Review Integrating connectivity and climate change into marine conservation planning Rafael A. Magris a,⇑ , Robert L. Pressey a , Rebecca Weeks a , Natalie C. Ban a,b a Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University Townsville, QLD 4811, Australia b School of Environmental Studies, University of Victoria, PO Box 1700 STN CSC, Victoria, BC V8W 3R4, Canada article info Article history: Received 10 August 2013 Received in revised form 2 December 2013 Accepted 21 December 2013 Keywords: Biodiversity conservation Marine reserve design Marine protected area Marine reserve Global warming Dispersal abstract Most applications of systematic conservation planning have not effectively incorporated biological pro- cesses or dynamic threats. We investigated the extent to which connectivity and climate change have been considered in an ecologically meaningful way in marine conservation planning, as an attempt to help formulate conservation objectives for population persistence, over and above representation. Our review of the literature identified 115 marine planning studies that addressed connectivity and 47 that addressed the effects of climate change. Of the statements identified that related to goals and objectives, few were quantitative and justified by ecological evidence for either connectivity (13%) or climate change (8.9%). Most studies addressing connectivity focused on spatial design (e.g. size and spacing) of marine protected areas (MPAs) or clustering of planning units. Climate change recommendations were primarily based on features related to MPA placement (e.g. preferences for areas relatively resilient and resistant to climate change impacts). Quantitative methods to identify spatial or temporal dynamics of features related to connectivity and/or climate change (e.g. functionally well-connected or thermal refugia areas) were rare, and these accounted for the majority of ecologically justified statements. Given these short- comings in the literature, we outline a framework for setting marine conservation planning objectives that describes six key approaches to more effectively integrate connectivity and climate change into con- servation plans, aligning opportunities and minimizing trade-offs between both issues. Ó 2013 Elsevier Ltd. All rights reserved. Contents 1. Introduction ......................................................................................................... 208 2. Methods ............................................................................................................ 209 2.1. Database of conservation planning studies ........................................................................... 209 2.2. Data analysis ................................................................................................... 210 3. Results.............................................................................................................. 210 3.1. Overview of studies.............................................................................................. 210 3.2. Connectivity .................................................................................................... 211 3.3. Climate change ................................................................................................. 213 3.4. Opportunities and trade-offs ...................................................................................... 214 4. Discussion ........................................................................................................... 214 4.1. Qualitative vs. quantitative objectives ............................................................................... 214 4.2. A framework for setting objectives for processes related to connectivity and climate change .................................. 215 4.3. Future prospects ................................................................................................ 218 5. Conclusions .......................................................................................................... 219 Acknowledgements ................................................................................................... 219 Appendix A. Supplementary material ....................................................................................... 219 References .......................................................................................................... 219 0006-3207/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biocon.2013.12.032 ⇑ Corresponding author. Present address: Australian Research Council Centre of Excellence for Coral Reef Studies, Room 108, Building 32 (Sir George Fisher Building), James Cook University, Townsville, QLD 4811, Australia. Tel.: +61 07 4781 6063; fax: +61 07 4781 6722. E-mail address: [email protected](R.A. Magris). Biological Conservation 170 (2014) 207–221 Contents lists available at ScienceDirect Biological Conservation journal homepage: www.elsevier.com/locate/biocon
Integrating connectivity and climate change into marine conservationplanning
0006-3207/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.biocon.2013.12.032
⇑ Corresponding author. Present address: Australian Research Council Centre of Excellence for Coral Reef Studies, Room 108, Building 32 (Sir George Fisher BuildinCook University, Townsville, QLD 4811, Australia. Tel.: +61 07 4781 6063; fax: +61 07 4781 6722.
Rafael A. Magris a,⇑, Robert L. Pressey a, Rebecca Weeks a, Natalie C. Ban a,b
a Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University Townsville, QLD 4811, Australiab School of Environmental Studies, University of Victoria, PO Box 1700 STN CSC, Victoria, BC V8W 3R4, Canada
a r t i c l e i n f o a b s t r a c t
Article history:Received 10 August 2013Received in revised form 2 December 2013Accepted 21 December 2013
Most applications of systematic conservation planning have not effectively incorporated biological pro-cesses or dynamic threats. We investigated the extent to which connectivity and climate change havebeen considered in an ecologically meaningful way in marine conservation planning, as an attempt tohelp formulate conservation objectives for population persistence, over and above representation. Ourreview of the literature identified 115 marine planning studies that addressed connectivity and 47 thataddressed the effects of climate change. Of the statements identified that related to goals and objectives,few were quantitative and justified by ecological evidence for either connectivity (13%) or climate change(8.9%). Most studies addressing connectivity focused on spatial design (e.g. size and spacing) of marineprotected areas (MPAs) or clustering of planning units. Climate change recommendations were primarilybased on features related to MPA placement (e.g. preferences for areas relatively resilient and resistant toclimate change impacts). Quantitative methods to identify spatial or temporal dynamics of featuresrelated to connectivity and/or climate change (e.g. functionally well-connected or thermal refugia areas)were rare, and these accounted for the majority of ecologically justified statements. Given these short-comings in the literature, we outline a framework for setting marine conservation planning objectivesthat describes six key approaches to more effectively integrate connectivity and climate change into con-servation plans, aligning opportunities and minimizing trade-offs between both issues.
Despite a rapid increase in applications of systematic conserva-tion planning (hereafter ‘‘conservation planning’’) over the last twodecades (Bottrill and Pressey, 2012), challenges persist. One chal-lenge is the dependence of successful planning on explicit goals,preferably translated into quantitative, operational objectives(Leslie, 2005; Game et al., 2013; Pressey and Bottrill, 2009). Con-servation planning also needs to move beyond merely representingbiodiversity features to ensuring the persistence and long-termviability of species assemblages (Sarkar et al., 2006), but this aspectof spatial prioritization is not yet well developed (Pressey et al.,2007). Planning for persistence, over and above representation, isinherently more complex and demanding of information. For in-stance, setting objectives for ecological processes can be problem-atic inasmuch as protection of natural processes must be based ontheir spatial surrogates rather than the processes themselves(Rouget et al., 2003), and requires understanding of associatedspatial and temporal dynamics (Ban et al., 2012). Accordingly,relatively few studies have developed explicit objectives forpersistence (but see Airamé et al., 2003; Fernandes et al., 2005;Green et al., 2009). Thus, there is an urgent need to advanceobjective setting in marine conservation to guide conservationefforts, making explicit objectives more defensible and facilitatingtheir refinement over time.
Connectivity – the movement of organisms encompassing dis-persal of propagules and movement of adults – is a key mechanismunderlying the persistence of populations, and hence is importancefor marine protected area (MPA) design in any region. The successof MPA networks and complementary management strategies iscontingent upon the maintenance of ecological connectivity pro-cesses because larval connectivity between MPAs ensures the per-sistence of populations within their boundaries (Berumen et al.,2012), and larval export from MPAs to fished reefs can make a sig-nificant contribution to the replenishment of populations (Bodeet al., 2012; Harrison et al., 2012). In general, areas that are period-ically disturbed require functional connectivity to other areas forimmigration of temporarily extirpated species (Birrell et al.,2008; Hughes et al., 2003; Salm et al., 2006) conferring ecosystemswith resilience (Cowen et al., 2007; Foley et al., 2010; Mumby andHastings, 2008). Although an understanding of connectivity isclearly crucial to effective conservation outcomes, it has beenpoorly incorporated into existing design protocols for MPA net-works (Almany et al., 2009). In the face of major declines in fisherystocks, increasing anthropogenic disturbance of marine ecosys-tems, and calls for ecosystem-based management, it is fundamen-tal to maintain larval or adult exchange and recruitment ofpopulations over demographically relevant time scales.
Climate change is of major interest for conservation because itacts simultaneously as a driver of biodiversity processes and a dy-namic threat (Pressey et al., 2007), adding additional challenges tospatial planning. For example, catastrophic events related to warmanomalies in sea surface temperature can potentially negate thecontribution made by MPAs to protecting a region’s biodiversity(Game et al., 2008b). Projected future climate change willundoubtedly result in even more dramatic shifts in the distribu-tions of species and composition of many marine ecosystems, bothdirectly and indirectly (Lawler, 2009). Protective management oflarge, functioning ecosystems cannot directly address such exter-nal influences on marine environments. Climate change has typi-cally been addressed in marine planning through genericstrategies or design principles with the aim of minimizing threatsto ecosystems, including requiring higher representation and rep-lication of features, and spacing protected areas to spread riskand represent differences in composition or genetics (Fernandes
et al., 2005; Lawler, 2009; McLeod et al., 2009; Salm et al., 2006).On the whole, however, few approaches to MPA planning havebeen based on knowledge of the directional or stochastic changesresulting from climate change and their effects on species and eco-systems. This limitation underlines the importance of new ap-proaches to designing MPA networks that will help clarifymanagement requirements for avoiding or mitigating climatechange impacts or promoting recovery after disturbance.
Connectivity and climate change also interact. Climate-relateddisturbances not only disrupt larval dispersal pathways by reduc-ing larval export from affected areas and changing hydrodynamics,but might also cause a shift in spawning phenology (earlier spawn-ing of adults), larval transport (shorter pelagic larval durations),larval mortality (reduced exposure to lethal temperatures andshorter larval life), and behavior (increased larval swimmingspeed) (Cowen and Sponaugle, 2009; Lett et al., 2010; O’Connoret al., 2007). The spatial scales of population connectivity mightbe reduced in the future due to these diverse effects on habitatfragmentation (Munday et al., 2009). Simultaneously, connectivitycan influence post-disturbance recovery and the ability of organ-isms to adapt to rapid climate change (Munday et al., 2008). Al-tered species distributions might also limit or expand theconnectivity of sites in the future. Conservation planners shouldthus consider all possible interactions between connectivity andclimate change that might act on species occurrences and abun-dances and influence the future efficacy of MPAs.
Despite recent literature emphasizing the need to incorporateconnectivity (Almany et al., 2009; Foley et al., 2010; Fox et al.,2012; Pressey et al., 2007; Roberts et al., 2003) and climatechange effects (Game et al., 2008b; Heller and Zavaleta, 2009;McLeod et al., 2009; West and Salm, 2003) into the design ofMPA networks, little work has been done to critically examinetheir integration into conservation planning. Here we review ap-proaches to incorporating connectivity and climate change intomarine conservation planning to evaluate the extent to whichecologically informed strategies have been recommended or ap-plied. We also explore what approaches have been recommendedor applied to combine connectivity and climate change consider-ations, revealing integrative approaches and potential trade-offs.Additionally, we identify the main shortcomings of goals andobjectives related to connectivity and climate change in marineconservation planning and suggest how these might be overcomein future applications.
Our review adds to the body of knowledge on marine planningfor dynamic processes in having four key characteristics: (i) com-prehensive – previous efforts have focused on particular aspectsof protected area configuration such as size and spacing; (ii) syn-thetic – studies to date are scattered in published studies and greyliterature (e.g. reports by nongovernmental agencies), so their find-ings are not readily available and not collated to identify patterns,trends and gaps; (iii) addressing tradeoffs between sets of objec-tives – tradeoffs between objectives for connectivity and climatechange and opportunities for aligning them have not been ade-quately addressed in previous work; and (iv) marine focused – gi-ven the pronounced differences in dispersal patterns for marine vs.terrestrial species and the high sensitivity of marine ecosystems tolarge-scale environmental change, exploring marine-based ap-proaches is of particular relevance. More specifically, given that ex-plicit conservation objectives are critical in shaping the subsequentstages in the conservation planning process, and that this phase issubject to frequent mistakes made by planners (Game et al., 2013;Pressey and Bottril, 2008; Pressey and Bottrill, 2009), a review ofmarine conservation planning in relation to connectivity and cli-mate change increases the accessibility of evidence to supportmore effective frameworks for decision making.
Fig. 1. (A) Flowchart depicting major steps in extracting information on studies considering connectivity and climate change in marine conservation planning. (B) Examplesof qualitative and each type of quantitative statement. Keywords and search criteria were used to select 134 studies that addressed or recommended marine conservationapplications of connectivity and climate change. Statements referring to both connectivity and climate change were extracted to a database. Categories and criteria fordesigning marine protected areas were assigned to each statement to understand the range of approaches identified. In addition, statements were classified to reflect how thecriteria were applied. Qualitative statements (QL) refer to statements of preferences. Quantitative statements (involving numerical values) were grouped into three classes:no rationale (QN), subjective (QS), or justified ecologically (QE).
We searched on the ISI Web of Knowledge (www.isiknowl-edge.com) and Google to identify peer-reviewed papers publishedin ecological journals, book chapters, and grey literature reports,from any year. Our literature search used the terms ‘connectivity’,‘climate change’, ’global warming’, ‘marine conservation’, ‘marinespatial prioritization’, ‘marine conservation prioritization’, ‘marinereserve selection’, ‘marine conservation planning’, ‘marine
protected area’, ‘marine reserve’ and ‘decision support tool’. Fur-ther studies were identified from references cited in these docu-ments. The information for our analyses was extracted andorganized in a database.
We initially scanned studies and selected for further analysis(Fig. 1) only those with a primary aim of: (i) proposing and design-ing an MPA network; (ii) evaluating how well existing MPAs werebeing managed, including zoning and rezoning; (iii) assessing a re-gion’s biodiversity or identifying areas of biological significanceacross a region; (iv) proposing broad guidelines for incorporatingconnectivity and/or climate change into marine conservation
assessments, whether by proposing theoretical frameworks oroffering reviews; (v) prioritizing areas for purposes other than bio-diversity conservation, such as fisheries management, or based onquantitative assessment of threats; or (vi) addressing disturbancefrom climate change or climate change impacts combined withthose of other threatening processes.
Applying these filters, we identified 134 studies (see Appendix/Supplementary material). Of these, 115 referenced connectivity, 47referenced climate change, and 28 considered both connectivityand climate change. Each study was placed into one of threethemes according to their scope (Fig. 1): ‘‘guideline’’ – studies pro-viding generic recommendations for conservation planning; ‘‘theo-retical’’ – studies conceived as an academic exercise or explorationwith no intention to inform practical applications; and ‘‘applied’’ –studies undertaken with the purpose of influencing real-worldconservation planning applications by involving stakeholders, sup-porting government planning processes, or informing a specificpolicy commitment.
2.2. Data analysis
We characterized approaches to considering climate change orconnectivity following the steps shown in Fig. 1. Statements(declarative sentences about methods of dealing with either con-nectivity or climate change) from each study were extracted andrecorded verbatim in the database, before being coded as below.Altogether, we extracted 318 statements related to connectivityand 190 statements for climate change. Although studies conveyedinformation in different ways, most statements about conservationplanning for connectivity and/or climate change could be assignedto five categories (see Table 1 for definitions): (i) design (spatialconfiguration); (ii) location relative to features of interest; (iii) rep-resentation of features such as species or ecosystems; (iv) analysisof dynamics; and (vi) adaptive management. The first three catego-ries related to placement and spatial arrangement of MPAs. Thelast two categories referred to techniques for identifying spatialpriorities for conservation management. Statements that did notfall into these categories were omitted (these were few, and extre-mely vague).
To explore the full range of approaches more specifically, weattributed criteria within each category to every statement. Forexample, statements in the ‘design’ category could be assigned tocriteria such as ‘size of reserves’ or ‘clustering of planning units’.This allowed us to more accurately identify similarities betweenapproaches. As information from each study was recorded, the
Table 1Definition of categories of criteria for connectivity and climate change in marine conserva
Category Definition
Design Spatial configuration of protected areas, planning units, or coecosystems)
Location Placement relative to features that are desirable or undesirabprotected areas
Representation Sampling of each biodiversity feature of interest in a networkbased on an objective for a minimum amount or frequency o
Analysis of dynamics Quantitative methods to characterize the spatial and/or tempvariables or features of conservation interest
Adaptive management Explicit statements about uncertainty and/or recommending
criteria were re-examined to ensure consistency of definition andinterpretation in the database. We then summarized the frequencywith which criteria were recommended or applied across studies.
We further classified each statement as either qualitative orquantitative (Fig. 1). Qualitative statements were those that ad-dressed climate change or connectivity by applying general princi-ples or setting goals without quantitative specification, ofteninvolving statements of preferences (see Fig. 1 for examples).Quantitative statements involved numerical values when inter-preting a principle or estimating requirements for conservationmanagement.
Quantitative statements were further classified (Fig. 1) on thebasis of their stated rationale as ‘‘no rationale’’, ‘‘subjective’’ or‘‘ecologically justified’’. Those with no rationale lacked any explicitjustification. Subjective quantifications were based on the opinionsof experts, stakeholders, or the authors, or on previous work ormodels, but without explicit ecological justification. Ecologicallyjustified quantifications drew on empirical data, ecological theo-ries, or models employed with supporting ecological information.Where statements were substantiated by a literature citation, thebasis of the information provided and the context of the original ci-ted study(ies) were investigated to classify statements as eithersubjective or justified ecologically.
3. Results
3.1. Overview of studies
Studies related to connectivity outnumbered those for climatechange, and most studies used more than one explicit statementto address one or both issues, regardless of their scope (i.e., guide-lines, theoretical or applied studies). Statements about incorporat-ing climate change into conservation planning werepredominantly (>78%) qualitative, proposing general principles orrecommendations. The few quantitative statements were split be-tween those that were subjective or ecologically justified (12.1%and 8.9% of statements, respectively). In contrast, connectivityhas been substantially addressed quantitatively (>45% of state-ments), although rarely with an ecological justification (13%).Overall, there appears to be little ecological information on whichto base guidelines for marine conservation planning to addressconnectivity or, especially, climate change. Most ecologically justi-fied statements about connectivity were based on literature re-views (>57%). Conversely, the few statements about climate
tion planning.
Examples of application
nservation features (e.g. species, Number, size, spacing, and directionalalignment of protected areasReplication and adjacency of featuresShape and clustering of planning units
le to include in networks of marine Spawning or aggregation sitesPoor water quality habitatsRefugia from warm anomalies in seasurface temperature
of marine protected areas, usuallyf occurrence
Represent a minimum amount of eachbioregion in no-take areasAt least 20% of each habitat type
oral dynamics of climate-related Predicted climatic regimeInterpreted historical climate variabilitySpecies’ range shifts
revision after evaluation Testing new approaches in response toexisting and future planning activities
Fig. 2. Integration of connectivity and climate change into marine conservation planning over time. Graphs indicate the numbers of studies in each scope category andnumbers of statements of each type, by publication year for connectivity (A and C, respectively) and climate change (B and D, respectively). QL refers to qualitativestatements; QN, quantitative with no rationale; QS, quantitative, subjective; QE, quantitative, justified ecologically. Study scopes are guideline (e.g. reviews), theoretical (e.g.novel approaches or advances), or applied (e.g. supporting government or NGO commitments).
change classified as ecologically justified were mostly supportedby calculations and/or models (>80%).
Applied studies mostly presented qualitative statements forboth connectivity (58%) and climate change (79%). Only a minorityof statements were quantitative and ecologically justified (6.4% forconnectivity and 3.4% for climate change). In contrast, quantitativestatements that were ecologically justified received the mostattention amongst theoretical studies for both connectivity (60%)and climate change (>80%). The distribution between studies ineach of the three scope categories varied widely through time(Fig. 2A and B) with no clear trends. Quantitative, ecologically jus-tified statements increased from about 2008 (Fig. 2C and D), withno apparent trends in the other types. Detailed information aboutthe distribution of statements across their types, categories, andscope of studies is reported in Appendix/Supplementary material.
3.2. Connectivity
Most case studies addressing connectivity focused on aspects ofMPA network design, including (Table 2): clustering of planningunits (72 statements), size of MPAs (26 statements), and spacingof protected areas (22 statements). Clustering of planning unitswas typically addressed through the boundary length modifier(BLM) in Marxan (a commonly used decision support tool for con-servation planning). The use of BLM highlights the difference be-tween quantitative, ecologically informed requirements for
connectivity and, as typically applied in Marxan, a software param-eter not necessarily related to actual connectivity requirements. Insubjective quantitative statements, BLM values were often chosento optimize the tradeoff between overall network cost and frag-mentation of planning units (e.g. Ban et al., 2009; Giakoumiet al., 2011; Grantham et al., 2011). Other stated reasons for BLMvalues included: wide distribution of MPAs (Game et al., 2010; Salaet al., 2002); a range of MPA sizes (Ban, 2009); comparable size andspacing to a previous proposal (Klein et al., 2008); and a predefinednumber of MPAs (Hansen et al., 2011). In quantitative statementswith no rationale, BLM values had no explicit justification (e.g.Allnutt et al., 2012; Geselbracht et al., 2009; Hinchley et al.,2007), or BLM was used with no information on the specific value(e.g. Ban et al., 2008).
All types of statements considered the size of MPAs (Table 3).Qualitative approaches to MPA size included statements relatedto ‘bigger is better’ or ‘reserves within systems should vary in size’(e.g. Almany et al., 2009; Fox et al., 2012; McCook et al., 2009).Although large MPAs were typically favored, there were also in-stances where smaller MPAs were preferred. For example, Robertset al. (2003) suggested that smaller MPAs spaced more widelycould provide greater connectivity for long-distance dispersers. Asingle quantitative statement with no rationale argued that MPAsshould ideally be at least 4–6 km in diameter, while acknowledg-ing that smaller MPAs have also been effective (Fox et al., 2012).Quantitative subjective recommendations based on authors’
Table 2Summary of criteria used to address connectivity (115 studies) and climate change (47 studies) in marine conservation planning. Criteria are listed alphabetically withincategories. Further details about how all criteria were attributed for each statement are given in Tables A1 and A2. Symbols in the interaction column indicate types of overlapbetween criteria for connectivity and climate change. Convergent (U) interactions indicate that the criterion has been adopted in a complementary way for both connectivity andclimate change. Undefined (?) interactions indicate that the application of the criterion could involve tradeoffs between achieving objectives for connectivity and climate change.Rows with no entries for interactions indicate that the criterion has been adopted exclusively for either connectivity or climate change.
Category Criterion Frequency of statements for connectivity Frequency of statements for climate change Interaction
Design Adjacency of features 5 1 U
Buffer around features 1 2 U
Clustering of planning units 72Directional alignment of protected areas 1Juxtaposition of sources and destinations 6Number of protected areas 19 7 U
Proximity of features 12 3 U
Replication of features 9 23 U
Shape of planning units 16Shape of protected areas 6 2 U
Size of protected areas 26 15 ?Spacing of protected areas 22 9 ?Stratification of study area 5 3 U
Location Anthropogenic stresses 4 12 U
Climatic refugia 1 8 U
Ecological functioning 61 28 U
Oceanographic features 10 10 U
Resilient/resistant ecosystems 18Topographic features 3 9 U
Representation 13 11 U
Analysis of dynamics Biophysical dispersal modeling 7Predicted climatic regime 9Interpreted historical climate variability 13Metapopulation modeling 7Species’ range shifts 3
opinions and expert judgments about size were provided by threestudies (Fernandes et al., 2005, 2012; Lowry et al., 2009). Accordingto Fernandes et al. (2005), for example, no-take areas should be atleast 20 km long on the smallest dimension to ensure maintenanceof populations. Quantitative recommendations for MPA sizejustified ecologically were based on previous literature. Some ofthese statements were precise (e.g. an optimum area of 10 km2,Mora et al., 2006, sizes between 10 and 100 km2, Weeks et al.,2010) while others were less so, such as MPAs ‘‘that are severalto tens of kilometers in alongshore length should be suitable tocontain adult movement for much of the diversity of nearshorespecies’’ (Gaines et al., 2010).
Spacing was addressed in similar ways to size (Table 3). Exam-ples of qualitative statements are that MPAs should be close en-ough to reduce fragmentation and allow connectivity (Hinchleyet al., 2007; Roberts et al., 2003). Quantitative subjective state-ments included: spacing less than 15 km (Agostini et al., 2012),no more than 20 km between MPAs (Fernandes et al., 2012), andspacing between 70 and 100 km (Fernandes et al., 2005). Ecologi-cally informed statements with supporting references were pro-vided by Almany et al. (2009), Fox et al. (2012), Greenet al. (2009), McCook et al. (2009), Mora et al. (2006), Saarmanet al. (2013), Sala et al. (2002), Weeks et al. (2010), and Wilsonet al. (2011). Although many studies focused on coral reefs,recommended spacing of MPAs to protect those habitats variedsubstantially from approximately 1–5 km to 100 km.
Many studies also related connectivity to the locations of spe-cific features in the seascape (Table 2). At least five types of biodi-versity surrogates were used in 71 studies to identify preferred orunfavoured locations linked to connectivity. Most locations wererelated to functional aspects of biological processes such as spawn-ing or aggregation sites, larval source habitats, or physical or struc-tural aspects of environments such as circulation patterns ortopographic features such as marine channels. These studies
mostly used qualitative statements (Table 3) to highlight theimportance of critical sites that accumulate larvae or function asmigratory pathways (e.g. Foley et al., 2010; Gaines et al., 2010;Saarman et al., 2013). Some approaches regarding spawning sitesinvolved subjective quantitative objectives (Geselbracht et al.,2009; Hinchley et al., 2007; Richardson et al., 2006). The identifica-tion and mapping of these functional areas was mostly donethrough expert workshops. Inclusion of source habitats in MPAswas always stated qualitatively, except for Sala et al. (2002) whomade the sole quantitative statement, supported by ecologicaldata, to protect source areas for recruitment and replenishment.The data for this study were mostly derived from interviews withfishermen, but the authors undertook diving surveys for validation(see Sala et al., 2003 for details).
It was apparent from our review of the 115 studies on connec-tivity that there were three seldom mentioned yet relevantcriteria in which connectivity has been accounted for throughecologically justified statements. These criteria could accommo-date a variety of specific biodiversity data that help formulatebiodiversity objectives. First, proximity of features was addressedthrough quantitative statements intended to juxtapose areas withspecific shared or complementary characteristics. For example,Edwards et al. (2009) used a cost function to select planning unitsrepresenting reef and/or seagrass habitats within 10 km ofsignificant mangrove forests. A second criterion – the juxtaposi-tion of sources and destinations – was used to reflect theactual larval dispersal among sites, whether adjacent or distant.Beger et al. (2010) used a modified version of Marxan’s BLM(CSM – connectivity strength modifier) that applies a penalty ifa source site in a connected pair is in the MPA network but thedestination site is not. CSM has the potential to add a functionaldimension to BLM’s structural connectivity, yet still requires asubjective decision when setting the CSM value. Planners alsoneed to weight the importance of connectivity relative to other
Table 3Types of statements for connectivity (unfilled squares) and climate change (filled squares) according to how each criterion has been applied in the reviewed literature. Criteria arelisted alphabetically within categories. Further details about how all criteria were attributed for each statement are given in Tables A1 and A2. Examples of references areprovided. QL refers to qualitative statements; QN, QS, and QE indicate quantitative statements with no rationale, defined subjectively, and justified ecologically, respectively.
Category Criterion QL QN QS QE Examples of studies for connectivity (c) and climate change (cc)
Design Adjacency of features h (c) Agostini et al., 2012; Fernandes et al., 2005j (cc) McLeod et al., 2009
Buffer around features h (c) Green et al., 2009j (cc) McLeod et al., 2009; Salm et al., 2006
Clustering of planning units h h h (c) Ban et al., 2009; Malcolm et al., 2012; Sala et al., 2002Directional alignment of protectedareas
h (c) Ferdana, 2002
Juxtaposition of sources anddestinations
h h h (c) Ardron et al., 2002; Beger et al., 2010; Mumby et al., 2011
Number of protected areas h (c) Green et al., 2011; Hooker et al., 2011; Lombard et al., 2007j (cc) Green et al., 2011; Fernandes et al., 2012; Stewart et al., 2003
Proximity of features h h (c) Edwards et al., 2009; Lowry et al., 2009; Olds et al., 2012j (cc) Grimsditch and Salm, 2006; McLeod et al., 2009
Replication of features h h (c) Gaines et al., 2010; Green et al., 2009; McCook et al., 2009j j (cc) Airamé et al., 2003; Gaines et al., 2010; Roberts et al., 2003
Shape of planning units h (c) Green et al., 2009 Geselbracht et al., 2009; Green et al., 2009; Wilson et al.,2011
Shape of protected areas h h (c) Agostini et al., 2012; Fernandes et al., 2012; Stewart et al., 2003j (cc) McLeod et al., 2009
Size of protected areas h h h h (c) Almany et al., 2009; Mora et al., 2006; Weeks et al., 2010j j j (cc) Allison et al., 2003; Fernandes et al., 2012; Soto, 2002
Spacing of protected areas h h h (c) Almany et al., 2009; Roberts et al., 2003; Weeks et al., 2010j j j (cc) Ardron et al., 2002; Fernandes et al., 2012; Roberts et al., 2003
Stratification of study area h h (c) Airamé et al., 2003; Sala et al., 2002; Smith et al., 2009j j (cc) Airamé et al., 2003; Green et al., 2009; Ferdana, 2002
Location Anthropogenic stresses h (c) Agostini et al., 2012; Fernandes et al., 2005; McCook et al., 2009j (cc) Grimsditch and Salm, 2006; Hansen et al., 2009; Wilson et al., 2011
Climatic refugia h (c) McCook et al., 2009j j (cc) Ban et al., 2012; Game et al., 2011; Hansen et al., 2009
Ecological functioning h h h (c)Foley et al., 2010; Hooker et al., 2011; Lowry et al., 2009j (cc) Agostini et al., 2012; Grimsditch and Salm, 2006; Hansen et al., 2009
Oceanographic features h (c)Foley et al., 2010; Roberts et al., 2003; Wilson et al., 2011j (cc) Lombard et al., 2007; Salm et al., 2006; Wilson et al., 2011
Resilient/resistant ecosystems j (cc) Green et al., 2011; Hinchley et al., 2007; West and Salm, 2003Topographic features h (c) Ardron et al., 2002; Hinchley et al., 2007; Wilson et al., 2011
j (cc) Agostini et al., 2012; Fernandes et al., 2012; Salm et al., 2006
Representation h h h (c) Airamé et al., 2003; Fernandes et al., 2012; Fox et al., 2012j j (cc) Green et al., 2009; McCook et al., 2009; McLeod et al., 2009
Analysis of dynamics Biophysical dispersal modeling h (c) Beger et al., 2010; Berglund et al., 2012; Treml et al., 2008Predicted climatic regime j j (cc) Ferdana et al., 2010; Game et al., 2008b; Salm et al., 2006Interpreted historical climatevariability
j j j (cc) Allnutt et al., 2012; McLeod et al., 2010; Mumby et al., 2011
Metapopulation modeling h (c) Bode et al., 2012; Kininmonth et al., 2011; Watson et al., 2011Species’ range shifts j (cc) Lombard et al., 2007; McLeod et al., 2009; Soto, 2002
Adaptivemanagement
h (c) Fernandes et al., 2012; McCook et al., 2009; Saarman et al., 2013j (cc) Hansen et al., 2009; McCook et al., 2009
objectives (e.g. biodiversity representation) – another subjectiverequirement.
Third, with a set of criteria categorized under analysis ofdynamics, ten studies produced quantitative, ecologically justifiedstatements to reflect functional aspects of connectivity and charac-terize their spatial and temporal dynamics (e.g. Bode et al., 2012;Treml et al., 2008; Watson et al., 2011). More specifically, thesestudies analyzed dynamics through metapopulation modeling, bio-physical dispersal modeling, or a combination thereof. Forexample, Treml et al. (2008) identified stepping-stone sites criticalto local/regional connectivity based on an Eulerian advection–dif-fusion model of coral dispersal across the Tropical Pacific. A dy-namic and spatially realistic model for several nearshore specieswas developed by Watson et al. (2011) using data on populationdynamics and Lagrangian probability density functions. Whilstthese studies were theoretical explorations that identified areasof biological significance without specifically targeting areas forconservation management, their insights into connectivity indicatestrong contributions to understanding requirements forpersistence.
Integration between analysis of connectivity and optimal designof network was promoted by a novel method proposed in Jacobiand Jonsson (2011). Applying the criterion of prioritizing the sitesthat act as both donors and recipients of larvae, they identified apotential network of MPAs that maximized metapopulation sizebased on eigenvalue perturbation theory. Since application of con-servation planning occurs in a dynamic context in which plansmust be formulated with a wide variety of criteria, a remainingchallenge is to combine this framework with a larger set of conser-vation objectives.
3.3. Climate change
Studies considering climate change were more equally dividedbetween criteria concerned with design (65 statements) and loca-tion (86 statements) than was the case for connectivity. Of the de-sign criteria, replication was the most frequently used and wastypically stated qualitatively (Tables 2 and 3). Quantitative subjec-tive statements (e.g. protect at least three examples of each conser-vation feature (Ardron et al., 2002; Fernandes et al., 2005; Green
et al., 2009; McLeod et al., 2009; Wilson et al., 2011) were based onexpert opinion, authors’ opinions, and precedents in previous plan-ning exercises, but there was a lack of ecological justification forreplication.
MPA size was addressed in contrasting ways for climate change.On one hand, some qualitative statements clearly proposed theimportance of large MPAs in the face of ongoing global warming(Keller et al., 2009; McLeod et al., 2009; Soto, 2002). Conversely,other authors argued that separate, small reserves offer greaterinsurance against disturbance from climate change (Ardron et al.,2002; Grimsditch and Salm, 2006). In a single quantitative subjec-tive statement, Fernandes et al. (2012) recommended that MPAsshould have a minimum size of 40 ha (0.4 km2). Quantitative, eco-logically justified statements advocated insurance factors forincreasing the area under protection based on intensity and fre-quency of severe disturbances, including or analogous to warmanomalies of sea surface temperature (Airamé et al., 2003; Allisonet al., 2003). For MPA spacing, at least two qualitative statementssuggested that protected areas should be concentrated to reducethe between-MPA distance (McLeod et al., 2009; Soto, 2002) whilemost qualitative statements recommended more widely separatedMPAs (Almany et al., 2009; Ardron et al., 2002; Roberts et al., 2003;Salm et al., 2006). McLeod et al. (2009) made the sole ecologicallyjustified statement on spacing, recommending a maximum dis-tance between MPAs of 15 km, on the basis that favouring connec-tivity will facilitate recovery after climate-related disturbance.
MPA location for climate change was addressed in diverse ways,almost all qualitative (Table 3). We identified 6 classes of featuresthat were explicitly related to placement of MPAs taking climatechange effects into account. These included: important areas forecological functioning (e.g. source habitats, stepping-stone sites,spawning and feeding areas), sites that are relatively resilientand resistant to climate change impacts, specific oceanographicfeatures (e.g. fronts, areas with high turbidity), and topographicfeatures (e.g. low-lying coastal plains and shaded areas). Prefer-ence for resistant or resilient sites was relatively common, basedon the view that undisturbed areas are more desirable for conser-vation management (e.g. Green et al., 2009; Salm et al., 2006).However, Côté and Darling (2010) speculated that altered commu-nities might be more resilient to climate-related disturbance.
Another frequent consideration for location in relation to cli-mate change was the avoidance of areas already under anthropo-genic stress. Many studies recommended reducing or removingnon-climatic threats as a way of buffering against the additionalimpacts of climate change (e.g. Grimsditch and Salm, 2006; Hansenet al., 2009; Wilson et al., 2011). Although refugia from climate-re-lated disturbance have been mentioned primarily in qualitativestatements (e.g. Game et al., 2011; Hansen et al., 2009; Wilsonet al., 2011), two studies (Ban et al., 2012; Levy and Ban, 2012) pro-posed subjective quantitative objectives related to refugia withinno-take zones.
Analysis of dynamics was often qualitative (23 statements), butalso had the most ecologically justified quantitative statementswhich involved the spatial and temporal distribution of areas un-der minimal thermal stress. These statements related to historicand/or future climatic regimes associated with bleaching risk forcoral reefs through time-series analysis and future projections ofsea surface temperatures (e.g. Allnutt et al., 2012; Game et al.,2010; Mumby et al., 2011). Four aspects of these studies emergedas important considerations for the persistence of species. First,some of the studies that integrated dynamic analyses of anoma-lously high sea-surface temperatures and MPA design did notexplicitly mention conservation objectives to achieve persistence(e.g. Allnutt et al., 2012). Second, authors defined temperatureanomalies or assessed thermal stress predominately through spa-tial and temporal thresholds. A thermal stress index – degree heat-
ing weeks (DHW) – emerged as the most influential predictor ofcoral bleaching, combining both intensity and duration of warmanomalies and related to ecological thresholds (e.g. McLeod et al.,2010). Third, the problem of using historic climatology as a basisfor predicting probabilities of future bleaching involves the un-tested assumption of spatial congruence between current and fu-ture stress (e.g. Allnutt et al., 2012; Mumby et al., 2011). Lastly,although there are many pathways of disturbance caused by globalwarming and a variety of conditions can induce coral bleaching,few studies considered sea level rise, irradiance, and ocean acidifi-cation (but see Ferdana et al., 2010; Halpern et al., 2009; Runtinget al., 2013) or investigated other variables related to thermalstress (but see Maina et al., 2008).
3.4. Opportunities and trade-offs
We sought to identify whether approaches to incorporatingconnectivity and climate change in conservation planning werecomplementary, or required trade-offs between these two sets ofobjectives. We found that most criteria can be applied consistentlyto design MPA systems for both connectivity and climate change.For example, features targeted to protect against climatic distur-bances (e.g. source habitats, spawning sites, upwelling areas, andoceanic fronts) were also cited as promoting connectivity amongstpopulations. In addition, adaptive management (whereby manage-ment strategies are progressively changed or adjusted in responseto new information) is relevant to evaluating errors and uncertain-ties and improving objectives for both sets of considerations.Mumby et al. (2011) provided the sole study that explicitly ad-dressed the interaction between connectivity and climate changeobjectives. By examining spatial patterns of thermal stress and pre-dictions of larval connectivity, they evaluated whether adequatelarval dispersal occurred among populations of corals in contrast-ing thermal regimes, which were defined according to differentmeasures of acute and chronic stress.
Nevertheless, two design criteria can potentially create trade-offs for those seeking to address connectivity and climate changesimultaneously (Table 2): MPA spacing and size. The studies thatwe reviewed made conflicting recommendations for optimal spa-tial configurations of MPAs. For example, while most studies rec-ommended widely spaced MPAs to ensure against negativeimpacts from climate change, shorter distances between MPAswere recommended to promote connectivity. This implies loss ofconnectivity with better insurance against climate change, and viceversa. Divergent recommendations could also result from limita-tions inherent to meta-analyses, with problems arising from spe-cies selection, publication bias, and the collection of data acrossdifferent biogeographic regions.
Recommendations for MPA size varied widely, from ‘bigger isbetter’, to preferences for networks of smaller MPAs, to the bet-hedging ‘reserves within systems should vary in size’. Based onour review, the required minimum MPA size to achieve climatechange objectives (n = 2: 0.4; 0.8 km2) appears much smaller thanthat recommended to fully achieve connectivity objectives(n = 11, mean = 60 km2, median = 17 km2). Consequently, MPA net-works designed to achieve climate change objectives might notalso attain connectivity objectives.
4. Discussion
4.1. Qualitative vs. quantitative objectives
Our review demonstrates that, although connectivity andclimate change have frequently been considered in marineconservation planning, these considerations have typicallybeen incorporated through qualitative statements that are not
translated into measurable objectives; furthermore, where quanti-tative objectives have been stated, these have rarely been justifiedecologically. Of all the statements related to connectivity and cli-mate change, only 13% and 8.9%, respectively, were quantitativeand ecologically justified. Further, less than 25% and 15% of thefew statements based on ecological evidence were derived fromapplied studies incorporating connectivity and climate change,respectively. Nevertheless, theoretical studies that use ecologicallyinformed statements to formulate conservation objectives for con-nectivity and climate change are emerging, which could indicate agrowing awareness of the importance of interpreting both pro-cesses in marine planning.
Quantitative objectives in conservation planning provide a clearpurpose for conservation decisions, impart accountability anddefensibility, help to translate spatially explicit data into decisions,interpret and operationalize broad conservation goals so that theresulting conservation priorities can be scrutinized and, if neces-sary, contested by interested parties (Game et al., 2013; Leslie,2005; Pressey and Cowling, 2001; Pressey et al., 2003; Tear et al.,2005). When based on ecological evidence, they also produce themost scientifically robust conservation plans, enabling assessmentof achievements with respect to ecologically meaningful goals, andpromote scrutiny for refinement when more information becomesavailable (Pressey et al., 2013). Sutherland et al. (2004) also con-tend that a shift towards evidence-based conservation is likely toresult in enhanced funding, through an improved ability to demon-strate effectiveness to donors and policy-makers. Although formu-lation of objectives supported by ecological evidence can requireanalytical methods surrounded by assumptions and caveats (e.g.see Mumby et al., 2011), optimization for conservation has somehistory of dealing with uncertainty (Pressey et al., 2007), whichcan be reduced by deliberately including learning in an adaptiveplanning process (Grantham et al., 2009). Progressively tailoringmodels to the level of knowledge supported by primary biologicaldata and formulating conservation objectives supported by ecolog-ical evidence help highlight knowledge gaps, thereby motivatingcritical thinking on conservation requirements.
4.2. A framework for setting objectives for processes related toconnectivity and climate change
Although a substantial set of theoretical and operational conser-vation planning guidelines have been developed to direct practitio-ners (e.g. Álvarez-Romero et al., 2011; Groves et al., 2002;Margules and Pressey, 2000; Knight et al., 2006; Lehtomäki andMoilanen, 2013), our review indicates that there remains a criticalneed to better understand approaches to setting objectives for con-nectivity and climate change, enabling decision makers to proac-tively design and deliver better strategies. We present a genericframework with this intent, encompassing a broad spectrum ofpossible approaches, which require varying amounts of ecologicalinformation, offer varying levels of ecological relevance (Fig. 3),and have different relative advantages and drawbacks (Fig. 4).Although this framework will need to be adapted to particular con-texts, it provides a starting point for planning for persistence inwhich the effectiveness of objectives in promoting underlyinggoals can be reviewed adaptively as new techniques, datasets,and knowledge are acquired and as experience with conservationplanning grows. Our framework is also relevant to planning forother ecological processes that support the persistence of biodiver-sity (e.g. local extinctions and recolonizations, migration, patchdynamics) and ensure against dynamic threats and catastrophesother than those related to climate change (e.g. urbanization, oilspill, land-based runoff) (see Pressey et al., 2007).
Conservation planning is driven by strategic goals, so initialqualitative statements (Pressey and Bottrill, 2009) should be used
to shape and conceptualize more specific conservation objectives(Fig. 3). Defining goals related to persistence is a critical first stepin moving from planning for representation to explicitly consider-ing persistence. When understanding of ecological processes is toopoor to support specific quantitative objectives for persistence,planners have resorted to qualitative objectives expressed as pref-erences (Fig. 3). Where feasible, quantitative objectives based onprogressively more reliable ecological data are of course preferable.In this case, planners would usually choose approaches further tothe right in Fig. 3, although the actual order of preference mightvary with context, considering factors such as availability of areasfor conservation, implementation capacity, tradeoffs with fisheriesor other socio-economic objectives, and spatial scale. For example,spatial mismatches between the extent and delineation of theplanning region and the resolution of data or the phenomena ofinterest might complicate and diminish the utility of the sixth ap-proach (targeting surrogates based on analysis of dynamics). In thissituation, planners might be focused on protecting habitats at res-olutions of a few km2 while the most severe sea-surface-tempera-ture anomalies are typically >500 km2 in extent (Selig et al., 2010).Also, strategic decisions about conservation actions would not beappropriate if their scale is on the order of 10s of meters or fewkilometers and larval connectivity estimates are derived from re-gional hydrodynamic models (Treml and Halpin, 2012).
The following six sections refer to the approaches listed fromleft to right at the bottom of Fig. 3.
1. Stating preferences for spatial configuration of MPAs and theirplacement relative to critical areas in the seascape. In the first ap-proach, we group all qualitative criteria that fall under aspects ofspatial arrangement of MPAs or preferences for areas to be man-aged for conservation (e.g. aggregations of key fisheries speciesor areas that could be naturally more resistant or resilient to coralbleaching). Collectively, although qualitative criteria have someutility in their own right when subsequent approaches are not pos-sible, there are major concerns about the benefits that might ac-crue from using qualitative criteria alone (see Section 4.1). Theyprovide a poor basis for assessing progress in the development ofMPA systems and fail to explicitly address uncertainties in conser-vation planning (Nicholson and Possingham, 2006; Noss et al.,2012; Pressey and Cowling, 2001; Sanderson, 2006).
2. Applying generic ‘rules of thumb’ for size and spacing of MPAs.Generic rules of thumb offer simple, quantifiable solutions for verycomplex conservation concerns as a way of making decisions man-ageable. Determining optimum MPA size and spacing is complexdue to the interactions and uncertainties around the scales ofmanagement actions and movements of organisms (Halpern andWarner, 2003; Hastings and Botsford, 2003; Moffitt et al., 2011;Shanks et al., 2003). Many studies derived their statements froma single study (Shanks et al., 2003), which highlighted somemarked taxonomic, geographical, and methodological biases. Usingfindings of one or a few studies in this way ignores the likely needto alter the source recommendations according to specific charac-teristics of diverse study regions. Also, meeting size and spacingguidelines has not been proved to guarantee persistence for allspecies that managers might wish to protect (Moffitt et al.,2011). Furthermore, our review indicates that aiming for a rangeof MPA sizes and spacing might be more effective in achievingmultiple objectives (e.g. connectivity, climate change) than usingsingle, specific thresholds such as specifying a maximum distancebetween MPAs at which populations might no longer beconnected. Such thresholds are often recommended for ease ofapplication, but have also been criticized for their inability toaddress the requirements of multiple species and ecosystems.
Potential tradeoffs between objectives for climate change andconnectivity (Section 3.4.) arise when design criteria focus onlyon MPA size and spacing. Recommendations from the literature
Fig. 3. A framework for integrating connectivity and climate change into marine conservation planning through conservation objectives. The framework is based on ahierarchical relationship between qualitative and all types of quantitative statements for conservation planning addressing representation (upper portion of the diagram) andpersistence (lower portion of the diagram). Arrows represent steps toward setting qualitative and quantitative objectives. On the basis of reviewed literature, we provideexamples of six approaches along a gradient of ecological relevance.
indicate that larger and more closely spaced reserves are requiredto achieve connectivity objectives at the expense of climate changeobjectives. The lack of recognition of such trade-offs can lead tounrealistic expectations and outcomes that fail to balance thetwo sets of considerations. For all the reasons discussed here, itis preferable for conservation planners to adopt a more skepticalview when applying rules of thumb by considering other ap-proaches (to the right in Fig. 3), or adapting generic rules derivedfrom studies of other regions to account for, as far as possible, pop-ulation dynamics and other characteristics of specific studyregions.
3. Tailoring replication and representation objectives to therequirements of specific conservation features. Quantitative ap-proaches to considering climate change in marine conservationplanning have largely focused on requiring replication of featureswithin a network as insurance against disturbance events. Typi-cally, rules of thumb for MPA network design (e.g. McLeod et al.,2009), including those for replication, are uniform prescriptionsapplied to all biodiversity features, which potentially underminestheir effectiveness and defensibility. More effective conservationoutcomes might be achieved by refining these recommendations,for example by considering the rarity and geographic extent of
Fig. 4. Conceptual diagram detailing some advantages and drawbacks when applying each of the six approaches (from Fig. 3) to setting persistence-related objectives to planfor connectivity and climate change. Arrows combined with intensity of shading indicate the direction of change along the gradient. Bidirectional arrows with uniformshading (explicitness, need to resolve tradeoffs, and potential benefits to multiple species) indicate no consistent change across the six approaches. Some considerations arenot relevant to qualitative objectives. * Potential to achieve synergies refers to the possibility of achieving multiple objectives when planning for connectivity and/or climatechange. This could involve achieving both types of objectives simultaneously. Alternatively, for example, planning for disturbances related to climate change could alsocontribute to objectives regarding other kinds of disturbances. As planning becomes more ecologically informed towards the right side of the diagram, we suggest that thepotential for synergies increases.
each feature, intra-habitat variability in species composition or in-tra-specific genetic variation (Harborne, 2009), the distributionand severity of expected disturbances, feature-specific vulnerabil-ity to disturbance by climate change, and aspects of dispersal oforganisms of interest that determine recovery times of disturbedareas. Appropriate spacing between replicates will also dependupon the spatial characteristics of the expected disturbances,which can be informed by time-series analysis (Allison et al.,2003; Ban et al., 2012).
Similarly, objectives for representation can be fine-tuned topromote persistence. One approach is to consider insurancefactors related to frequency of disturbances and estimatedrecovery times (Allison et al., 2003) perhaps combined withspacing protected samples of ecosystems to minimize the likeli-hood of all being affected by the same disturbance events(and see Thrush et al., 2005). Another approach is to adjustobjectives for features according to specific dispersal traits andavailable evidence concerning threats. For instance, species withshort dispersal distances and/or small home ranges mightdecline quickly with increased localized fishing pressure andtherefore require higher levels of protection across theirdistribution ranges.
4. Using ecological insights to guide rules for spatial relationshipsamong features in decision support tools. Despite technical limita-tions, the use of decision-support tools provides insights into the
planning implications of connectivity objectives and the potentialgains to be made over applying rules of thumb for MPA size andspacing. Our review highlighted that the most widespread ap-proach to incorporating connectivity into marine conservationplanning was by altering the value of the boundary length modifier(BLM) in Marxan (Watts et al., 2009). Whilst changing the BLM al-ters the importance of structural connectivity relative to otherparameters (Lotter et al., 2010), it does not address functional con-nectivity. Therefore, if the aim is to ensure biodiversity persistence,there is an inherent limitation in BLM. This limitation is partiallyresolved by using the connectivity strength modifier (CSM) in placeof BLM in Marxan by recognizing the asymmetry of ecologicalconnections (Beger et al., 2010), but neither BLM nor CSM allowspecies- or ecosystem-specific connectivity to be applied acrossmultiple features.
Feature-specific connectivity can, however, be employed byZonation – software that has rarely been used in a marine context(but see Leathwick et al., 2008; Delavenne et al., 2012). To take ac-count of the likely impacts of fragmentation on species protectionprovided by MPAs when defining the biological value of a focal cell,the boundary quality penalty (BQP) assesses consequences of con-nectivity loss in neighborhoods of varying size and at varying ratesdescribing the connectivity requirements and strength of connec-tivity for the species (Lehtomäki and Moilanen, 2013). A remainingconstraint here is the inability of Zonation to include the
multi-directional connectivity typical of marine ecosystems (Begeret al., 2010).
Despite these constraints on BLM, CSM and BQP, and puttingaside some unsophisticated applications, tools such as Marxanand Zonation can provide valuable starting points for strengthen-ing the ecological basis of conservation planning. Ultimately, how-ever, decisions about the design of MPAs will require more thanconnectivity parameters in software. Informed decisions aboutconnectivity will benefit from interactive and iterative use ofdecision-support tools by people familiar with planning regions(Pressey et al., 2013).
5. Defining objectives for structural or functional surrogates. Thisapproach provides planners with a refined set of surrogates thatmight be targeted to conserve and maintain ecological processesand incorporate climate-related disturbances. The approach alsolays the foundation for identifying functionally well-connected orresilient areas in the sixth approach that follows. Instead of aspir-ing to comprehensively assess biodiversity, objectives can be setfor functional groups that sustain ecosystem processes or areasof high species richness (e.g. Hooker et al., 2011), if these or otheraspects of marine ecosystems can be established as reliable proxiesfor resilience to climate change or maintenance of ecological pro-cesses. This would be one way for managers to take a resilience-based approach and afford a degree of insurance against ecologicaluncertainty (Hughes et al., 2005). Furthermore, because some fea-tures in seascapes have been identified as proxies for both connec-tivity processes and resilience to climate change (e.g. source areasthat ensure recruitment to damaged sites or areas that retain highdiversity), these features can be prioritized when planning for mul-tiple objectives.
Many types of surrogates have been proposed for biodiversityprocesses, but few have been tested. Physical seascape attributessuch as marine channels, nursery habitats and upwelling areas ex-hibit some level of spatial or temporal predictability (Game et al.,2009), and might act as surrogates for ecological processes impor-tant to population persistence (Pressey et al., 2007), for example aspredictors of spawning aggregation sites. Nevertheless, empiricalstudies testing the link between these spatial surrogates and themaintenance of ecosystem processes are lacking, so it is unclearwhich surrogates should be prioritized in planning, particularlywhen socio-economic constraints might prevent all conservationobjectives from being achieved. Consideration of surrogates relatedto the effects of climate change has sometimes entailed a focus onprioritizing resistant, resilient, or otherwise undisturbed habitats.There is some debate as to whether increased resilience to climatechange impacts is conferred by an absence of local stress, as moststudies presume (Folke et al., 2002; Heller and Zavaleta, 2009;Mumby and Steneck, 2008), or the opposite (Côté and Darling,2010). Furthermore, resilience is understood to be conferred notby any one attribute of areas, but through a combination of phys-ical, biological, and anthropogenic conditions acting synergisticallyto promote recovery after disturbance. Thus, attempts to incorpo-rate climate change into conservation planning inevitably requireassumptions about the identification of resilient sites and theirmanagement requirements (Game et al., 2008a).
6. Predicting and targeting functional surrogates based on analysisof dynamics. The ability to predict spatial and temporal patterns offunctionally critical sites and evaluate their relative importance tospecies and communities has obvious advantages for conservationplanning. Prediction of dynamic features and targeting or optimiz-ing connectivity features such as spawning sites and stepping-stone areas might simultaneously achieve many benefits for biodi-versity conservation. The range of quantitative methods identifiedin this approach can also contribute to predictions of habitat lossand disruption of connectivity pathways under future disturbancesand increase the potential to achieve synergies (see Fig. 4).
Although integrating prediction of dynamic conservation featureswith reserve selection tools is not straightforward, a small numberof peer-reviewed theoretical studies have demonstrated potentialways in which this could be achieved. By doing so, it might be pos-sible to better estimate the required percentage targets for criticalsites that sustain ecological processes within networks of MPAs,while also highlighting the ecological benefits of connectivityobjectives.
Typically, criteria grouped under this approach detail connec-tivity requirements of only a few well-known focal species, forc-ing practitioners to extrapolate this information when drawingconservation plans for a wider range of species. For instance,Jacobi and Jonsson (2011) offered an improved link between con-nectivity and metapopulation dynamics and presented an optimaldesign of MPAs based on a specific life-history trait. However,more realistic and ideal planning efforts need to assess howMPAs will affect a broad range of target species and assess theeffects of species interactions within communities. As Baskett et al.(2007) and Economo (2011) point out in their metacommunityapproaches, species interactions raise an additional complicationin predicting effective reserve size, spacing, and placement: undercompetition, only species with dissimilar combinations of traitswould coexist, revealing an inherent tradeoff between representa-tion and persistence.
By protecting important habitats and ecosystem functions, suchas thermal refugia (see Ban et al., 2012), and ensuring that a certainamount of these areas remains protected through time, an MPAsystem designed around dynamic features provides the foundationfor ecosystem-based mitigation and adaptation strategies. Formu-lating the most accurate conservation objectives requires consider-ation of exposure, sensitivity, and adaptive capacity to climatechange (Williams et al., 2008). Although current techniques to pre-dict climate change impacts have focused on exposure (Dawsonet al., 2011), new perspectives are emerging for integrating sensi-tivity and adaptive capacity in the conceptualization of objectivesto make conservation objectives more informative and conserva-tion actions more appropriate (Donner et al., 2005; Pandolfiet al., 2011). Furthermore, more reliable conservation objectiveswould benefit from species-specific analyses of shifting habitatsuitability and inclusion of other underlying mechanisms in re-sponse to climate change such as changes in patterns of speciesdispersal.
4.3. Future prospects
Based on our review, there are at least four important re-search or management issues that require consideration tostrengthen the management guideline presented above. First, rec-ommendations and stated objectives should be accompanied byclear statements about their ecological rationale and any under-lying assumptions to facilitate application within different geo-graphic and socioeconomic contexts. In addition, the utility ofsurrogates to estimate temporal and spatial relationships be-tween biotic and abiotic factors and how these change over timeshould be carefully considered and further empirical research ef-forts are required here. Practitioners might also consider whetherdynamic surrogates, such as sea-surface-temperate anomalies,have a certain level of predictability as a means to outweighuncertainties around their temporal and/or spatial variation. Like-wise, the role played by common ‘rules of thumb’ in achievingreal outcomes for connectivity and climate change objectives re-mains unknown and increasing the underpinning ecologicalknowledge upon which to devise appropriate management guide-lines would have strong benefits for conservation. Finally, furthertesting is needed to more fully understand the implications ofmetacommunity theory for marine spatial planning, considering
objectives for species with distinct combinations of life-historytraits and the effects of integrating competitive interactions.
5. Conclusions
A clear conclusion of this literature review is that, whilst con-nectivity and climate change have been widely considered in mar-ine conservation planning, this has been largely throughqualitative and conceptual statements that have not been explic-itly translated into quantitative objectives, or supported by ecolog-ical evidence. Yet ecologically justified, quantitative objectives arecritical to achieving effective outcomes through conservationplanning.
Although the consideration of connectivity and climate changein conservation planning remains largely subjective, the widerange of possible approaches provides insights as to how conserva-tion objectives for connectivity and climate change could be set toinform more effective planning. Many criteria recommended forintegrating connectivity into the design of MPAs were also citedas preventing negative impacts from climatic disturbances, provid-ing opportunities to integrate objectives for both. Although there isno perfect approach for any conservation plan, the frameworkpresented here will hopefully assist planners to move towardsplanning for the persistence of biodiversity features by focusingon the ecological evidence base. Our suggested frameworkcontains a non-exhaustive set of complementary approaches thatcould be adopted for refining marine conservation planning withrespect to persistence:
1. Stating preferences for spatial configuration of MPAs and theirplacement relative to critical areas in the seascape.
2. Applying generic ‘rules of thumb’ for size and spacing of MPAs.3. Tailoring replication and representation objectives to the
requirements of specific conservation features.4. Using ecological insights to guide rules for spatial relationships
among features in decision support tools.5. Defining objectives for structural or functional surrogates.6. Predicting and targeting functional surrogates based on analysis
of dynamics.
Strategies for considering connectivity and climate change inmarine conservation planning must move towards explicit, quanti-tative objectives grounded in ecological knowledge. Empiricalunderstanding of ecological connectivity processes and factors thatconfer resilience to climate change impacts is rapidly improving,and new methods to incorporate this knowledge into existingconservation planning frameworks are emerging. We show that,even in the absence of these new insights, advances can be madethrough careful consideration of context-specific threats andvulnerabilities when refining replication and representationobjectives.
Acknowledgements
RAM thanks CNPq-Brazil for financial support. RAM, RLP, RW,and NCB acknowledge support from the Australian ResearchCouncil. We are grateful to anonymous reviewers who providedconstructive and insightful comments.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.biocon.2013.12.032.
References
Agostini, V.N., Grantham, H.S., Wilson, J., Mangubhai, S., Rotinsulu, C., Hidayat, N.,Muljadi, A., Muhajir, Mongdong, M., Darmawan, A., Rumetna, L., Erdmann, M.V.,Possingham, H.P., 2012. Achieving Fisheries and Conservation Objectives withinMarine Protected Areas: Zoning the Raja Ampat Network. The NatureConservancy, Denpasar.
Airamé, S., Dugan, J.E., Lafferty, K.D., Leslie, H., McArdle, D.A., Warner, R.R., 2003.Applying ecological criteria to marine reserve design: a case study from theCalifornia Channel Island. Ecol. Appl. 13, S170–S184.
Allnutt, T.F., McClanahan, T.R., Andréfouët, S., Baker, M., Lagabrielle, E., McClennen,C., Rakotomanjaka, A.J.M., Tianarisoa, T.F., Watson, R., Kremen, C., 2012.Comparison of marine spatial planning methods in Madagascar demonstratesvalue of alternative approaches. PLoS One 7, 1–15.
Ardron, J., Lash, J., Haggarty, D., 2002. Modelling a Network of Marine ProtectedAreas for the Central Coast of BC, Living Oceans Society Version 3.1., Sointula.
Ban, N., 2009. Minimum data requirements for designing a set of marine protectedareas, using commonly available abiotic and biotic datasets. Biodivers. Conserv.18, 1829–1845.
Ban, N., Royle, K., Short, C., Davis, N., Bodtker, K., Bryan, T., Cripps, K., Day, A.,Haggarty, D., Lee, L., 2008. Identifying priority areas for marine conservation inBritish Columbia: a collaborative approach. In: Proceedings of the SixthInternational Conference of Science and the Management of Protected Areas,pp. 251–262.
Ban, N.C., Hansen, G.J.A., Jones, M., Vincent, A.C.J., 2009. Systematic marineconservation planning in data-poor regions: socioeconomic data is essential.Mar. Policy 33, 794–800.
Ban, N.C., Pressey, R.L., Weeks, S., 2012. Conservation objectives and sea-surfacetemperature anomalies in the Great Barrier Reef. Conserv. Biol. 26, 799–809.
Baskett, M.L., Micheli, F., Levin, S.A., 2007. Designing marine reserves for interactingspecies: insights from theory. Biol. Conserv. 137, 163–179.
Beger, M., Linke, S., Watts, M., Game, E., Treml, E., Ball, I., Possingham, H.P., 2010.Incorporating asymmetric connectivity into spatial decision making forconservation. Conserv. Lett. 3, 359–368.
Berglund, M., Jacobi, M.N., Jonsson, P.R., 2012. Optimal selection of marineprotected areas based on connectivity and habitat quality. Ecol. Model. 240,105–112.
Berumen, M.L., Almany, G.R., Planes, S., Jones, G.P., Saenz-Agudelo, P., Thorrold, S.R.,2012. Persistence of self-recruitment and patterns of larval connectivity in amarine protected area network. Ecol. Evol. 2, 444–452.
Birrell, C.L., McCook, L.J., Willis, B.L., Diaz-Pulido, G.A., 2008. Effects of benthic algaeon the replenishment of corals and the implications for the resilience of coralreefs. Oceanogr. Mar. Biol. Annu. Rev. 46, 25–64.
Bode, M., Armsworth, P.R., Fox, H.E., Bode, L., 2012. Surrogates for reef fishconnectivity when designing marine protected area networks. Mar. Ecol. Prog.Ser. 466, 155–166.
Bottrill, M.C., Pressey, R.L., 2012. The effectiveness and evaluation of conservationplanning. Conserv. Lett. 5, 407–420.
Côté, I.M., Darling, E.S., 2010. Rethinking ecosystem resilience in the face of climatechange. PLoS Biol. 8, 1–5.
Cowen, R.K., Sponaugle, S., 2009. Larval dispersal and marine populationconnectivity. Annu. Rev. Mar. Sci. 1, 443–466.
Delavenne, J., Metcalfe, K., Smith, R.J., Vaz, S., Martin, C.S., Dupuis, L., Coppin, F.,Carpentier, A., 2012. Systematic conservation planning in the eastern EnglishChannel: comparing the Marxan and Zonation decision-support tools. ICES J.Mar. Sci. 69, 75–83.
Donner, S.D., Skirving, W.J., Little, C.M., Oppenheimer, M., Hoegh-Guldberg, O., 2005.Global assessment of coral bleaching and required rates of adaptation underclimate change. Global Change Biol. 11, 2251–2265.
Economo, E.P., 2011. Biodiversity conservation in metacommunity networks:linking pattern and persistence. Am. Nat. 177, 167–180.
Edwards, H.J., Elliott, I.A., Pressey, R.L., Mumby, P.J., 2009. Incorporating ontogeneticdispersal, ecological process and conservation zonig into reserve design. Biol.Conserv. 143, 457–470.
Ferdana, Z., 2002. Approaches to integrating a marine GIS into The NatureConservancy’s ecoregional planning process. In: Breman, J. (Ed.), MarineGeography: GIS for the Oceans and Seas. Redlands, Washington, pp. 151–158.
Ferdana, Z., Newkirki, S., Whelchel, A.W., Gilmer, B., Beck, M.W., 2010. Buildinginteractive decision support to meet management objectives for coastalconservation and hazard mitigation on Long Island, New York, USA. In: Perez,
A.A., Fernandez, B.H., Gatti, R.C. (Eds.), Building Resilience to Climate Change.International Union for Conservation of Nature, Gland, pp. 73–88.
Fernandes, L., Day, J.O.N., Lewis, A., Slegers, S., Kerrigan, B., Breen, D.A.N., Cameron,D., Jago, B., Hall, J., Lowe, D., Innes, J., Tanzer, J., Chadwick, V., Thompson, L.,Gorman, K., Simmons, M., Barnett, B., Sampson, K., De’Ath, G., Mapstone, B.,Marsh, H., Possingham, H., Ball, I.A.N., Ward, T., Dobbs, K., Aumend, J., Slater,D.E.B., Stapleton, K., 2005. Establishing representative no-take areas in the GreatBarrier Reef: large-scale implementation of theory on marine protected areas.Conserv. Biol. 19, 1733–1744.
Fernandes, L., Green, A., Tanzer, J., White, A., Alino, P.M., Jompa, J., Lokani, P.,Soemodinoto, A., Knight, M., Pomeroy, B., Possingham, H.P., Pressey, R.L., 2012.Biophysical Principles for Designing Resilient Networks of Marine ProtectedAreas to Integrate Fisheries, Biodiversity and Climate Change Objectives in theCoral Triangle. The Nature Conservancy for the Coral Triangle SupportPartnership, Jakarta.
Folke, C., Carpenter, S., Elmqvist, T., Gunderson, L., Holling, C.S., Walker, B., 2002.Resilience and sustainable development: building adaptive capacity in a worldof transformations. AMBIO: J. Human Environ. 31, 437–440.
Fox, H.E., Mascia, M.B., Basurto, X., Costa, A., Glew, L., Heinemann, D., Karrer, L.B.,Lester, S.E., Lombana, A., Pomeroy, R., 2012. Reexamining the science of marineprotected areas: linking knowledge to action. Conserv. Lett. 5, 1–10.
Gaines, S.D., White, C., Carr, M.H., Palumbi, S.R., 2010. Designing marine reservenetworks for both conservation and fisheries management. Proc. Natl. Acad. Sci.107, 18286–18293.
Game, E.T., McDonald-Madden, E., Puotinen, M.L., Possingham, H.P., 2008a. Shouldwe protect the strong or the weak? Risk, resilience, and the selection of marineprotected areas. Conserv. Biol. 22, 1619–1629.
Game, E.T., Watts, M.E., Wooldridge, S., Possinghan, H.P., 2008b. Planning forpersistence in marine reserves: a question of catastrophic importance. Ecol.Appl. 18, 670–680.
Game, E.T., Grantham, H.S., Hobday, A.J., Pressey, R.L., Lombard, A.T., Beckley, L.E.,Gjerde, K., Bustamante, R., Possingham, H.P., Richardson, A.J., 2009. Pelagicprotected areas: the missing dimension in ocean conservation. Trends Ecol.Evol. 24, 360–369.
Game, E.T., Lipsett-Moore, G., Hamilton, R., Peterson, N., Kereseka, J., Atu, W., Watts,M., Possingham, H., 2010. Informed opportunism for conservation planning inthe Solomon Islands. Conserv. Lett. 4, 38–46.
Game, E.T., Lipsett-Moore, G., Saxon, E., Peterson, N., Sheppard, S., 2011.Incorporating climate change adaptation into national conservationassessments. Global Change Biol. 17, 3150–3160.
Game, E.T., Kareiva, P., Possingham, H., 2013. Six common mistakes in conservationpriority setting. Conserv. Biol. 27, 480–485.
Geselbracht, L., Torres, R., Cumming, G.S., Dorfman, D., Beck, M., Shaw, D., 2009.Identification of a spatially efficient portfolio of priority conservation sites inmarine and estuarine areas of Florida. Aquat. Conserv.: Mar. Freshwater Ecosyst.19, 408–420.
Giakoumi, S., Grantham, H.S., Kokkoris, G.D., Possingham, H.P., 2011. Designing anetwork of marine reserves in the Mediterranean Sea with limited socio-economic data. Biol. Conserv. 144, 753–763.
Grantham, H.S., Game, E.T., Lombard, A.T., Hobday, A.J., Richardson, A.J., Beckley,L.E., Pressey, R.L., Huggett, J.A., Coetzee, J.C., van der Lingen, C.D., Petersen, S.L.,Merkle, D., Possingham, H.P., 2011. Accommodating dynamic oceanographicprocesses and pelagic biodiversity in marine conservation planning. PLoS One 6,1–16.
Green, A., Smith, S.E., Lipsett-Moore, G., Groves, C., Peterson, N., Sheppard, S., Lokani,P., Hamilton, R., Almany, J., Aitsi, J., Bualia, L., 2009. Designing a resilientnetwork of marine protected areas for Kimbe Bay, Papua New Guinea. Oryx 43,488–498.
Grimsditch, G.D., Salm, R.V., 2006. Coral Reef Resilience and Resistance to Bleaching.The International Union for the Conservation of Nature and Natural Resources,Gland.
Halpern, B.S., Warner, R.R., 2003. Matching marine reserve design to reserveobjectives. Proc. R. Soc. London 270, 1871–1878.
Halpern, B.S., Kappel, C.V., Selkoe, K.A., Micheli, F., Ebert, C.M., Kontgis, C., Crain,C.M., Martone, R.G., Shearer, C., Teck, S.J., 2009. Mapping cumulative humanimpacts to California Current marine ecosystems. Conserv. Lett. 2, 138–148.
Hansen, L., Hoffman, J., Drews, C., Mielbrecht, E., 2009. Designing climate-smartconservation: guidance and case studies. Conserv. Biol. 24, 63–69.
Hansen, G.J.A., Ban, N.C., Jones, M.L., Kaufman, L., Panes, H.M., Yasué, M., Vincent,A.C.J., 2011. Hindsight in marine protected area selection: a comparison ofecological representation arising from opportunistic and systematicapproaches. Biol. Conserv. 144, 1866–1875.
Harborne, A.R., 2009. First among equals: why some habitats should be consideredmore important than others during marine reserve planning. Environ. Conserv.36, 87–90.
Harrison, H.B., Williamson, D.H., Evans, R.D., Almany, G.R., Thorrold, S.R., Russ, G.R.,Feldheim, K.A., Van Herwerden, L., Planes, S., Srinivasan, M., 2012. Larval exportfrom marine reserves and the recruitment benefit for fish and fisheries. Curr.Biol. 22, 1023–1028.
Hastings, A., Botsford, L.W., 2003. Comparing designs of marine reserves forfisheries and for biodiversity. Ecol. Appl. 13, S65–S70.
Heller, N.E., Zavaleta, E.S., 2009. Biodiversity management in the face of climatechange: a review of 22 years of recommendations. Biol. Conserv. 142, 14–32.
Hinchley, D., Lipsett-Moore, G., Sheppard, S., Sengebau, F.U., Verheij, E., Austin, S.,2007. Biodiversity Planning for Palau’s Protected Areas Network: AnEcoregional Assessment. The Nature Conservancy, Brisbane.
Hooker, S.K., Cañadas, A., Hyrenbach, K.D., Corrigan, C., Polovina, J.J., Reeves, R.R.,2011. Making protected area networks effective for marine top predators.Endanger. Species Res. 13, 203–218.
Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly, S.R., Folke, C., Grosberg,R., Hoegh-Guldberg, O., Jackson, J., Kleypas, J., 2003. Climate change, humanimpacts, and the resilience of coral reefs. Science 301, 929–933.
Hughes, T.P., Bellwood, D.R., Folke, C., Steneck, R.S., Wilson, J., 2005. New paradigmsfor supporting the resilience of marine ecosystems. Trends Ecol. Evol. 20, 380–386.
Jacobi, M.N., Jonsson, P.R., 2011. Optimal networks of nature reserves can be foundthrough eigenvalue perturbation theory of the connectivity matrix. Ecol. Appl.21, 1861–1870.
Keller, B.D., Gleason, D.F., McLeod, E., Woodley, C.M., Airamé, S., Causey, B.D.,Friedlander, A.M., Grober-Dunsmore, R., Johnson, J.E., Miller, S.L., 2009. Climatechange, coral reef ecosystems, and management options for marine protectedareas. Environ. Manage. 44, 1069–1088.
Kininmonth, S., Beger, M., Bode, M., Peterson, E., Adams, V.M., Dorfman, D.,Brumbaugh, D.R., Possingham, H.P., 2011. Dispersal connectivity and reserveselection for marine conservation. Ecol. Model. 222, 1272–1282.
Klein, C.J., Steinback, C., Scholz, A.J., Possingham, H.P., 2008. Effectiveness of marinereserve networks in representing biodiversity and minimizing impact tofishermen: a comparison of two approaches used in California. Conserv. Lett.1, 44–51.
Knight, A.T., Cowling, R.M., Campbell, B.M., 2006. An operational model forimplementing conservation action. Conserv. Biol. 20, 408–419.
Lawler, J.J., 2009. Climate change adaptation strategies for resources managementand conservation planning. Ann. N. Y. Acad. Sci. 1162, 79–98.
Leathwick, J., Moilanen, A., Francis, M., Elith, J., Taylor, P., 2008. Novel methods forthe design and evaluation of marine protected areas in offshore waters.Conserv. Lett. 2, 91–102.
Lehtomäki, J., Moilanen, A., 2013. Methods and workflow for spatial conservationprioritization using Zonation. Environ. Modell. Software 47, 128–137.
Leslie, H.M., 2005. A synthesis of marine conservation planning approaches.Conserv. Biol. 19, 1701–1713.
Lett, C., Ayata, S., Huret, M., Irisson, J., 2010. Biophysical modelling to investigate theeffects of climate change on marine population dispersal and connectivity. Prog.Oceanogr. 87, 106–113.
Levy, J.S., Ban, N.C., 2012. A method for incorporating climate change modelling intomarine conservation planning: an Indo-west Pacific example. Mar. Policy 38,16–24.
Lombard, A.T., Reyers, B., Schonegevel, L.Y., Cooper, J., Smith-Adao, L.B., Nel, D.C.,Froneman, P.W., Ansorge, I.J., Bester, M.N., Tosh, C.A., 2007. Conserving patternand process in the Southern Ocean: designing a Marine Protected Area for thePrince Edward Islands. Antarct. Sci. 19, 39–54.
Lotter, M., Lieberknecht, L., Ardron, J.A., Wells, R., Ban, N.C., Nicolson, D.J., Gerhartz,J.L., 2010. Reserve design considerations. In: Ardron, J.A., Possingham, H.P.,Klein, C.J. (Eds.), Marxan Good Practices Handbook. Pacific Marine Analysis andResearch Association, Victoria, BC, pp. 39–48.
Lowry, G.K., White, A.T., Christie, P., 2009. Scaling up to networks of marineprotected areas in the Philippines: biophysical, legal, institutional, and socialconsiderations. Coast. Manage. 37, 274–290.
Maina, J., Venus, V., McClanahan, T.R., Ateweberhan, M., 2008. Modellingsusceptibility of coral reefs to environmental stress using remote sensing dataand GIS models. Ecol. Model. 212, 180–199.
Malcolm, H.A., Foulsham, E., Pressey, R.L., Jordan, A., Davies, P.L., Ingleton, T.,Johnstone, N., Hessey, S., Smith, S.D.A., 2012. Selecting zones in a marine park:early systematic planning improves cost-efficiency; combining habitat andbiotic data improves effectiveness. Ocean Coast. Manag. 59, 1–12.
McCook, L.J., Almany, G.R., Berumen, M.L., Day, J.C., Green, A.L., Jones, G.P., Leis, J.M.,Planes, S., Russ, G.R., Sale, P.F., Thorrod, S.R., 2009. Management underuncertainty: guide-lines for incorporating connectivity into the protection ofcoral reefs. Coral Reefs 28, 353–366.
McLeod, E., Salm, R., Green, A., Almany, J., 2009. Designing marine protected areanetworks to address the impacts of climate change. Front. Ecol. Environ. 7, 362–370.
McLeod, E., Moffitt, R., Timmermann, A., Salm, R., Menviel, L., Palmer, M.J., Selig,E.R., Casey, K.S., Bruno, J.F., 2010. Warming Seas in the Coral Triangle: coralreef vulnerability and management implications. Coast. Manage. 38, 518–539.
Moffitt, E.A., White, J.W., Botsford, L.W., 2011. The utility and limitations of size andspacing guidelines for designing marine protected area (MPA) networks. Biol.Conserv. 144, 306–318.
Mora, C., Andréfouët, S., Costello, M.J., Kranenburg, C., Rollo, A., Veron, J., Gaston, K.J.,Myers, R.A., 2006. Coral reefs and the global network of marine protected areas.Science 312, 1750–1751.
Mumby, P.J., Hastings, A., 2008. The impact of ecosystem connectivity on coral reefresilience. J. Appl. Ecol. 45, 854–862.
Nicholson, E., Possingham, H.P., 2006. Objectives for multiple-species conservationplanning. Conserv. Biol. 20, 871–881.
Noss, R.F., Dobson, A.P., Baldwin, R., Beier, P., Davis, C.R., Dellasala, D.A., Francis, J.,Locke, H., Nowak, K., Lopez, R., 2012. Bolder thinking for conservation. Conserv.Biol. 26, 1–4.
O’Connor, M.I., Bruno, J.F., Gaines, S.D., Halpern, B.S., Lester, S.E., Kinlan, B.P., Weiss,J.M., 2007. Temperature control of larval dispersal and the implications formarine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. 104, 1266–1271.
Pandolfi, J.M., Connolly, S.R., Marshall, D.J., Cohen, A.L., 2011. Projecting coralreef futures under global warming and ocean acidification. Science 333, 418–422.
Pressey, R.L., Bottril, M.C., 2008. Opportunism, threats, and the evolution ofsystematic conservation planning. Conserv. Biol. 22, 1340–1345.
Pressey, R.L., Bottrill, M.C., 2009. Approaches to landscape- and seascape-scaleconservation planning: convergence, contrasts and challenges. Oryx 43, 464–475.
Pressey, R.L., Cowling, R.M., 2001. Reserve selection algorithms and the real world.Conserv. Biol. 15, 275–277.
Pressey, R.L., Cowling, R.M., Rouget, M., 2003. Formulating conservation targets forbiodiversity pattern and process in the Cape Floristic Region, South Africa. Biol.Conserv. 112, 99–127.
Pressey, R.L., Cabeza, M., Matthew, E.W., Cowling, R.M., Wilson, K.A., 2007.Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592.
Pressey, R.L., Mills, M., Weeks, R., Day, J.C., 2013. The plan of the day: managing thedynamic transition from regional conservation designs to local conservationactions. Biol. Conserv. 166, 155–169.
Richardson, E.A., Kaiser, M.J., Edwards-Jones, G., Possingham, H.P., 2006. Sensitivityof marine-reserve design to the spatial resolution of socioeconomic data.Conserv. Biol. 20, 1191–1202.
Rouget, M., Cowling, R.M., Pressey, R.L., Richardson, D.M., 2003. Identifying spatialcomponents of ecological and evolutionary processes for regional conservationplanning in the Cape Floristic Region, South Africa. Divers. Distrib. 9, 191–210.
Runting, R.K., Wilson, K.A., Rhodes, J.R., 2013. Does more mean less? The value ofinformation for conservation planning under sea level rise. Global Change Biol.19, 352–363.
Saarman, E., Gleason, M., Ugoretz, J., Airamé, S., Carr, M., Fox, E., Frimodig, A., Mason,T., Vasques, J., 2013. The role of science in supporting marine protected areanetwork planning and design in California. Ocean Coast. Manage. 74, 45–56.
Sala, E., Aburto-Oropeza, O., Paredes, G., Parra, I., Barrera, J.C., Dayton, P.K., 2002. Ageneral model for designing networks of marine reserves. Science 298, 1991–1993.
Sala, E., Aburto-Oropeza, O., Paredes, G., Thompson, G., 2003. Spawningaggregations and reproductive behavior of reef fishes in the Gulf of California.Bull. Mar. Sci. 72, 103–121.
Salm, R.V., Done, T., McLeod, E., 2006. Marine protected area planning in a changingclimate. In: Phinney, J.T., Hoegh-Guldberg, O., Kleypas, J., Skirving, W., Strong, A.(Eds.), Coral Reefs and Climate Change: Science and Management. AmericanGeophysical Union, Washington, DC, pp. 207–221.
Sanderson, E.W., 2006. How many animals do we want to save? The many ways ofsetting population target levels for conservation. Bioscience 56, 911–922.
Sarkar, S., Pressey, R.L., Faith, D.P., Margules, C.R., Fuller, T., Stoms, D.M., Moffett, A.,Wilson, K.A., Williams, K.J., Williams, P.H., Andelman, S., 2006. Biodiversityconservation planning tools: present status and challenges for the future. Annu.Rev. Environ. Resour. 31, 123–159.
Selig, E.R., Casey, K.S., Bruno, J.F., 2010. New insights into global patterns of oceantemperature anomalies: implications for coral reef health and management.Global Ecol. Biogeogr. 19, 397–411.
Shanks, A.L., Grantham, B.A., Carr, M.H., 2003. Propagule dispersal distance and thesize and spacing of marine reserves. Ecol. Appl. 13, S159–S169.
Smith, R.J., Eastwood, P.D., Ota, Y., Rogers, S.I., 2009. Developing best practice forusing Marxan to locate marine protected areas in European waters. ICES J. Mar.Sci. 66, 188–194.
Soto, C.G., 2002. The potential impacts of global climate change on marine protectedareas. Rev. Fish Biol. Fisheries 11, 181–195.
Stewart, R.R., Noyce, T., Possingham, H.P., 2003. Opportunity cost of ad hoc marinereserve design decisions: an example from South Australia. Mar. Ecol. Prog. Ser.253, 25–38.
Sutherland, W.J., Pullin, A.S., Dolman, P.M., Knight, T.M., 2004. The need forevidence-based conservation. Trends Ecol. Evol. 19, 305–308.
Tear, T.H., Kareiva, P., Angermeier, P.L., Comer, P., Czech, B., Kautz, R., Landon, L.,Mehlman, D., Murphy, K., Ruckelshaus, M., 2005. How much is enough? Therecurrent problem of setting measurable objectives in conservation. Bioscience55, 835–849.
Thrush, S.F., Lundquist, C.J., Hewitt, J.E., 2005. Spatial and temporal scales ofdisturbance to the seafloor: a generalized framework for active habitatmanagement. Am. Fish. Soc. Symp. 41, 639–649.
Treml, E.A., Halpin, P.N., 2012. Marine population connectivity identifies ecologicalneighbors for conservation planning in the Coral Triangle. Conserv. Lett. 5, 441–449.
Treml, E.A., Halpin, P.N., Urban, D.L., Pratson, L.F., 2008. Modeling populationconnectivity by ocean currents, a graph-theoretic approach for marineconservation. Landscape Ecol. 23, 19–36.
Watson, J.R., Siegel, D.A., Kendall, B.E., Mitarai, S., Rassweiller, A., Gaines, S.D., 2011.Identifying critical regions in small-world marine metapopulations. Proc. Natl.Acad. Sci. 108, 907–913.
Watts, M.E., Ball, I.R., Stewart, R.S., Klein, C.J., Wilson, K., Steinback, C., Lourival, R.,Kircher, L., Possingham, H.P., 2009. Marxan with zones: software for optimalconservation based land- and sea-use zoning. Environ. Modell. Software 24,1513–1521.
Weeks, R., Russ, G.R., Alcala, A.C., White, A.T., 2010. Effectiveness of marineprotected areas in the Philippines for biodiversity conservation. Conserv. Biol.24, 531–540.
West, J.M., Salm, R.V., 2003. Resistance and resilience to coral bleaching:implications for coral reef conservation and management. Conserv. Biol. 17,956–967.
Williams, S.E., Shoo, L.P., Isaac, J.L., Hoffmann, A.A., Langham, G., 2008. Towards anintegrated framework for assessing the vulnerability of species to climatechange. PLoS Biol. 6, 2621–2626.
Wilson, J., Darmawan, A., Subijanto, J., Green, A., Sheppard, S., 2011. ScientificDesign of a Resilient Network of Marine Protected Areas: Lesser SundaEcoregion, Coral Triangle. The Nature Conservancy, Brisbane.
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