Milne Bay Corridor Planning A systematic conservation planning approach to opportunity cost trade-offs

Transcription

1 Milne Bay Corridor Planning A systematic conservation planning approach to opportunity cost trade-offs Presented at Melanesia CBC Management Team Meeting, Jayapura, Indonesia 12 September 2005

2 Corridor Planning Goals Identify corridors connecting Key Biodiversity Areas Capture landscape scale species and ecological processes within corridors Identify gaps in representation of ecosystem types captured within KBAs Identify corridor options that minimize impact on livelihoods Delineate candidate corridors

3 Conceptual Framework Sites KBAs/ global significance Conservation Goals Defined CI outcomes process Biodiversity representation Existing PAs Corridors Complementarity National Significance Ecological process, landscape species Regional significance CI Strategy Livelihood Goals Defined Local socioeconomic National macroeconomic Cultural Systematic Conservation Planning framework to address Conservation and Livelihood goals in Milne Bay Province

4 Systematic Conservation Planning GOALS Conservation Livelihoods 1. Species Goals -KBAs -Existing PAs -Landscape species 2. Landscape Goals -Habitat Matrix -Ecological Processes -Representation 1. Subsistence and Food Security -Current -Potential 2. Economic Development -Cash crops -Forestry Viability Goals Fragmentation Access/Risk Landuse -Current -Future Conservation Cost

5 Integrating Marine and Terrestrial Corridor Planning Marine and terrestrial areas are linked by hydrological processes Must coordinate conservation planning to ensure viable marine and terrestrial areas that complement each other Dave Mitchell / CI

6 Marine and Terrestrial Corridor Planning Terrestrial and marine areas can be combined in a single planning framework However, practical problems exist: 1. Compelling problems may not be the same 2. Different biodiversity attributes, targets and opportunity costs 3. Marine data is currently being developed and terrestrial data is already compiled Our proposed solution: an iterative feed-back approach to planning within Milne Bay starting with terrestrial zone

7 Marine and Terrestrial Framework Terrestrial Zone Coastal Zone Mangroves Coastal Zone Mangroves Marine Zone Terrestrial and marine areas are analysed separately, but share common attributes Areas from one zone are used to seed the selection process in the other zone We will test this approach by initially targeting a few intact watersheds

8 Planning Requirements and Data Inputs Planning Unit Watersheds Species Goals KBAs, PAs Biodiversity Surrogate Vegetation type Ecological Process Catchments, coastal zone, riparian zone, mangroves Targets -CI outcomes -Biodiversity surrogate -Ecological process -Intact watersheds Opportunity Cost Trade-off scenarios

9 Planning Units Potential Planning Units Wards (administrative unit) Resource Management Units Sub-Catchments Artificial Units (hexagon) Sub-Catchments Natural unit Neutral with respect to ownership Relates to ecological process Individual watersheds nested within catchments Delineated from hydrological analysis of 90 meter SRTM DEM (watersheds are nested within basins) 4688 planning units, average 300 Ha (range 3 to 3053 Ha)

10 Watershed Planning Units Legend 0 7 Watershed Planning Unit Basin

11 Species Outcomes and KBAs Existing protected areas and sacred sites are included as conserved areas Delineated KBAs form the core areas of the corridor Intact catchments linking with marine areas are targeted

12 Species Outcomes and KBAs Legend STATUS Available Conserved Watersheds Excluded 0 7

13 Biodiversity Surrogate Vegetation mapping (aerial photo) 59 vegetation types in PNG plus 4 nonvegetated categories Polygons contain a mosaic of up to four vegetation types Analysis focused on base vegetation types Biodiversity surrogate is the area of each vegetation type within each planning unit

14 Vegetation Types (Ecosystems) ID code PNG-FIM Vegetation type PNG (Ha) MILNE BAY %PNG % MBP 1 M Mangrove B Mixed forest FriCg Riverine successions with Casuarina grandis Fsw Mixed swamp forest Hm Medium crowned forest Hs Small crowned forest L Small crowned forest Lc Small crowned forest with conifers LN Small crowned forest with Nothofagus LsN Very small crowned forest with Nothofagus Mo Very small crowned forest Pl Large to medium crowned forest Po Open forest Ps Small crowned forest G Grassland Ga Gf Alpine grassland Grassland with some forest Gi Subalpine grassland Hsw Herbaceous swamp Sa Savanna Susan E. Cameron Sc Scv W Wsw Scrub Volcanic successions dominated by scrub Woodland Swamp woodland

15 text 0 7 Biodiversity Surrogate

16 Partial Biodiversity Land use is typically a mosaic of primary vegetation, anthropogenic regrowth vegetation, and current land use (subsistence gardens) We accounted for the partial biodiversity contribution of these areas Multiplied the original vegetation extent by the partial biodiversity value to account for habitat loss and degradation Dave Mitchell / CI

17 Partial Biodiversity Land use Code Class Anthropogenous Vegetation (% area) Current Use (%area) Disturbance (% area) Biodiversity discount value Partial Biodiversity value 0 Very high with tree crops 75+ > Very high with tree crops High Moderate Low < Very low -20 < Extremely low < < Grassland Up to Sago stands sufficient Sub-alpine grassland Alpine grassland Up to Savanna woodland Up to high Assume moderate/high Assume 50 Assume low Assume very low Assume -20 Assume < change Assume moderate/high Assume 50 Assume

18 Ecological Process Coastal Processes 0 meter buffer of existing vegetation along coastline (or according to existing legislation) Mangroves Riparian Processes 0 meter buffer of existing vegetation along major rivers (or according to existing legislation) Catchment Processes Vegetation cover stratified by 5 elevation classes to capture process throughout catchment Dave Mitchell / CI

19 Coastal Buffer 50 Legend Coastal Buffer High 20 Low

20 Mangroves 50 Legend Mangroves High 20 Low

21 Riparian Buffer 50 Legend Riparian Buffer High 20 Low

22 Catchment Stratification 50 Legend Elevation classes 0-0m 0-500m m m >3000m

23 Targets Species targets captured within CI outcomes definition, KBAs Ecosystem targets based on biodiversity surrogate (30 percent representation) Ecological process targets based on hydrology and coastal protection Dave Mitchell / CI

24 Species Area Curve Targets Wsw W Scv Sc Sa Hsw Gi Gf Ga G Ps Po Pl Mo LsN LN Lc L Hs Hm Fsw FriCg B M 1 90% 80% 70% 60% 50% 40% 30% 20% % MBP (Ha) PNG (Ha) code ID Milne Bay conservation target scenarios and area-based percentage targets for forest vegetation types Forest vegetation types (ecosystems)

25 Ecological Process Targets Coastal Protection Mangroves: High, e.g. 75% Fringing coastal vegetation: High, e.g. 75% Riparian Protection (water quality) Riparian buffers of major rivers: 20-25% target Catchment Protection Maintain vegetation cover throughout catchment to reduce erosion and protect water quality and quantity Low targets for low elevation classes High targets for high elevation classes Based upon rarity and importance for maintenance of process Susan E. Cameron

26 Ecological Process Targets Feature Total area (Ha) % target Area target Stream Order Stream Order Stream Order Stream Order Stream Order Coastal Buffer Vegetation cover 1-0m Vegetation cover 0-500m Vegetation cover m Dave Mitchell / CI Vegetation cover m Vegetation cover >3000m

27 Conceptual Framework CI Strategy Conservation Goals Defined Livelihood Goals Defined CI outcomes process Biodiversity representation Local socioeconomic National macroeconomic Systematic Conservation Planning framework to address Conservation and Livelihood goals in Milne Bay Province Food Security Cash crops Forestry, Mining, Fishing Plantation Agriculture Cultural Sacred sites Eco-tourism

28 Opportunity Cost Trade-off Scenarios Food Security Macro Economic Viability and threat 1. Subsistence gardens 2. Cash crop potential 3. Population 1. Timber Volume 2. Agriculture plantations 3. Mining Exploration 1. Distance to roads and airstrips 2. Timber concession and mining exploration leases 3. Population

29 Food Security 1. Land use: area of subsistence gardens per planning unit from FIMS land use ( ) 2. Cash Crop: $ per planning unit from MASP 3. Population: total population per planning unit from 2000 census, distance to village Dave Mitchell / CI

30 Macro Economic Development 1. Forestry Timber volume: Index of actual and potential timber volume (1975), with and without extreme constraints, with and without extreme and serious constraints 2. Timber Concession leases (2004): area per planning unit 3. Mining exploration licenses (2004): area per planning unit Cash crops: $ per planning unit 5. Agriculture Plantations: area per planning unit Legend Exploration License Applications Current Exploration Licenses Exploration License Renewals 20 Milne Bay Planning Area

31 Viability and Threat Analysis Access: Distance from villages, distance from airstrips (currently open and potential), distance from roads (2004) 2. Population: total population per planning unit from 2000 census 3. Exploration licenses, timber concession leases, industrial plantations (area) Legend Census Villages 20 Milne Bay Planning Area

32 Multiple cost trade-offs It is possible to combine some opportunity costs if units are equivalent Example: a combined cost representing the total (sum) area of mining exploration licenses, timber concessions, and industrial plantations per planning unit Cannot combine costs if units are not equivalent Example: dollars of cash crop, number of people and area of land use

33 Multiple cost trade-offs Most opportunity costs will be treated as separate trade-offs in initial analysis Meta-analysis to assess congruence across multiple constraints Example: where are areas with high biodiversity value, low cash crop value, low number of people and low area of land use Conservation utility score measures how many times a planning unit is selected per scenario to assess congruence across multiple costs

34 MBP Corridor Planning database Feature table: represents the distribution of biodiversity features in MBP, rows are planning units, columns are biodiversity features Target table: each biodiversity feature is assigned an area or percentage target Planning unit table: Rows represent planning units Columns record area and conservation status Columns represent the opportunity cost of each planning unit for the different scenarios

35 Planning Tool Marxan: uses simulated annealing heuristic to achieve a solution that maximizes biodiversity and minimizes cost, can use boundary length modifier (BLM) and minimum viable area to select contiguous areas CLUZ: an ArcView 3.2 extension that creates Marxan input files and displays Marxan results

36 Initial Analysis Existing protected areas, KBAs and sacred sites are initially selected and locked in Specific intact watersheds initially selected, may be deselected if cost trade off is too high Urban and oil palm plantation areas are excluded from selection

37 Planning Base Map Legend STATUS Available Conserved Watersheds Excluded 0 7

38 Conservation Utility Score Land use opportunity cost trade-off Utility Score

39 Best Set.7% initially conserved Selected an additional 27% 38% of total area necessary to meet targets Conserved 7 Selected 0

40 Future Work Analyse scenarios for all 25+ opportunity costs Evaluate the effects of multiple constraints with utility score Run multiple constraint analysis (requires cost ranking) Feedback with marine planning process and marine KBAs (ongoing) Present results at peer-review workshop (December 2005) Present options at stakeholder workshop (March 2006)

41 Acknowledgements SEC funded by NSF/Australian Academy of Science Summer Institute and University of California Pacific Rim Research Fellowship CSIRO Sustainable Ecosystems Conservation International