Ecosim* overview for NEMoW *and spawn of Ecosim: related dynamic models including Ecospace

1. Ecosim* overview for NEMoW*and spawn of Ecosim: related dynamic models including Ecospace Sarah Gaichas and Kerim Aydin, AFSC Chris Harvey, NWFSC John Field, SWFSC Frank Parrish, PIFSC Clay Porch, SEFSC Howard Townsend, NCBO

2. What is/has/will the model be used for? • Describing ecosystems and improving understanding of how simultaneous physical, ecological, and fisheries interactions affect commercial and bycatch species • Examining apex predator (and or protected species) carrying capacity and predicting responses to changing fishing and primary production • Examining ecosystem effects of • changing water quality • changing fishing gear • different MPA scenarios • Evaluating tradeoffs between management strategies • Providing foundation for developing proposals to integrate ecosystem-based management approaches into current management regimes

3. Has the model been published in the peer reviewed literature? Yes. Early version: Walters, C., Christensen, V., and Pauly, D. 1997. Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Rev. Fish. Biol. Fish. 7: 139-172. Most recent version with “multistanza” age structure: Christensen, V., and C. Walters, 2004. Ecopath with Ecosim: methods, capabilities, and limitations. Ecological Modelling 172: 109-139. Ecospace (also covered in Christensen & Walters 04): Walters, C., Pauly, D., and Christensen, V. 1999. Ecospace: prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems, 2: 539–554.

4. Static food web to dynamic simulation requires functional response + age structured population dynamics ?

5. Hypothetical MPA coverage Where bottom trawling occurs Ecospace: sim in space • Traceable spatial features in grid space • habitats, fleets, ports, management areas, advection fields, seasonal migrations, etc. Habitats (depth, substrate) Columbia River Blanco Mendocino

6. Biomass dynamics equations • For Biomass of group i, dBi /dt= GEi∑preyQ(BiBprey) consumption gain - FiBifishing loss - M0iBiother mortality loss - ∑predQ(BpredBi) predation loss +I immigration rate

7. Picturing the “foraging arena” (Walters et al 1997) “It’s cold down there!” Single “vulnerability” parameter X ~ 2v/aBj ratio V dVij /dt= vij(Bi-Vij) - vijVij - aijVijBj B-V Predator Bj Vulnerable prey Vij aijVijBj Assume fast equilibrium for Vij vij (Bi-Vij) vijVij Sophisticated functional response behavior ranges from stable donor-controlled to chaotic Lotka-Volterra Unavailable prey Bi - Vij

8. Gulf of Alaska (GOA) simulation Low vulnerability versus High vulnerability Biomass (t/km2) Simulation year

9. The full consumption equation: complex functional response Qt = alinkvlinkBpredBpreyTpredTprey /Dpred vlink + vlinkTprey + alinkBpredTpred / Dpred Where Dpred = hpredTpred 1 + ∑pred’sprey alinkBpreyTprey

10. Vulnerability: how much prey biomass is available to predators? Foraging Time: if I’m hungry, should I spend more time vulnerable? Handling Time: at some point, my consumption is limited even if there are more prey Functional response parameters “It’s cold down there!” V B-V “Our food is up there, but so are those big guys!” “Don’t worry, I’m still chewing.”

11. Ecospace equations and assumptions • Same biomass dynamics equation as Ecosim, except with coordinates x, y to designate location on map grid, and movement terms take on greater importance: • Growth efficiency, predation, mortality are now spatially explicit (habitat quality, abundance of other spp., fishing, etc.) • Instantaneous movement mi,x,y reflects organism’s ability to discern fitness trade-offs between x,y and surrounding cells dBi,x,y/dt = GEiSprey Q(Bi,x,yBprey,x,y) consumption gain - Fi,x,yBi,x,yfishing loss - M0iBi,x,yother mortality loss - Spred Q(Bpred,x,yBi,x,y) predation loss + Ii,x,yimmigration gain - mi,x,yBi,x,yemigration loss

12. Ecospace equations and assumptions • Fishing mortality by fleet k over all N cells of system is equal to N · Fk • For each model time step, that mortality is distributed spatially by assigning a weight G to each cell c: Gkc = Okc · Ukc · Si pkiqkiBic / Ckc Okc = status of fleet k in cell c (0=closed, 1=open) Ukc = ability of fleet k to fish in cell c habitat type (0, 1) pki = price fleet k receives for species i qki = catchability of species i in fleet k Bic = biomass of species i in cell c Ckc = cost for fleet k to operate in cell c

13. Data requirements I, Ecosim and Ecospace • All food web parameters from Ecopath, plus • Growth information for age structured groups • General habitat preferences • Dispersal and/or migratory characteristics • Time series to “drive” trajectories for some groups • Single species F, and/or Gear specific effort with bycatch • Primary Production or other group production/recruitment, B • Port locations, habitats where fishing occurs • For ecosystem map • Habitat distribution, including land • Advection patterns • 1° production patterns (can use Sea Around Us data) • Location of management zones (statistical areas, MPAs, etc.)

14. Data requirements II, calibration/fitting • Time series to “fit” (by estimating functional response Vulnerability) • B most common • Species total catch, recruitment • Values for functional response parameters Foraging time, Handling time • Alternatively, estimate these parameters* (see next slides) • Also, include time series of diet data to estimate functional response* • Known species interactions modeled as “mediation functions” • Not available in current version of Ecosim

15. What key data gaps have been identified? • Many regions missing time series of primary production • Time series that are NOT model output already • Mid TL forage fish and low TL zooplankton group dynamics are key low data interactions in many systems • Often, high TL unexploited predator dynamics (killer whales, seals) are unknown and influential • Nobody really knows functional response parameters Are these data gaps informing monitoring efforts? • Strategic data collections implemented from model gaps at PIFSC • NCBO can inform, but still need money approved • Much other data collection still opportunistic

16. Experience: equilibrium + uninformative data + vulnerability estimation in the GOA* • Today’s rules (path equilibruim) can’t recreate yesterday’s GOA. Species and or ecosystem production was different historically. • Supports both climate and fishing-related hypotheses for change, but with different predator prey relationships implied by estimated vulnerability parameters *Analyses in Sim alternative

17. * * * * Different drivers for different species* Key: Lower AIC is better overall fit; Each species fit varies by model Best fit Extinct OK fit

18. Fitted Vuls in each model*

19. Likelihood and AIC for all models* 250 estimated vul parameters

20. Even more functional response parameters* Data-free simulation testing using randomly sampled Vul, Ftime, and Htime (324 parameters) gave a wide range of alternative GOA ecosystems

21. The art part: Pick your poison Too many parameters, not enough data. Options: • The Walters bias: Fix many parameters, fit only vulnerabilities (in blocks), assume systematic residuals are “primary production anomaly.” • The Aydin bias: Group by predator and prey, fit all functional response parameters, assume systematic residuals are difference between start state being “in equilibrium” and the true equilibrium (initial spin-up to “true fitted” equilibrium). • Many other “biases” are possible, and possibly reasonable. Best practice would require more formal evaluation of these hypotheses within a statistical framework. Current EwE software allows only the first hypothesis, “manual adjustment” may be used to achieve the second.

22. Model improvement: Ecosim equations (re-written) where B* and Q* are biomass and consumption in a reference year (1991) Parameters fit using likelihood criteria to available time series, parallel search algorithms coded in C.

23. Whole guilds may move from equilibrium

24. What are the strengths of this model? • Ecosim is freely available, large user community • Improved understanding of data systems (multiple agency, multiple scale data assimilation) • Functional response parameterization is very flexible, much more advanced than many published forms • Simulates a wide variety of fishing scenarios, including spatial management in Ecospace • Simulates changes in production regimes • Ability to represent age structure for many groups • Biomass dynamics of whole ecosystem considered, see both direct effects and side effects of scenarios

25. What are the weaknesses of this model? • Functional response: • In some cases, results sensitive to (difficult to estimate) functional response parameters • Full functional response flexibility means more parameters to estimate than data available • Model weakness or data weakness??? • True of many stock assessments… • EwE statistical estimation of vulnerability only; manual adjustment of other parameters during calibration difficult to repeat if not well documented • Inability to estimate uncertainty in projections (Sim) • In big models, sensitivity analysis for all parameters is an overwhelming (but necessary) task • Ecospace relatively untested, few published examples

26. What remains for model development/improvement/enhancement? • More users for Ecospace, comparisons with Atlantis, etc. • Improved data (but when isn’t that the case?) • More rigorous • documentation of parameter estimation process in many applications (e.g. “manual adjustment” vs. statistical fitting) • statistical parameter estimation ability, including fitting to time varying diet composition data • estimation of uncertainty • Direct comparison of outputs with alternative models • Improved compatibility with complementary models • High resolution ocean circulation models • Fishery interaction and management system models • Age structured stock assessments