1. Spatial fisheries management in practice: an example

2. “Optimal” spatial management Recall though experiment: A fisherman “owns” the ocean. How would he harvest spatially to maximize NPV of profits? On reserve design: –Many reserve design planning processes are on- going –None are informed by the kind of modeling we can provide –As fisheries move towards private access (zoning, dedicated access, property rights…) this will be THE critical question

3. Some approaches people use The “Marxan” approach –Layer maps, score things, define “targets”, and select parcels –Ignores spatial connections which is the whole reason reserves can increase profits The “Collaborative” approach –Policy guy draws lines –Fishermen draw lines –Come to some agreement

4. Costello/Polasky F3 paper N exhaustive “patches” In each patch: –Cost function for harvesting –Growth function –Survival function (or percentage) Connectivity matrix (kernels) connecting all patches Dynamic optimization model derives optimal harvest in each patch over time

5. The result… Reserves – only optimal under some conditions (heterogeneity and connectivity) –Patch-specific inputs and connectivity matrix will determine whether reserves are optimal for a given applications Outside Reserves – model also derives optimal harvest (by patch over time) in all “fished” patches If reserves don’t emerge –Model derives optimal spatial management

6. A paper idea 1. Flesh out the interdependencies among these characteristics 2. Assemble the appropriate data layers for a real (sort of) system 3. Actually determine how harvest should be distributed across space (including reserves) 4. In principle, could compare optimal spatial management from this model with alternative designs (e.g. reserves sited by habitat only)

7. A Channel Islands Example? 1. Defining “patches” (on the order of 100 or 1000 or so would be fine) 2. Patch connectivity between patches (“Flow”) 3. Biological layers for each patch (“Fish”) – e.g. for urchin 4. Economic layers for each patch (“Fishing”) The result would be an optimal spatial management plan to maximize profits

8. Channel Islands with grid

10. Urchin habitat suitability

13. Kelp abundance

16. Fishing cost in each grid cell

18. Defining “patches” We desire a repeatable, transparent method for defining patches So far, we have: –Habitat suitability, kelp abundance, cost Define a “patch” as a set of grid cells: 1.Homogeneous biology (kelp and HSI) 2.Homogeneous economics (cost) 3.Spatially connected Wrote Matlab code to filter 1 & 2, use graph theory to accomplish 3

22. Here’s what we’ll need Define patches In each patch, derive: –Stock/Recruit relationships –Survival relationships –Economics variables Connectivity matrix across all patches Then run Costello/Polasky to derive optimal spatial management Simulate system over time using optimal spatial rule to derive biological and economic outcomes