1. Computer modelling ecosystem processes and change Lesson 8 Presentation 1

2. Suggested reading Harris, M. 1998. Lament for an ocean. The collapse of the Atlantic cod fishery. McClelland and Stewart Inc., Toronto. Chapter 5 documents the errors made in the inventory and calculation of cod populations

3. Why computers Ecosystems are complex Can vary the attributes and processes in complex ways with computers

4. To manage our activities Assess management actions across many spatial scales Predict long term effects Understand management on biodiversity Predict influences on specific components of ecosystem (e.g. climate, biological legacies) Predict population dynamics of wide range of species Compare to natural change Complex task

5. What types of models Many different types of models to project population trends, demographics, service & product supply, nutrient and energy fluxes in space and time Models used to understand interactions (e.g. researchers to explain results) Models used to predict future condition (e.g. used by managers to predict future condition of resources)

6. Three types of models: Deterministic (single outcome) Probabilistic (chance) Process (based on ecosystem process)

7. Deterministic Most common Based on rules that show how some change occurs E.g. tree growth based on data from permanent plots. We know how the dia. and ht changed over time. We use this info to grow a tree of the same species by changing dia and ht. with time.

8. Probabilistic Second most common Uses probabilities of chance events to show changes E.g. probability of fire in a forest landscape, what proportion of the forest will burn in a given time period

9. Process Least common Requires a lot of data and information to develop Uses ecological processes to show how attributes in an ecosystem will change. E.g. uses sunlight period, nutrient & moisture flux to grow a plant. Models today are a hybrid of these 3 types

10. Basic components of models Attributes of interest Logic how attributes change Change in attributes of interest Inventory or start population Projected inventory or population

11. Limits: Data used Is the inventory/population correct Garbage in = garbage out Basic rule but often overlooked E.g cod stocks: population estimates were wrong Forest inventories in Ontario: no requirement to quantifying accuracy

12. How to overcome limit Monitor and update inventory Recognize limit of monitoring or inventory method, develop verification methods E.g. creel and index netting for fish population Aerial photos and ground surveys for forest inventory

13. Limits: Logic used Incorrect rules result in incorrect outputs E.g. incorrect growth rate of trees will result in incorrect volume growth of timber Incorrect growth rate of prey population will result in incorrect growth rate for population of predators

14. How to overcome limit Know what the precision levels are for the rules used Use sensitivity analyses to determine how they affect outputs

15. Sensitivity analyses Finding out how sensitive the outputs are to variation in the rule set Small changes in some rules may result in huge changes in outputs. E.g. succession rules in how forest stand composition changes make a large difference in timber volume for each tree species

16. What about processes we do not know? We will never know everything about ecosystems Need validation studies Does the real changes on time match the computer output Need studies in ecosystem processes to discover key factors currently not known Use Adaptive Management Cod stocks

17. Adaptive management Based on social science and ecosystem knowledge Implementing policy or practice as experiments Involves systematic monitoring to detect surprise (what we do not know) Integrated assessment to develop knowledge system

18. Contrast Adaptive Management Most policy uses casual observation no system to capture new information from current practice and use it. Conventional experimentation results in new theory but applicability may be narrow Trial & error: may result in new knowledge but hit and miss

19. Adaptive management cycle New knowledge Apply operationally as experiment Hypotheses Monitor results

20. Questions