Modelling a Network of Marine Protected Areas for the Central

120 Pages·2002·2.9 MB·English

Modelling a Network of Marine Protected
Areas for the Central Coast of BC

Jeff A. Ardron, Jennifer Lash, Dana Haggarty

v 3.1 July 2002
Box 320 Ph: 250 973-6580
Sointula, BC Fax: [phone]
V0N 3E0 GIS: 250 653-9219
CANADA
A EXECUTIVE SUMMARY 1
B INTRODUCTION 3
B.1 Report Overview 3
B.2 Marine Conservation 4
B.3 Project Goals & Objectives 5
C BACKGROUND 7
C.1 Living Oceans Society 7
C.2 The Central Coast Marine Environment 7
C.3 Culture 9
C.4 Human Impacts 9
C.4.1 Over-Exploitation 9
C.4.2 Habitat loss 10
C.4.3 Water Quality 11
C.4.4 Introduced Species 12
C.4.5 Disease Transfer 12
C.5 Marine Protected Areas 13
C.5.1 Existing Marine Protection 13
C.5.2 MPA Design Principles 13
C.5.3 Definition of a Marine Protected Area 14
C.5.4 Guiding Principles 15
C.6 Site Selection 16
C.6.1 Overview 16
C.6.2 Marxan Software 17
D ASSUMPTIONS AND LIMITATIONS 18
D.1 MPAs as One of Several Useful Tools 18
D.1.1 Assumptions 18
D.1.2 Limitations 19
D.2 Best Available Data 19
D.2.1 Assumptions 19
D.2.2 Limitations 19
D.3 Habitat Representivity 20
D.3.1 Assumptions 20
D.3.2 Limitations 20
D.4 Focal Species 20
D.4.1 Assumptions 21
D.4.2 Limitations 21
D.5 Replication 2
D.5.1 Assumptions 22
D.5.2 Limitations 22
D.6 Distinctivenes 2
D.6.1 Assumptions 22
D.6.2 Limitations 22
D.7 Rarity 23
D.7.1 Assumptions 23
D.7.2 Limitations 23
D.8 Conectivity 24
D.8.1 Assumptions 24
D.8.2 Limitations 24
D.9 Fragmentation 25
D.9.1 Assumptions 25
D.9.2 Limitations 25
D.10 Benthic Complexity 26
D.10.1 Assumptions 26
D.10.2 Limitations 26
D.11 Scale 27
D.11.1 Assumptions 27
D.11.2 Limitations 27
E METHODS 28
E.1 Data Assembly 29
E.1.1 Colection 29
E.1.2 Choosing Common Standards 29
E.2 Data Classifications 32
E.2.1 Geographic & Oceanographic Spatial Hierarchy 33
E.2.2 Regions & Sub-Regions 34
E.3 Physical Features and Enduring Processes 38
E.3.1 Benthic Complexity 38
E.3.2 Depth / Substrate 40
E.3.3 High Curent 45
E.4 Biological Features 46
E.4.1 Salmon Streams 46
E.4.2 Salmon Holding Areas 48
E.4.3 Herring (Clupea pallasii) Spawn 48
E.4.4 Herring holding areas 49
E.4.5 Clams (Shorelines) 50
E.4.6 Kelp (Nereocystis luetkeana and Macrocystis intergrifolia) 51
E.4.7 Birds 52
E.5 Rare and Endangered Features (see also D.7, above) 53
E.5.1 Eulachon (Thaleichthys pacificus) -Estuaries 53
E.5.2 Marbled Murelet (Brachyramphus marmoratus) 54
E.5.3 Sea Oter (Enhydra lutris) 5
E.5.4 Hexactinellid Sponge Reefs 55
E.6 Data Gaps 56
E.7 Representing Data in Planning Units 58
E.7.1 Presence / Absence Areal Data 60
E.7.2 Line and Point Features 60
E.7.3 Examples 61
E.7.4 Exceptions 61
E.8 Setting Up MARXAN 62
E.8.1 Conservation Targets & Penalties 62
E.8.2 Separation Distance, Number, & Clump Size 68
E.8.3 Planning Unit Cost, Boundary Cost, & Boundary Length Modifiers 69
E.8.4 Annealing and Heuristics 70
F ANALYSES & RESULTS 71
F.1 Conservation Hotspots 72
F.1.1 24 Scenarios; 2,400 Solutions 72
F.1.2 Utility 72
F.1.3 Flexible Solutions 73
F.2 SLOSS 75
F.2.1 Plotting Reserve Fragmentation 75
F.2.2 MARXAN Penalties vs. Fragmentation 75
F.3 Conflict 78
F.3.1 Ireconcilable Diferences… 78
F.3.2 A Reasonable Compromise? 78
F.4 Existing BC Marine Parks 80
G DISCUSION 82
G.1 The Model 82
G.2 Implementation 86
G.2.1 Recommendations 86
G.2.2 Creating the Opportunity for MPAs and an Integrated Management Process 89
G.2.3 Implementation to Date 90
H ACKNOWLEDGEMENTS 91
I REFERENCES 92
J APPENDIX 1: GLOSSARY OF SPATIAL TERMS 102
K APPENDIX 2: CLASSIFICATIONS & HIERARCHIES 104
K.1 Existent Classification Systems 104
K.1.1 BC Marine Ecological Classification (MEC) 104
K.1.2 World Wildlife Fund: A National Framework for Marine Conservation 105
K.1.3 Parks Canada: Study to Identify National Marine Conservation Areas 105
K.1.4 Additional Classification Systems 106
K.2 Error in Spatial Hierarchies 106
K.2.1 Cumulative Errors: Boundaries 106
K.2.2 Cumulative Errors: Slivers 107
L APPENDIX 3: BENTHIC COMPLEXITY GIS RECIPE 110
M APPENDIX 4: MAPS OF DATA LAYERS 111

List of Figures
FIGURE 1: CONSERVATION HOTSPOTS 2
FIGURE 2: THE CENTRAL COAST SEA FLOOR 6
FIGURE 3: FLOWCHART OF MPA DESIGN PROCESS 28
FIGURE 4: BENTHIC COMPLEXITY 39
FIGURE 5: EXAMPLE OF BENTHIC COMPLEXITY ANALYSIS 39
FIGURE 6: PLOTTING DEPTH VS AREA 43
FIGURE 7: DISCREPANCIES WITHIN THE PROVINCIAL PHOTIC CLASSIFICATION 44
FIGURE 8: FITTING DATA WITHIN HEXAGONS 59
FIGURE 9: DISTRIBUTION OF DEPTH-SUBSTRATE 67
FIGURE 10: EXAMPLES OF MPA SOLUTIONS 74
FIGURE 11: EFFECT OF THE BOUNDARY LENGTH MODIFIER ON PERIMETER AND
AREA OF RESERVES 76
FIGURE 12: DIMENSIONLESS FRAGMENTATION VS. CLUMPING (BLM) 77
FIGURE 13: PLOTTING PENALTIES (NORMALIZED) VS RESERVE CLUMPING 77
FIGURE 14 A) & B) CONFLICTING PLANNING VALUES, SCENARIOS #1 & #2 79
FIGURE 15 A) & B) EXAMINING HOW PARKS FIT INTO MPA NETWORKS 81
FIGURE 16 EXAMPLE OF HEXAGONS AND FEATURE GRID 103
FIGURE 17: HIERARCHICAL POLYGON ERROR 108
A EXECUTIVE SUMMARY

In 1998, Living Oceans Society embarked upon modelling science-based networks of marine
protected areas for the Central Coast of British Columbia. The results and methodologies presented
in this paper are from three successive refinements of the model. These are the first of their kind in
BC, and comparable to only a handful of others in the world. We have made every attempt to use the
best available data and apply current reserve design theory and algorithms. This project remains on-
going, and details presented here may change. The analysis is current to October 2001, with
document edits to July 2002. The next refinement of the analysis is expected in early 2003.

Presently, there are no fully protected areas in BC’s Central Coast waters. We define a marine
protected area (MPA) as consisting of one or more core no-take areas that should be surrounded by a
buffer zone. In this analysis we have focused on identifying the core no-take areas first.

The following findings and methodologies have emerged:

1. Conservation Hotspots: Regardless of whether MPA networks are small or large, scattered
or clumped, certain areas are identified repeatedly over the course of thousands of solutions.
While these areas alone would not constitute a fully representative MPA network, it is very
likely that without them, such a network would be difficult or impossible to achieve.

2. Physical Complexity: Areas of high physical complexity are believed to harbour greater
species richness, and as such are distinctive and valuable. We developed a unique
methodology to extract a measure of benthic complexity from bathymetric data.

3. SLOSS: (Single Large or Several Small?) We have developed a procedure to inform the
MPA designer of the ecological vs. pragmatic trade-offs involved between many scattered
MPAs and a few large ones.

4. Scale: Unique amongst conservation analyses, we have begun to take into account differing
scales and spatial variability of processes within different regions in our study area.

5. Conflict: Conflicting values, such as biodiversity requirements, declining stocks, and
widespread fishing effort, that would appear irresolvable to a human planner, are also
irresolvable to a computer model. However, modelling does provide candidate networks of
MPAs that can be the basis for discussion in a planning process, where outstanding conflicts
and other issues may be resolved.

6. BC Marine Parks: BC’s marine parks, ecological reserves, and recreation areas, could be
effectively incorporated into several possible networks of MPAs. (Note: There are no
Migratory Bird Sanctuaries or National Wildlife Areas in the Central Coast.) The exception is
Hakai Recreation Area, which owing to its large size and emphasis on recreational use, does
not fit in very well; however, portions such as the Goose Islands could be incorporated.
Figure 1: Conservation Hotspots