Systematic conservation

planning

In the beginning….In the beginning….

there were forests….

• 70s/80s: Jamie Kirkpatrick, Mike Austin, Bob Pressey, Chris Margules,…

• 80s/90s: Nic Nichols, Dan Faith, Hugh Possingham,…

• The ‘Australian school of conservation planning’

• Application in SPEXAN in the RFA

• Biorap, C-plan, Marxan

marine• 90s: Biorap (World Bank)

• Marine Biorap—primordial conservation and multiple use marine planning

• Marxan: built to respond to GBRMPA requirements: Ian Ball, Hugh Possingham

• Re-engineer Spexan to deal with marine issues, specifically for the GBRMPA RAPinitiative (vast scale, high stakes)

• GBRMP-RAP Highly successful• Technical sense - optimal selection of areas for high

protection with stakeholder support

• Consequent fisheries restructuring - contentious, should have been included

Many other Marxan

applications

• California - Channel Islands

• SE Marine Region (BAOI) (although not the final MPA layout)

• World domination - conservation planning

Example1: GBRMPA-RAP

process• (1) classification—describes the biological

diversity of the entire planning region

• (2) review—evaluates the gaps in existing network of no-take areas

• (3) identification—identifies potential networks of no-take areas that achieve the biological objectives of the rezoning process– optional sets of ‘candidate’ areas = the options

for achieving the biodiversity objectives

• (4) selection—integrates social, economic, cultural, and management factors into development of potential networks to maximize the beneficial and minimise the detrimental impacts;

• (5) draft zoning—invites public comment on a draft zoning plan that displays the proposed new zoning, including the recommended network of no-take areas;

• (6) final zoning—considers the ‘cost’ of converting the draft to a final arrangement;

• (7) monitoring—monitors effectiveness of the new zoning plan

the backroom

• GBRMPA internal capacity limited

• Established Analytical Working Group (AWG); in-house group : GIS, information management, operations, fisheries, PR; independent consultant chair

• Established the independent science committee & socioeconomic committee, to provide decision rules (and build broader ownership)

• AWG developed the analytical protocols (Pressey, Possingham), and the RAP MPA options for stakeholder discussion/public

• Iterative refinement by AWG provided the data platform for reconciliation of stakeholder asks, issues and options

the engine:

decision rules• ecological

• 11 rules (the ‘BOPs’) (AWG/science ctte)Minimum size; large NTAs; replicates; avoid fragmentation;

minimum proportion of each reef and non-reef bioregion; capture cross-shelf diversity; minimum of each type of habitat, community, & physical environment; represent major ocean/ecology processes; special/unique places; adjacent land/sea uses

• Targets at different taxonomic and spatial levels—MPAs to include (eg) 20% of each bioregion; 10% of each habitat type; and 5% of faunal assemblage type

decision rules

Social, economic, cultural, and management feasibility operational principles (the ‘SOPs’; from socio-economic stakeholder ctte)1. Maximise complementarity with human uses and

values

2. Final selection of no-take areas recognises all costs and benefits

3. Complement present and future tenure arrangements

4. Maximise user compliance

Example2: SEMR

• Australia’s Oceans Policy 1998: RMP in marine regions (SE first, then North and SW)

• Biodiversity conservation/management:off-reserve (various tools) and on-reserve (MPAs)

• Major players: oil/gas; fishing - major crisis in number of fish stocks

• MPAs: Marxan for broad areas of interest, then stakeholders for finer scale detail of location, size, boundaries, zoning of MPAs

• Highly biased outcome: mainly deep water MPAs; low proportion of CAR NTAs

BAOI

MPA design risks1. Failing to include CAR samples of the

biodiversity in an MPA network that purports to protect/manage biodiversity

2. Failing to minimise the impacts of the MPAs on the stakeholders’ uses and interests (securing broad base of support, where feasible avoiding adverse economic and social impacts)

SEMR - Hybrid process (switch from marxan optimisation of biodiversity outcomes to stakeholder-driven outcomes)

SEMR process largely achieved the latter but not the former

Reducing the risks #1CAR: Limited biodiversity data?

Surrogates: seascapes for species

Accepting surrogacy risks

• MPA design is about use of surrogates (biodiversity is a complex and vague concept: species-level, assemblage-level, habitat-level, processes,….)

• Surrogacy models: seascapes for species in MPA design carries a big risk (high levels of uncertainty)

• Options may be limited (short timeline for MPAs, no flexibility to collect new data,..)

• Risk management tools: sensitivity analysis -response of the MPA options to various types/levels of input data

Reducing the risks #2Minimising stakeholder issues:

• Data on uses—spatial, most highly-valued to least-valued areas, highest spatial resolution available

• Intention is to fit the MPAs around the high-value areas for each stakeholder, as far as possible while still achieving a good set of MPAs for biodiversity

• Trade-offs are always required

• Typical real-world MPA design problems require many trade-offs to be explored, seeking the most optimal set of MPAs

2007: systematic conservation planning for MPAs

• Marine systems/biodiversity poorly known but highly valued: highly speciose, complex multi-scale processes, high levels of endemicity, iconic species,….

• Information base for biodiversity features and ecological processes is mainly surrogates (some species level data, but taxonomically and spatially highly biased)

• Marine conservation planning: precautionary, reflect the risks of many uncertainties - using taxonomic and spatially poorly-resolved surrogates

• Deals effectively, efficiently and transparently with the risk of failing to achieve biodiversity conservation objectives within MPAs designed to have the least cost to stakeholders

• Such complex problems require a systematic decision-support approach