Biological Conservation 169 (2014) 6–13

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Biological Conservation

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Perspective

Linking regional planning and local action: Towards using social networkanalysis in systematic conservation planning

0006-3207/$ - see front matter � 2013 Published by Elsevier Ltd.http://dx.doi.org/10.1016/j.biocon.2013.10.015

⇑ Corresponding author at: Global Change Institute, University of Queensland, Brisbane, QLD 4072, Australia.E-mail address: morena.mills@uq.edu.au (M. Mills).

Morena Mills a,b,⇑, Jorge G. Álvarez-Romero b, Ken Vance-Borland c, Philippa Cohen b,d, Robert L. Pressey b,Angela M. Guerrero e, Henrik Ernstson f,g

a Global Change Institute, University of Queensland, Brisbane, QLD 4072, Australiab Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD 4811, Australiac The Conservation Planning Institute, Corvallis, OR 97330, USAd WorldFish, P.O. Box 438, Honiara, Solomon Islandse Centre of Excellence for Environmental Decisions, University of Queensland, Brisbane, QLD 4072, Australiaf African Centre for Cities, University of Cape Town, University of Cape Town, Pvt Bag X3, 7701 Rondebosch, South Africag Department of History, Stanford University, 450 Serra Mall, Building 200, Stanford, CA 94305, USA

a r t i c l e i n f o

Article history:Received 12 December 2012Received in revised form 14 October 2013Accepted 23 October 2013

Keywords:Spatial prioritizationNatural resource managementConservation area designSocial networkImplementation

a b s t r a c t

Social networks play an important role in facilitating effective and sustained connections between peopleresponsible for regional conservation plans and those responsible for local conservation actions. Yet, fewstudies have utilized social network analysis in systematic conservation planning initiatives; this, in spiteof social network analysis being developed as a structural and relational approach to describe and ana-lyze the characteristics of patterns of relationships that make collaborative efforts more or less effectiveat solving natural resource management problems. Systematic conservation planning provides a frame-work for allocating actions in time and space to promote the conservation of biodiversity. Our study dis-cusses three potential contributions of social network analysis to systematic conservation planning:identifying stakeholders and their roles in social networks, and characterizing relationships betweenthem; designing and facilitating strategic networking to strengthen linkages between local and regionalconservation initiatives; and prioritizing conservation actions using measures of social connectivityalongside ecological data. We propose that social network analysis has the potential to be a valuable toolto support decision making in conservation planning. We identify challenges and future research ques-tions to be addressed to allow the integration of social network analysis into conservation planningprocesses.

� 2013 Published by Elsevier Ltd.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72. Identifying stakeholders and their roles in a social network and characterizing relationships between them . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73. Designing and facilitating strategic networking to strengthen linkages between local and regional conservation initiatives . . . . . . . . . . . . . . . . . 94. Prioritizing conservation actions using measures of social connectivity alongside ecological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95. Challenges of incorporating SNA into systematic conservation planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

M. Mills et al. / Biological Conservation 169 (2014) 6–13 7

1. Introduction

Conservation planners are increasingly aware that effective andsustained connections between people responsible for regionalsystematic conservation plans and those responsible for local con-servation actions (e.g. natural resource management, educationprograms) are critical for achieving effective protection of ecosys-tems (Briggs, 2001). However, to translate what this means intopractice is a recurrent challenge. Failure to address this problemhas resulted in ‘paper plans’ (Knight et al., 2006), or suboptimal,localized, conservation outcomes from ad hoc conservation ac-tions. In addition to our poor understanding of the necessary con-nections between plans and actions, the implementation ofconservation plans is further limited by the lack of considerationof social variables influencing the implementation of conservationactions, including governance arrangements, capacity for imple-mentation, and individuals’ values and willingness to participate(e.g. Cowling and Wilhelm-Rechmann, 2007). The social sciencesoffer a rich literature and numerous tools that can aid conservationplanners to understand these variables, some of which have beenapplied in conservation science (e.g. Ban et al., 2013; Knightet al., 2010; Raymond and Brown, 2011; Mills et al., in press).The relevance and relative importance of these social variables,and when and how these can be considered within planning pro-cesses, will depend on the social context.

In conservation initiatives, social networks can emerge fromcollaborative relationships (e.g. information or resource exchange)between stakeholders concerned with achieving common objec-tives. We define a social network as the set of relationships be-tween stakeholders involved in or affected by conservationinitiatives, including individuals, and government and non-govern-ment organizations. Social networks facilitate information sharingand collective action, and are an important component of socialcapital (Grootaert and van Bastelaer, 2001). Social capital is definedas ‘‘social organization such as networks, norms, and social trustthat facilitate coordination and cooperation for mutual benefit’’(Rohe, 2004). The patterns of relationships and structural charac-teristics of social networks, and not merely the absence or presenceof relationships between stakeholders, are critical in facilitating (orhindering) effective governance processes and positive environ-mental outcomes (Bodin et al., 2006; Bodin and Crona, 2009). Forexample, some natural resource management studies suggest thatthe power and influence held by an organization can be related toits position and influence within a social network (Ernston et al.,2008; Ernstson, 2011; Borgatti et al., 2009). Social network analysis(SNA) describes a set of methods to systematically measure, de-scribe, analyze and interpret patterns of relationships between ac-tors; in this case stakeholders participating in governanceprocesses (Bodin and Prell, 2011; Scott, 2000; Wasserman andFaust, 1994).

There has been an increased emphasis on incorporating socialdimensions into conservation planning (Cowling and Wilhelm-Rechmann, 2007; Knight et al., 2006; Mascia et al., 2003). In thiscontext, SNA can provide a useful perspective on the opportunitiesand constraints for conservation action associated with the gover-nance of natural resources within a region (e.g. Bodin and Crona,2008; Knoot and Rickenbach, 2011; Weiss et al., 2012). SNA hasthe potential to add value to the planning process and strategicallyguide actions that strengthen connections between conservationplanning and conservation action. In particular, SNA helps to gaina broader view or ‘map’ of the landscape of interactions among lo-cal and regional planning actors. When contextualized in researchand assessment reports, these ‘maps’ could be used to facilitatediscussions among conservation managers, and the strategic allo-cation of resources to improve collaboration. In particular, SNA

has been suggested as a potentially useful tool for identifying,understanding and involving stakeholders (Vance-Borland andHolley, 2011; Prell et al., 2009), identifying conservation opportu-nities and constraints (Knight et al., 2010), and in helping to ensuremultiple scales of action are linked or coordinated (Guerrero et al.,2013). However, the practical mechanisms and challenges fordoing all this have not been fully explored.

In this study, we explore ways in which data on social networksmight inform the process of systematic conservation planning(Margules and Pressey, 2000; Margules and Sarkar, 2007; Presseyand Bottrill, 2009). Specifically, we explore three options for incor-porating spatially-explicit data on social networks into conserva-tion planning: (1) identifying stakeholders and their roles in thesocial network, and characterizing relationships between them,to achieve more effective stakeholder collaboration; (2) designingand facilitating strategic networking to strengthen linkages be-tween local and regional conservation initiatives, thus enablingeffective planning and implementation of conservation actions;and (3) prioritizing conservation actions using measures of socialconnectivity alongside ecological data. We present theory and casestudies to illustrate approaches for each option. We draw on find-ings from northern Australia and the Philippines to illustrate howinformation about social networks can inform conservation initia-tives. For Solomon Islands, we demonstrate how data on social net-works can be represented spatially by discussing an initiative thatexplicitly aimed to build and promote a social network for effectiveresource management. We also discuss some of the challenges andlimitations of using SNA and highlight future research needed forits potential in conservation planning to be realized.

We acknowledge that there are several other social processesthat influence and shape conservation planning—ranging from leg-islation to micro-scale everyday practices of social learning (e.g.Barthel et al., 2010; Reed et al., 2010) – which are not easily cap-tured within a social network perspective. However, it is beyondthe scope of this study to discuss how social networks relate toother social processes in conservation, although such discussionshave been started by Reed et al. (2010), Crona et al. (2011) andErnstson et al. (2010). Here we focus our contribution on how a so-cial network perspective and its methods can contribute, and beapplied, to conservation planning.

2. Identifying stakeholders and their roles in a social networkand characterizing relationships between them

Active involvement of stakeholders from the outset is critical ifconservation plans are to be translated into effective actions(Knight et al., 2006; Pierce et al., 2005). Among the first steps ofconservation planning, therefore, is identifying resource users,conservation organizations, scientists, and government organiza-tions that could affect, or be affected by, conservation decisions(CMP, 2007; Pressey and Bottrill, 2009). There are multiple toolsthat can help identify who these stakeholders are and how theyinteract (e.g. stakeholder analysis; Grimble and Wellard, 1997;Prell et al., 2009; Reed et al., 2009). SNA is among these, helpingto elucidate stakeholders and their relationships, which can be eas-ily overlooked by less systematic appraisals. For example, in Solo-mon Islands, SNA was used to examine a formal governancenetwork – the Solomon Islands Locally Managed Marine Area Net-work (SILMMA) – established to support and promote marine re-source management and conservation outcomes by connectinggovernment and non-government agencies working across thecountry (Cohen et al., 2012). According to formal network mem-bership (defined by criteria established by SILMMA itself) ten local,national and international organizations are easily identified asstakeholders (Fig. 1). However, through using SNA, SILMMA

Fig. 1. Cross-scale social networks involved in adaptive co-management of marine resources in Solomon Islands. Black lines represent connections across local, provincial,national and international scales. The formal network, constituting the Solomon Islands Locally Managed Marine Area Network, is depicted in the national layer where tenorganizations are represented by grey squares. Non-member national organizations are represented by black squares. Sizes of squares represent in-degree centrality,representing the number of direct links each actor has to others in the network, including all organizations at local, provincial and national scales (Hawe and Ghali, 2008). (a)Relationships between the ten formal network members and their co-managed sites. (b) Relationships between national and provincial organizations supporting co-management. (c) Relationships between the ten formal network members and international agencies supporting co-management. Modified from Cohen et al. (2012).

8 M. Mills et al. / Biological Conservation 169 (2014) 6–13

representatives identified an additional 23 organizations that ac-tively support marine resource management and conservation (Co-hen et al., 2012).

Depending on the types of relationships analyzed, SNA canillustrate which stakeholders are particularly important for influ-encing policy or on-ground action, or for facilitating informationand knowledge transfer (Weiss et al., 2012). In the Solomon Islandsstudy, in-degree measures – which express the number of timeseach actor was named by others in the network (Hawe and Ghali,2008) – indicated that certain organizations, that were not ‘‘for-mal’’ network members, were looked to for collaboration andinformation exchange as often as many member organizations.Consequently, if conservation planning processes were initiatedin Solomon Islands, they should engage organizations shown tobe players in supporting marine resource management even if theyare not formally part of SILMMA. In Solomon Islands, measures ofbetweenness – how important an actor is for connecting other ac-tors that would otherwise be disconnected or tenuously connected(Prell, 2011) – confirmed that the organization acting as the net-work’s formal coordinator did indeed hold a strategic position,being able to control or influence the flow of information betweenother actors (e.g. Weiss et al., 2012). Fostering and facilitating sucha role can aid consultation and communication amongst otherstakeholders and the dissemination of information between them.With this insight, planners can ensure actors of high betweennessare involved throughout the planning process. However, relation-ships are not always positive and productive, and actors are not al-ways effective or collaborative; parallel, complementary researchshould always be undertaken to understand the nature of relation-ships within networks. Depending on the motivations and actionsof an organization or person with high betweenness, this strategicposition could be utilized to either facilitate or hinder the actionsof others or the network as a whole (Grootaert and van Bastelaer,2001).

A particularly important network position to facilitate cross-scale interactions, and increase feedback between regional and lo-cal scales, is the ‘‘scale-crossing broker’’ (Ernstson et al., 2010). This

is a network actor that links otherwise disconnected actors operat-ing at different spatial scales or at different physical sites (Ernstsonet al., 2010). In practice, scale-crossing brokers are people or orga-nizations that spend time and resources engaging with both localmanagers and regional planners, and who create and nurture socialarenas to deliberate the (often differing and contested) priorities ofdifferent groups for shared resources (Ernstson et al., 2010). Previ-ous studies have suggested that linking different levels of organiza-tions and knowledge systems can require multiple facilitators andcoordinators, because these scale-crossing brokers often deal withroles beyond brokering knowledge, including resolving conflictsand building trust (Berkes, 2009; Hahn et al., 2006). Notably, bro-kering between scales is often not the nominal role of such people,who therefore have to balance this contribution with otherrequirements of their organizations.

In the Solomon Islands case, partnerships between local com-munities and SILMMA members were central to management ac-tions. Certain SILMMA members acted as scale-crossing brokersby creating pathways to represent local knowledge, interests andactions at national and international levels, whilst retaining theirrelationships to communities and their abilities for localized learn-ing and action (Cohen et al., 2012). In practice, outcomes of coordi-nation and representation were potentially constrained by capacity(e.g. limited finances and time to learn from new information andwork together) and reliance on a partner to indirectly represent lo-cal issues at wider scales (Ratner et al., in press). This again high-lights the importance of coupling SNA with other methods tounderstand the nature and quality of connections and networkoutcomes, alongside the patterns of relationships.

In addition to characteristics describing the role that individualactors play in a social network, SNA can also help to describe theoverall network structure. The structure of a network can influenceits potential to facilitate diverse social processes such as collectiveaction, consensus, or problem resolution (Bodin and Crona, 2009;Ernstson et al., 2010). Importantly, a network structure that en-hances one of these processes can simultaneously inhibit another(Bodin et al., 2006). For instance, engaging actors in the planning

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process can occur more efficiently with centralized networks, inwhich relationships are concentrated through one or few actorswith high betweenness. These central actors are well placed tocommunicate and coordinate with others, providing a forum forthe integration of knowledge and coordination. However, in certaincontexts, finding ways of managing complex ecosystem processesmight require decentralized networks – in which connections aremore evenly dispersed amongst actors and across multiple groups– because each group will contribute with, or engage through, adifferent type of expertise in addressing, contesting and reachingmanagement decisions (Ernstson et al., 2010; Bodin et al., 2006;Leavitt, 1951). Connections can lead to facilitating communicationof best practices and exchange of resources, but also to sharingfalse information or the homogenization of information and prac-tice ideas, thus precluding innovation. Polycentric networks (mul-tilayered with multiple centres) are considered to allow a balancebetween decentralized and centralized control, increasing thediversity of response options by encouraging the exploration of dif-ferent ideas (e.g. Hahn et al., 2006). Redundancy in connectionsconfers such networks with resilience so they can better deal withuncertainty and surprises (Folke et al., 2005), such as the loss of amember of the network. There is a growing research agenda to bet-ter understand what types of structures suit different conservationcontexts and phases, and how such structures change over time(Bodin and Prell, 2011).

Table 1Target percentages for marine coral reef types. All targets had a base target value of10%, but this value would increase depending on the information on threat and rarityof that coral reef type (from Kool et al., 2010).

Coral reef types Objective (% of total extent)

Shallow complexes 12.3Shallow fringing 26.0Shallow lagoons 30.4Variable forereef 15.1Variable lagoons and terraces 22.4Variable subtidal reef flats 15.3Variable terraces 14.0Deep barrier reef 19.8

3. Designing and facilitating strategic networking to strengthenlinkages between local and regional conservation initiatives

Conservation plans can be undertaken across regions of varyingextents. Large planning regions provide perspectives on ‘‘patternsand processes of biodiversity and human uses’’ (Pressey and Bot-trill, 2009), while small regions emphasize local context and thepotential for direct application of actions. A key challenge forextensive regional plans is to manage the cross-scale transitionfrom very broadly defined and usually generic prescriptions forprotection, to local decisions about specific management actions(Briggs, 2001; Groves, 2003; Sarkar et al., 2006). It can also be dif-ficult to communicate local knowledge and contextual informationto broader scales of decision making to influence regional planning(e.g. Berkes, 2006; Guerrero et al., 2013). Consequently, insightsabout existing links between resource management stakeholdersoperating at different scales will improve the ability of conserva-tion practitioners to work with these links and, if needed, improvethem.

Social network analysis can inform strategic changes to net-work structure considered beneficial to conservation projects(hereafter ‘‘network weaving’’). Network weaving involves net-work mapping and analysis followed by facilitation to developnew links between non-communicating stakeholders to increasethe network’s ability to govern dynamic ecosystems (Vance-Borland and Holley, 2011). Importantly, for those researchers orconsultants doing the work, strategic networking involves achange from observing networks to becoming part of them.Ernstson et al. (2010) described social network structure as ‘‘aproduct of localized interactions. . .all actors need to act throughand within the network—there is no ‘outside’ nor any external‘control knobs’ ’’ (Ernstson et al., 2010).

The SILMMA network was explicitly founded to facilitate collab-oration and information exchange between actors operating atdifferent scales, from local to international (Fig. 1; Cohen et al.,2012). Network analysis revealed a weakness in the connectivityof SILMMA members to provincial government agencies, whichare considered to be critical to supporting healthy long-termresource governance (Lane, 2008). In this way, SNA identified a

priority for strategic investments in networking with provincialgovernments, hence highlighting the need to increase provincialcapacity to work with other stakeholders to foster and sustainmanagement. While opportunities to strengthen networks mightbe readily identified, the subsequent processes for implementingchange in agencies and relationships are complex and context-specific.

Investments in strategic networking can aid in coordinating lo-cal conservation actions to better achieve wider conservationobjectives such as ecological connectivity and complementarity(Pressey et al., 2007). Due to the open nature of ecosystems, con-nectivity has been identified as a critical consideration in configur-ing marine protected area networks (e.g. Saunders et al., 1991:Palumbi, 2003). For example, a locally marine managed area(LMMA; an area in which measures such as no-take marine re-serves or restrictions on fishing gears or harvested species are ap-plied) can be established in an ad hoc way or planned to contributeto ecologically functional networks. As networks, LMMAs can bedesigned to enhance connectivity between protected fish popula-tions, thereby promoting the recovery of species after disturbances(Almany et al., 2007). Depending on the topology of a network ofmarine reserves, inadequate management in some LMMAs couldjeopardize the integrity of the whole network (e.g. Game et al.,2008; Bodin and Saura, 2010). Networking of management, withadequate synergies or collaboration between managers, can beimportant. This strategic networking is already occurring in Cebu,Philippines, where a social network of managers of marine pro-tected areas (MPAs) is considered one of the crucial mechanismsfor the formation of an ecologically functional network of MPAs(Lowry et al., 2009; Horigue et al., 2012).

4. Prioritizing conservation actions using measures of socialconnectivity alongside ecological data

A geographic view of a social network can provide another per-spective on the opportunities and constraints for conservation ac-tion. Linking patterns of social relationships to geographic localescan highlight existing relationships that help to operationalizecomplementary conservation actions (e.g. protecting ecosystemsrequired by species at different life stages). For instance, the geo-graphic location of stakeholders’ conservation actions, and theirposition and role in a social network, can reveal conservation pri-ority areas that are: a) within areas where well connected/centralstakeholders operate (e.g. Knight et al, 2010); b) in close proximityto areas where well connected/central stakeholders operate; or c)difficult to manage without stakeholders investing in a new basefrom which they can operate, or without strengthening relation-ships between unconnected stakeholders. This approach assumesthat, from situations (a) to (c), there is a progressive reduction inboth the likelihood of information about best-practice manage-ment being disseminated, and in access to conservation resourcesthrough the social networks. Other things equal (e.g. conservation

Fig. 2. Example of conservation prioritization that combines ecological data (from Kool et al., 2010) with social network data (from Cohen et al., 2012). Inset identifies thelocation of focus within the Solomon Islands. (a) Coral reef types targeted in the prioritization of marine areas for the Solomon Islands. (b) In-degree centrality – the numberof direct links each actor has to others in the network – of each ward in the Solomon Islands. In-degree centrality scores were from Cohen et al. (2012), calculated using Ucinetversion 6.288 (Borgatti et al., 2002) and based on responses by conservation agencies to the question: ‘‘which agencies do you collaborate with when implementing marineresource management and conservation?’’ For each ward, we used the in-degree measure of the most connected NGO operating within that ward, and we extended wardboundaries into the inshore marine areas. In-degree scores indicate the maximum number of SILMMA members identifying any NGO operating in the ward as a collaborativepartner. (c) Differences in the spatial distribution of marine conservation priorities (differences in selection frequency calculated in Marxan) between a prioritization based onbiodiversity features only (scenario 1, equal cost of planning units) and one based on both biodiversity features and in-degree centrality (scenario 2, costs of planning unitsrelated inversely to in-degree centrality of conservation organizations, preferentially selecting areas with well integrated management). Areas in yellow to red have a higherlikelihood of selection when centrality is considered (scenario 2). Blue to purple areas have a higher likelihood of selection when centrality is not considered (scenario 1).Parameterization for Marxan: uniform feature penalty factor (10) and BLM set to 0 (no clumping) for both scenarios. Results for each scenario are from 100 repeat runs, eachwith 1,000,000 iterations.

10 M. Mills et al. / Biological Conservation 169 (2014) 6–13

value, threat, cost, capacity and willingness to participate inconservation), the order of preference for engagement to achieveconservation objectives would begin with the first situation andend with the third.

To demonstrate the potential use of SNA data for prioritizingconservation actions, we used Marxan, a software system for prior-itizing conservation in systematic conservation planning (Ballet al., 2009), to run two priority-setting scenarios; without andwith social network data (scenarios 1 and 2, respectively). Marxanidentifies reserve configurations that achieve conservation objec-tives at minimum or near-minimum cost. Our conservation objec-tives were set as percentage representation of each of eight typesof coral reef (Table 1 and Fig. 2a), and based on Kool et al.(2010). Because we did not include data on species or on socioeco-nomic variables such as threat or opportunity costs that shouldguide conservation priorities in practice (Pressey et al., 2007,Knight et al., 2010; Ban et al., 2013), our scenarios are indicative,and designed solely to illustrate how information from SNA can

influence prioritization of areas for conservation. We assumed thatopportunities to implement a complementary action in part of amarine conservation area network will be higher in areas wherehighly connected actors operate, because they can have a positiveinfluence on the achievement of conservation outcomes. However,in other planning contexts or in practice, this assumption may notbe met where, for example, the cost of changing behaviors/prefer-ences increases as more individuals are connected. This is animportant area for future research.

To run the scenarios, the planning region, defined as SolomonIslands coastal marine waters less than 30 m deep, was subdividedinto 250 ha hexagonal planning units. Each planning unit contrib-uted differentially to conservation objectives, as illustrated by itsselection frequency (i.e. the number of times that each unit was se-lected in each scenario; see Ardron et al., 2008). The cost of conser-vation for scenario 1 was based on the area of the planning unitand was equal across all planning units. The cost layer in scenario2 was informed by the in-degree centrality data, or the number of

Table 2Challenges in incorporating SNA into systematic conservation planning, in relation to the three potential roles of SNA in planning.

Role of SNA Challenge References

1. Identifying stakeholders and their roles in the socialnetwork and characterizing relationships betweenthem

Difficulties in defining the boundaries of social network analyses might meanthat some apparently ‘‘unconnected’’ stakeholders are not considered, eventhough they have potentially important contributions to make. Some of theSNA measures require a complete view of the network (e.g. betweenessmeasure), which can be extremely difficult to obtain

Prell et al. (2009), Scott(2000), Wasserman andFaust (1994)

2. Designing and facilitating strategic networking tostrengthen linkages between local and regionalresource management

Limited understanding of the dynamics of social networks through time.Social networks are constantly changing as connections between actors formor sever, e.g. because an actor changes jobs. We need to understand the speedof changes within social network structures and assess how influentialchanges can be for conservation outcomes. Changes in membership andstructure of networks could have positive or negative effects, althoughrepeated changes could inhibit information flow and establishment ofrelationships. Understanding dynamics could therefore guide investment inmore stable social networks that can be linked to effective conservationinvestments

Borgatti et al. (2009),Ernstson et al. (2010),Janssen et al. (2006)

3. Prioritizing conservation actions using measures ofsocial connectivity alongside ecological data

There are many social characteristics that will determine opportunities andconstraints for conservation. This highlights the difficulty of understandingthe relative importance of characteristics of social networks and the roles ofstakeholders within them

Emirbayer and Goodwin(1994), Ostrom (2009)

The dual effect of structural characteristics of networks superimposed ondiverse social processes – with structure hindering some processes whilesupporting others – makes it difficult to identify what structuralcharacteristics should be used to prioritize actionsIncomplete understanding of network structures could lead to incorrectassessments of which geographical areas have the most connectedstakeholders.

Scott (2000), Wasserman andFaust (1994)

Evaluations of associations between social network structures and indicatorsof ecological condition and management effectiveness are lacking.

Armitage et al. (2009),Plummer and Armitage(2007)

M. Mills et al. / Biological Conservation 169 (2014) 6–13 11

direct links each actor has to others in the network, representingsocial connectivity. In-degree centrality scores, from Cohen et al.(2012), were based on conservation agency responses to the ques-tion: ‘‘which agencies do you collaborate with when implementingmarine resource management and conservation?’’. To assign an in-degree centrality score to each planning unit we first associatedeach planning unit with a ward (a local governance unit of Solo-mon Islands) and used the in-degree measure of the most con-nected NGO operating within that ward. The cost in scenario 2was then the inverse of the in-degree centrality score per ward,scaled to 100, so planning units with higher in-degree scores hadlower costs. By modifying the cost based on SNA data, we aimedto preferentially select areas where highly connected stakeholderswere working with local communities. For this indicative exercise,we assumed that well connected stakeholders can facilitate imple-mentation of conservation actions via their superior capacity to ex-change and benefit from resources, experiences, and lessonslearned with other organizations. In reality, other factors (e.g. will-ingness and capacity) will also influence the participation of stake-holders in collective action.

We found that including social network data changed the focusof some conservation priorities (Fig. 2c). Areas selected more fre-quently when lower costs reflected higher in-degree centrality (yel-low to red in Fig. 2c) contributed to ecological objectives andpresented more potential for coordinated conservation action(Fig. 2b). In contrast, some areas (blue to purple) were selectedmore frequently when costs were uniform (in scenario 1). Theseareas were important in minimizing the number of planning unitsrequired to achieve all objectives but, because of low in-degree cen-trality, were replaced by other areas in scenario 2. The solutionswith uniform cost (scenario 1) required less area to achieve thesame objectives compared to solutions where cost was informedby in-degree centrality (scenario 2). However, the potential advan-tage of scenario 2 is that the larger number of required areas couldbe offset by more effective implementation in many of them. Inves-tigation of areas selected more often in scenario 1 than scenario 2might show that their replacements in scenario 2 should remain

priorities for action. Alternatively, the areas selected more oftenin scenario 1 could be targeted for investment in building relation-ships and increasing in-degree centrality. Among the factors influ-encing this choice would be the importance of each area incontributing to conservation objectives, and the willingness andcapacity of presently unconnected NGOs to implement comple-mentary actions. Investments in building in-degree centrality couldbe directed at improving relationships between managers from dif-ferent areas to aid sharing of management resources and expertise.

Ideally, social connectivity data should also be placed in thecontext of ecological connectivity, because the patterns of connec-tions between social and ecological networks (e.g. through larvaldispersal and spillover, see Almany et al., 2009) can be quite differ-ent (Bodin and Tengö, 2012). Investing in areas that are highly con-nected both ecologically and through social networks could lead topotentially more efficient deployment of resources through moreeffective implementation. In contrast, a mismatch between thesetwo networks could indicate the need to focus on building relation-ships between actors that manage sites with strong ecological con-nections (e.g. through the movement of focal species; Cumminget al., 2010).

5. Challenges of incorporating SNA into systematic conservationplanning

While there are potential contributions of SNA to conservationplanning, some challenges remain in incorporating SNA as a rou-tine part of the planning process (Table 2). For example, networksof conservation actors can be extensive and highly dispersed geo-graphically, so undertaking a comprehensive SNA can require largeamounts of time and money (Scott, 2000; Wasserman and Faust,1994). Alternatively networks can be too localized. For example,Landcare networks in the wheat-sheep belt of New South Walesoperate over areas of up to 150 km by 150 km and cover onlyportions of planning regions based on ecological principles, andunless these networks are connected at a higher level there might

12 M. Mills et al. / Biological Conservation 169 (2014) 6–13

be limited applicability of SNA data to regional planning (Briggs,2001). Furthermore, while the social network analytical methodsaim to capture relations that are longer-term and stable (Schneideret al., 2003; Ernstson, 2011, 2012), there are increasing efforts tobetter understand how network structure and function changeover time in response to endogenous and exogenous drivers(Borgatti et al., 2009; Snijders et al., 2010). Consequently, socialnetworks might require reassessing their structures periodically.This requires additional investment to identify, maintain orpromote desirable linkages among actors, particularly when struc-tural changes of networks could compromise implementation ofconservation actions.

Observing and managing changes in networks might be lessburdensome if ‘self-measuring the network’ becomes part of man-agement and stakeholder procedures. For instance, in bi-annualmeetings, or in a bi-annual self-administered survey, known net-work participants could be asked about their connections andwhere they work, including new and previously unidentifiedgroups and locales. The activity itself could become part of strate-gic networking, or ‘network weaving’, and be an activity that plan-ners and consultants undertake during planning. More studies areneeded to understand which social network structures tend tochange more than others, how different structures influence man-agement of spatially separated conservation areas, and in whichcontexts and planning stages different network structures are mostbeneficial (Crona and Bodin, 2010).

Decision making in conservation is influenced not only by net-work structures, but also by stakeholders’ characteristics, motiva-tions and reasoning processes, and the broader social andinstitutional contexts in which networks function (Ajzen andFishbein, 1980; Chen et al., 2009; Emirbayer and Goodwin, 1994;Ostrom, 1990; Knight et al., 2010; Raymond and Brown, 2011;Bodin and Prell, 2011; Mills et al., in press). The relative impor-tance of network structure in shaping conservation decisionmaking and outcomes, compared with other social, economic andpolitical drivers, is an important area for further research. Workis underway to understand the implications of structural charac-teristics of networks for local conservation actions (e.g. Cronaand Bodin, 2010), but there is limited understanding of the impli-cations for conservation planning or cross-scale conservationactions (Ernstson et al., 2010; Guerrero et al., 2013). Nonetheless,SNA is one of the suite of tools that can provide practical insightsinto the social dimensions of conservation planning (Bodin andPrell, 2011; Ban et al., 2013).

We propose that SNA is likely to be a valuable tool in informingdecision making in conservation planning through: (1) facilitatingthe strategic identification of stakeholders and their patterns ofcollaboration; (2) guiding planners and managers that aim to inter-vene in (and work within) networks to better integrate knowledge,stakeholders and sites across scales; and (3) accounting for socialand social-ecological connectivity in the prioritization of actions.For the moment, however, operationalizing SNA in conservationplanning and management remains a challenge both academicallyand practically.

Acknowledgements

Data collection in Solomon Islands was funded by the Coral ReefInitiatives of the Pacific. Thanks are extended to the SolomonIslands Government for permitting this research and to theSolomon Islands Locally Managed Marine Area Network for partic-ipation. M.M., J.G.A.R. and R.L.P acknowledge the support of theAustralian Research Council. J.G.A.R. acknowledges support fromMexico’s Consejo Nacional de Ciencia y Tecnología (CONACYT)and Secretaría de Educación Pública (SEP). H.E. acknowledgessupport from Swedish research council Formas (grant number

211-2011-1519). P.C. acknowledges support from the AustralianCentre for International Agricultural Research grant (FIS/2012/056) and the CGIAR Research Program on Aquatic AgriculturalSystems.

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