 0.62 0.09 0.56 0.01 0.46 <0.001 <0.001 0.87 <0.001 0.42 <0.001 <0.001 0.87 <0.001 <0.001 <0.001 <0.001 0.07 0.09 <0.001 <0.001 <0.001

Ejido council

School

Borrower locals

Supp sick None

Type local support agric

Level selfishness

Fig. 2. Network of affiliation (a) and Network of support after hurricanes (b) in LR. blue boxes show the households and orange boxes show the groups to whom they belong and the actors supporting after a hurricane, in the same order.

6

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Fig. 3. Network of affiliation (a) and Network of support after hurricanes (b) in TN. blue boxes show the households and orange boxes show the groups to whom they belong and the actors supporting after a hurricane, in the same order.

groups originated and developed within the community. On the other side, in TN there are more support actors associated with linking re­ lationships than with bonding or bridging, as the majority of actors recognized as relevant support are outsiders. As in the case of LR, most of the groups of the affiliation net are bonding or bridging groups; how­ ever, there are some groups, such as the Payment for Environmental Service group (PES), brigade PRONATURA and the Microcredits group, which have been triggered or motivated by external actors. Summarizing, there are 3 groups in LR that seem to dominate the network of affiliation whereas in TN there are many more groups to which households belong, so they are not as dependent on one group. As for the support network, it seems like in TN households have less of dependence than LR, mainly because in LR there are less actors sup­ porting than in TN, where they are linked to more actors, so they have more sources of support (diversified support), which shows greater ev­ idence of bridging and linking capital. Considering this, we assessed the selected statistics of the networks (shown in Supplementary Material- Appendix 4) through a statistical analysis, to test if there is a correlation between the nets. We found that, extinction slope was effectively correlated in both nets (p = 0.006), meaning that both networks are vulnerable to a lack of an actor at the superior level. This probably means that both of the networks rely heavily in the actors present at the superior level, which make them vulnerable to changes in social relationships that may harm the affilia­ tion or support given to the households. We also found a correlation between the number of links per household (p = 0.03), which confirms that the relationships between groups and households vary in a similar fashion when referring to support when hurricanes. As for the other statistics of the network, we did not find statistically significant results.

be the most important action. Third, in terms of the type of support received, both communities received mainly short and rapid responses, however in LR these responses were carried out by the government, while in TN the responses were implemented by the community and NGOs. According to the network analysis, we found that there is strong vulnerability of households in terms of the disappearance of an actor supporting them as they strongly rely on just one or two. A relevant aspect to consider is that both communities are legally recognized as ejidos. This form of organization forces them to have formal groups for managing and making decisions regarding their ter­ ritory. That might be the reason why in LR most of the people recognized “compulsory” groups (ejido group, local committee), as the most important for them. However, legal binding is not always the best motivation for getting people together, as it is not a spontaneous pro­ cess, so it can be very fragile and not long lasting. This is reflected if we compare the likelihood to attend meetings and the way decisions are taken in LR (regular attendance rate, committees make decisions) versus TN (highly attendance rate, meetings for making decisions) where autonomous organizational initiatives are occurring, beyond the compulsory groups (cleaning groups “faenas”, beekeeping groups, etc.) Social relationships act then as a mean to ensure long-term actions to prevail autonomously, constituting the basis for collective action [24]. This has implications when it comes to make policies towards adaptation more operational, as it is fundamental to trigger the factors that moti­ vate people to act autonomously [54], so that barriers to better confront environmental disturbances can be overcome [55]. As showed throughout the results, local support is key to confront hurricanes. Even though in LR the community shows significant levels of distrust and less cohesion, still, people considered that support among the members of the community as the second most important group when emergencies occur. The same situation occurred in TN, where local people were mentioned as the most important actors when emer­ gencies occur. Rapidness and urgency of basic needs are probably the reasons why sometimes bonding results more important, as stated by the same respondents. This is why relationships with neighbors (bonding) have always been important and widely recognized in the literature [56]. Proximity and rapidness, as well as urgency, are key elements on the formula of local bonding that are irreplaceable in societies under conditions of uncertainty, such as the ones that climate change affects. This finding aligns with a large body of literature that supports the idea of bonding social capital as fundamental in the short term [25,27–29]. Despite the fact that we did not find formal protocols to prepare for the impact of a hurricane at the household and community levels, there are several different activities carried out to prevent damages. Within

5. Discussion and conclusions We begin the discussion by presenting a summary of the most important findings of this research. First, this study shows that there is a positive correlation between belonging to a group with the level of preparedness to extreme events, such as hurricanes. While most households in LR are affiliated to 1 or 2 groups, in TN the vast majority of households belong at least to 3 different groups, with some house­ holds participating in as much as 7 groups at the same time. Productive groups were recognized as the most important in both of the commu­ nities. Second, prevention and preparedness were recognized as key for facing the impacts of hurricanes. In this aspect, we found important differences between communities, mainly that TN implemented more actions, including having “anti-hurricane shelters”, which turned out to 7

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those, it is remarkable the role of local relationships for carrying on particular decisions when the community is at risk, particularly in TN, where there is a warning system and a good level of organization after a hurricane occurs, which is dismissed almost immediately in LR. This might be occurring due to the absence of strong bonding relationships, which led to the disappearance of the support and trust generated due to the sense of urgency posed by the hurricane. Nevertheless, it is possible to implement actions to strengthen household’s resilience considering the individualism prevailing on the community by analyzing the main factors shaping the perception and the actions taken to cope with haz­ ards, as proposed by Sandanam and colleagues [57]. However, it is fundamental to strengthen local capabilities from the inside, considering that relationships among members of a society are complex and require a special treatment from the perspective of the locals. Programs with targets established or strategies designed from the outside are usually decontextualized, which makes them more difficult to be appropriated by the local communities. Bottom-up approaches, more likely participatory, can have more efficient outcomes. Notwithstanding the fact both communities recognized the relevance of the bonding relationships, even in the case of LR people still consid­ ered that support from external actors (linking) was more important than the support within the community (bonding or bridging). Although the opposite occurred in TN, where community was more important than the government, still, strong support came also from NGOs and the government in previous events. These results have been similar in other case studies where external influence was fundamental to overcome negative impacts of hurricanes mainly in the long term [18]. Despite the importance of strong bonding relationships, linking has always been crucial for local communities to respond adequately to external shocks [13], particularly in a long-term sense [25,26]. Results showed that respondents positively evaluate linking, but there are important differences between the groups working on short and long term actions. Activities such as PES, or the microfinance program car­ ried out in TN, seem to have helped local people in strengthening their vertical links with outside groups. These programs have a long-term logic compared with many of the aid programs received by the com­ munities, particularly in the case of Mexico, where social aid has been developed for decades with a paternalist approach [58]. Shelters in TN were also built due to strong links with outsiders, and this infrastructure turned out to be most important for locals in their perception about preparedness. In fact, this was one of the biggest differences between the two communities: TN had strong capabilities and infrastructure that was developed through those vertical relationships with the external groups. Having these relationships that cross group boundaries in a vertical di­ rection was recognized as another stronghold in case of emergencies. Reliance on external actors can make the difference on the vulnerability to hurricanes; within this context, the stronger the links with the outside, the better the outcomes. However, as pointed out by Hung [59]; strong dependence on external actors (such as NGOS or the government) may hamper the autonomy of the communities to develop actions by them­ selves, converting the external support in a substitute of autonomous initiatives [54]. We also found evidence in this regard, as we identified through the network analysis that there is evidence of strong vulnera­ bility to the absence of an actor supporting the aid scheme. Moreover, differences in the access and the type of social capital can accentuate inequity dynamics [60], as the “more connected” households usually benefit from these connections, as it has been shown in other studies [29,31]. This also means that differences in access to social capital “can reinforce barriers to recovery for groups already marginalized” [24]. In order to trigger efficient networks that can work as adaptation tools, trust and reciprocity should be addressed from inside the com­ munity. In this regard, outsiders can have a role as supporters, but they are completely unable to substitute local bonding and trust among the members. Still, spaces for collective creativity and experience sharing are highly valuated as practices for improving internal issues, and they can actually make the difference. The same difficult experiences, such as

hurricanes, have brought about collective learning that must be capi­ talized to trigger communication among the members of the community and also with the external actors. At the end, we must always remember that there are no one and only recipe, so strategies should always be focused on particular contexts. However, as a general rule, and ac­ cording to the results of this research and others [21,64], strengthening social capital seems like a general base that should be persecuted as an opportunity to confront the risks posed by climate change [28,61]. Networks analysis supports the idea of empowering social relation­ ships, differentiating how bonding and bridging relationships bring about different outcomes, compared with linking relationships. This approach is still new on the literature of disasters and hazards [30,62], so further research can and should focus on studying differentiated ac­ cess of the households to social capital, and how power balances and “bad practices” [31] associated with this access can hamper the positive potential of social capital. This differentiated distribution of social capital can also influence the way and pace of recovery [28,29], so it is particularly important for developing countries, where inequity has been historically behind marginalization processes, amplifying vulner­ ability to climate change impact. It is also crucial to delve into some other social characteristics and structures that shape “responsibility, vulnerability and decision-making power of individuals and groups in relation to climate change”, as pro­ posed by Kaijser & Kronsell [63]; within the intersectionality frame­ work. Variables such as gender, economic status, ethnicity, age, etc. are relevant to social capital as they intersect and regulate how people relate to each other in particular situations and real-life scenarios, thus, being relevant to design situated adaptation strategies in the future. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledegment This project was funded by the Programa de Apoyo a Proyectos de ´n e Innovacio ´n Tecnolo ´gica (PAPIIT) - Universidad Nacional Investigacio ´noma de M´exico (Project IN300514). The first author would like to Auto thank the CONACYT and Guerrero State Government for the Scholarship granted to develop his graduate studies (391576) at the Department of Geography and Environment, London School of Economics and Political ´n Science, which framed this project.The authors thank Oscar Salmero and Diego Astorga de Ita for their support during the fieldwork. They also thank Georg Odenthal for his support to create the figure of the location of the study sites. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.ijdrr.2020.101951. References [1] J.W. Smith, D.H. Anderson, R.L. Moore, Social capital, place meanings, and perceived resilience to climate change, Rural Sociol. 77 (3) (2012) 380–407. [2] Y. Guo, J. Zhang, Y. Zhang, C. Zheng, Examining the relationship between social capital and community residents’ perceived resilience in tourism destinations, J. Sustain. Tourism 26 (6) (2018) 973–986. [3] R. Putnam, Turning in, turning out: the strange disappearance of social capital in America, Political Science and Politics 28 (1995) 664–683. [4] B.L. Soler, Disaster Risk Reduction and Resilience through Social Capital: A Case Study of the Lived-Experiences from Hurricanes Harvey, Irma and Maria, Doctoral Thesis, Northeastern University, Boston, USA, 2019. [5] E.L. Tompkins, W.N. Adger, Does adaptive management of natural resources enhance resilience to climate change? Ecol. Soc. 9 (2) (2006) 10. [6] E.J. Castellanos, C. Tucker, H. Eakin, H. Morales, J.F. Barrera, R. Díaz, Assessing the adaptation strategies of farmers facing multiple stressors: lessons from the

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