Coupling Ecology and GIS Technology to Evaluate the Efficacy of Marine Protected Areas in Hawaii
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1 Coupling Ecology and GIS Technology to Evaluate the Efficacy of Marine Protected Areas in Hawaii Alan M. Friedlander 1, Eric Brown 2,3 Mark Monaco 4 and Athline Clark 2 1 NOAA/NOS/National Centers for Coastal and Ocean Science/Biogeography Team & The Oceanic Institute, Waimanalo, HI 2 State of Hawaii, DLNR, Division of Aquatic Resources 3 National Park Service, Kalaupapa NHP, Hawaii 4 NOAA/NOS/National Centers for Coastal and Ocean Science/Biogeography Team, Silver Spring, MD
2 Organization of Presentation Objective: Conduct integrative coral reef ecosystem mapping and monitoring to evaluate MPA effectiveness, define essential fish habitat, and identify biologically relevant MPA boundaries. I) Background - Integrated Benthic Habitat Mapping and Monitoring II) Define Reef Fish Habitat Utilization Patterns III) Evaluate MPA Effectiveness IV) Design principles for reserves in Hawaii NOAA/NOS National Centers for Coastal Ocean Science
3 Imagery for Developing Benthic Habitat Maps IKONOS true-color; 4 m pixel AERIAL PHOTOGRAPHY true-color; 1.2 m pixel HYPERSPECTRAL 72 bands between 350 and 1000 nm; 3 m pixel NOAA/NOS National Centers for Coastal Ocean Science
4 South Molokai, Hawaii Coral Reef Ecosystem Habitats MMU = 0.4 ha (1 acre) NOAA/NOS National Centers for Coastal Ocean Science
5 South Molokai Study Area 4. Map Accuracy Analysis 1. Draw Boundary 2. Delineate Habitats 3. Generate Random Points 5. Assessment Transects- Fish/benthic Community 6. Identify Essential Fish Habitat 7. Evaluation of Marine Protected Areas
6 Locations of Hawaii Marine Life Conservation Districts and the University Marine Laboratory Refuge (Moku o Loe)
7 Marine Life Conservation Districts in Hawaii Island Protected area Acres Year estab. Use Protection from fishing Oahu Hanauma Bay High High Pupukea * Mod Mod Waikiki High High Moku o Loe Low High Hawaii Kealakekua Bay High Mod Lapakahi Low Low Waialea Bay Low Low Old Kona Airport Mod Mod Wai Opae Mod High Lanai Manele-Hulopoe Mod Mod Maui Molokini Shoal High High Honolua-Mokuleia Bay Mod High Friedlander and Brown 2003 * Modified 2003
8 Using Maps to Support Field Activities Random stratified design inside vs. outside MPAs coral, other hard bottom, macroalgae, sand Fish Censuses 25m x 5m transects Corresponding habitat metrics biotic cover abiotic cover habitat complexity 5m 25m
9 Waikiki Benthic Habitats, MPAs, and Sampling Locations (N = 99) Waikiki MLCD Est. 1988, 31 Ha Waikiki-DH FMA Rotational closure since Ha
10 Fish biomass (t ha-1) by individual transects (N=99) for Waikiki including Waikiki MLCD and Waikiki-Diamondhead FMA.
11 Mean biomass (t ha -1 ) by habitat type and management regime for Waikiki Error bars are standard error of the mean F 5,68 = 5.10 P < MLCD>FMA=Open MLCD FMA Open Biomass (t/ha) CHB Macro UCH Sand
12 Fish Biomass (t ha -1 ) by Habitat among Management Regimes A A Open FMA MLCD Biomass ( t ha -1 ) B CHB B B UCH B B MAC B A B SAND B A FMA Open MLCD
13 Factors Influencing Fish Biomass (t ha -1 ) among all Locations Stepwise multiple regression analysis (hardbottom only) Probability to enter model 0.25, probability to leave 0.10, R 2 = 0.41 Sand (-) Macroalgae (-) Protected-open Rugosity Patial R 2
14 Comparison of Fish Biomass between MPAs and Open Areas Across Range of Habitat Complexities Ln biomass (t ha -1 ) MPA Open F 3,21 = P < 0.001, R 2 = 0.78 LS Means Intercept (p < 0.05) MPA Open Habitat Complexity
15 Ratio of biomass (t ha -1 ) inside MPAs vs. outside areas open to fishing Hardbottom habitats only. Waialea Kealakekua Lapakahi Partial protection No-take Manele Old Kona Airport Moku o Loe Waiopae Waikiki Pupukea Honolua Molokini Hanauma Ratio of biomass (t ha -1 ) inside/outside protected area
16 Size spectra of Log 10 number ha -1 by standardized size class (TL in cm) for all fishes on hardbottom habitats FMA 3.0 MLCD Log 10 Number ha F 2,26 = P < LS Means Intercept MLCD = 2.08 A FMA = 1.71 B Open = 1.54 C Open Standardized Size Class (TL in cm)
17 Mean biomass per transect (t ha -1 ) by trophic guild and management regime on hardbottom habitats MLCD FMA Open Biomass (t ha -1 ) A B B A A B A B B Primary Secondary Apex
18 Colonized hardbottom Macroalgae Trophic composition among major habitat types pooled across all locations 0.4 Proportion of total biomass Uncolonized hardbottom Highest biomass of apex on colonized hardbottom Highest proportion on sand Sand important corridor Sand Lowest on macroalgae and uncolonized hardbottom Primary Secondary Apex
19 Sand Depth Fish Species Richness (-) Partial R 2 Fish Biomass Habitat Variables Affecting Fish Assemblage Characteristics in MLCDs Rugosity Results of General Linear Models hardbottom only Partial R 2
20 Major Conclusions Habitat complexity explained much of the variability in fish species richness, biomass, and diversity Overall fish biomass was 2.6 times greater in reserves Apex predators showed the greatest difference between open and protected areas Depth explained most variability in richness and diversity among reserves Overall size of the adult fish assemblage larger in the protected areas & larger size classes had greater number of individuals compared with the other management regimes Despite their proven success, no-take reserves account for less than 1% of nearshore areas in the main Hawaiian Islands
21 Design Criteria for Effective MPAs in Hawaii Range of habitat complexities Full protection from fishing Shoreline to deep habitats Mosaic of habitats Sand corridors Low macroalgae cover Representative wave exposures
22 Spatial Management is the Best Solution for Fisheries and Biodiversity in Hawaii Conventional fisheries management not well suited for Hawaii or other tropical insular areas Catch/effort restrictions not effective in non-commercial fisheries Closed seasons not effective with non-selective gear Gear restrictions difficult to enforce Size limits (little enforcement, lack of compliance, non-selective gear) Large fish contribute more and higher quality offspring High endemism = biodiversity hotspot Ecosystem management = spatial management
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