1. Lecture 11 review: Stock synthesis models “Synthesis model” is a term coined by Methot for what had been called “statistical catch at age” (SCA) models; examples are SS2, CASAL Basic idea is to use age-structured model to generate predictions of multiple types of observations –Catch for multiple fleets with different age selectivities –Length and age composition of catch –Multiple abundance trend indices Basic aim is to reconstruct historical changes in stock size and recruitment Main limitation is bad trend index data and complex temporal change in size-age selection patterns

2. Parameter estimation and state reconstruction for dynamic models State dynamics Model N Observation Model (predicted y) Statistical criterion Data (observed y) y N N t+1 =N t -C t y t =qN t Parameter N o Parameter q Log-likelihood function Parameters Process errors Observation errors

3. Lecture 12 topics: dangerous quick fixes in fisheries management Several simplistic solutions to management problems are defended with religious fervor by their proponents –Stock enhancement –Marine protected areas –Individual Vessel Quotas Simplistic solutions derive from simplistic models (eg. produce more  catch more) Must take a systems view to understand why these solutions fail

4. Taking a systems view These three subsystems are dynamically linked: mess with any one of them, and there will be (sometimes pathological) responses in the other two. Ignore such responses at your peril. Fishers and Other Stakeholders Management (Assessment, Regulation) Fish stocks, Ecosystem

5. Hatchery programs: the biggest single threat to sustainable fisheries? Two types of hatcheries –Production (meet growing demand by producing more fish) –Conservation (breeding programs for the culls, so stupid as not to merit discussion) Based on concept that protected rearing can radically increase egg-juvenile survival rates, often by several orders of magnitude (e.g. from 3% to 80% for pink and chum salmon) Huge growth in hatchery capability (species, efficiency) and capacity in last two decades, particularly in the North Pacific.

6. Huge growth in hatchery capacity: North Pacific salmon From NPAFC 2007 Doc. 1060 (Malbec model report )

7. Hatchery programs: the biggest single threat to sustainable fisheries? Negative impacts on wild stocks –Competition with wild juveniles leading to depressed juvenile survival –Transmission of diseases, attraction of predators –Fishing effort responses where gear takes both types of fish –Genetic impacts: low fitness of hatchery x wild crosses means hatchery fish in the wild can act like sterile male releases But there are situations where hatcheries have produced very large net benefits –Systems with no wild spawning (e.g. BC lakes) –“Put and Take” fisheries near population centers

8. When wild juvenile production already “fills” available habitat, total catch may not increase at all

9. Remedies for the dark side of enhancement programs Problem –Poor survival after release –Competition with wild juveniles –Disease and predator impacts –Attract fishing pressure –Mate with wild fish (poor reproductive performance in wild) –Declining hatchery performance over time Remedy –Increase release size, acclimate at release sites –Increase release size to avoid nursery area overlaps –Prophylaxis, predator control programs –Selective fisheries (marking, location) –Two extreme options: use only wild spawners, or pure captive brood stock –No known remedy, mechanisms not understood

10. How scientists contribute to misunderstandings about enhancement Myopic performance measures that are convenient to study (e.g. survival rates) Failure to assess competition and exploitation rate effects on wild stocks Focus on sexy topics (genetics) rather than the more important but difficult to study ones (disease, wild stock effects, effort responses) Value laden analysis and communication: natural fish are intrinsically “better”, need for wild fish for risk management (diverse production portfolio)

11. Marine Protected Areas: substituting mindless protectionism for effective management Main arguments for MPAs –Increased fishery yield in cases where of management fails to constrain efforts, especially on less valued stocks/species, through “spillover” effects –Protecting 20% of area will insure spawning stocks at least 20% of unfished levels (Bohnsack) –Protection of habitat from damage by fishing activities (especially trawling) –Places where fauna can live undisturbed (basically an animal rights argument) –Reference areas for assessing unfished abundances, longevity, etc.

12. Marine Protected areas: the reality Fish move, so protecting 20% of the habitat typically protects much less than 20% of the stock; full protection achieved only near center of very large MPAs Severe overfishing outside MPAs likely results in inadeqate larval seeding everywhere, including inside the MPAs (do not achieve natural abundance in them unless they are largely self- seeding, in which case they do not provide large “spillover” benefits anyway Wishful thinking: networks of reserves will lead to “connectivity” where the reserves seed each other with larvae

13. Marine Protected areas: the reality Empirical studies show abundances 2-4 x higher in reserves than outside (Halpern), but proponents of MPAs do not like to mention that most MPAs are sited in areas of higher abundance in the first place (this is dishonest science at its worst) Collateral damage to non-target stocks and habitat often very restricted in space/time; exceptions like big trawled areas are treated as “typical”

14. Marine protected areas: complex ecosystem responses (McClanahan 2007) Studies like this are revealing fairly strong top- down effects of large predator recovery on smaller species: it is not obvious that reserves even increase “biodiversity” in the long run

15. Marine protected areas: the SLOSS debate Population and ecosystem models clearly indicate that SL (Single Large) is necessary to obtain any protection at all for more mobile species But it is clearly much easier from a social, economic, and political perspective to implement SS (Several Small) So the latest science advocacy game is to pretend that SS works, based on experience with coral reef MPAs

16. Marine protected areas: where and when are they actually needed? When there is no way to control efforts, and effort will remain high even at very low stock sizes (high prices, low cost, availability of productive species) When there is source-sink metapopulation structure: should protect source or nursery areas that provide recruits to many other areas that do not self-seed When there is unacceptable damage to habitat or nontarget species with high existence value

17. New modeling approaches are offering guidance about optimal mosaics of protected areas EDOM predicts long term responses of multiple populations, estimates optimum spatial distribution of fishing effort MARXAN uses GIS information to identify areas of high value by stakeholders with conflicting interests (fishing, protection) and to seek optimal spatial patterns ECOSPACE evaluates ecosystem-scale effects of alternative MPA proposals

18. Example: optimized effort distributions from EDOM Optimum effort when arbitrary closed areas (Plan A) are imposed includes high efforts just at MPA boundaries, but much more even effort distribution absent such arbitrary closures; the optimized effort distribution for Plan A achieves about 93% of the economic value that could be achieved without MPAs.

19. IVQs and TURFs are the first step in “getting the incentives right” for fishermen to cooperate with scientists and managers to find sustainable management solutions But just because we must have them doesn’t mean that they will work right The main pitfalls are –Setting the wrong Quota leads to depensatory fishing mortality rate –Changes in fisher behavior when fishing with IVQ make historical cpue trend data unusable (must have surveys, direct U assessments, which cost a lot) –Concentration of ownership: the little guys always lose

20. Error propagation in stock size estimates makes quota management very dangerous There is a fairly high correlation between stock size estimation error in year t+1 and the error in year t, i.e. errors persist (if overestimate this year, will do so next year) for several years (from Walters 2004 CJFAS 61:1061-1065)

21. How do you identify system-scale (“indirect effect”) problems that may make some quick-fix dangerous Keep your eyes open (think more broadly) Keep your ears open (listen to warnings from people who have been thinking about things that might go wrong; it is the raving loonie you want to listen to most closely, not your trusted colleagues) Look closely at the things you might want to treat as constant “parameters” in models (survival rates, fishing effort, etc) Identify things you can control, and also things you can’t