1 / 52

Glenn Ledder University of Nebraska-Lincoln math.unl/~gledder1

The Past, Present, and Future of Endangered Whale Populations : An Introduction to Mathematical Modeling in Ecology. Glenn Ledder University of Nebraska-Lincoln http://www.math.unl.edu/~gledder1 gledder@math.unl.edu. Supported by NSF grant DUE 0536508. Outline. Mathematical Modeling

andreap
Download Presentation

Glenn Ledder University of Nebraska-Lincoln math.unl/~gledder1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Past, Present, and Future of Endangered Whale Populations: An Introduction to Mathematical Modeling in Ecology Glenn Ledder University of Nebraska-Lincoln http://www.math.unl.edu/~gledder1 gledder@math.unl.edu Supported by NSF grant DUE 0536508

  2. Outline • Mathematical Modeling • What is a mathematical model? • The modeling process • A Resource Management Model • The general plan for the model • Details of growth and harvesting • Analysis of the model • Application to whale populations

  3. (1A) Mathematical Model Math Problem Input Data Output Data Key Question: What is the relationship between input and output data?

  4. Rankings in Sports Mathematical Algorithm Game Data Ranking Weight Factors Game Data: determined by circumstances Weight Factors: chosen by design

  5. Rankings in Sports Mathematical Algorithm Game Data Ranking Weight Factors Model Analysis: For a given set of game data, how does the ranking depend on theweight factors?

  6. Endangered Species Fixed Parameters Mathematical Model Future Population Control Parameters Model Analysis: For a given set of fixed parameters, how does the future population depend on the control parameters?

  7. Models and Modeling A mathematical model is a mathematical object based on a real situation and created in the hope that its mathematical behavior resembles the real behavior.

  8. Models and Modeling A mathematical model is a mathematical object based on a real situation and created in the hope that its mathematical behavior resembles the real behavior. Mathematical modeling is the art/science of creating, analyzing, validating, and interpreting mathematical models.

  9. (1B) Mathematical Modeling Real World approximation Conceptual Model derivation Mathematical Model validation analysis

  10. (1B) Mathematical Modeling Real World approximation Conceptual Model derivation Mathematical Model validation analysis A mathematical model represents a simplified view of the real world.

  11. (1B) Mathematical Modeling Real World approximation Conceptual Model derivation Mathematical Model validation analysis A mathematical model represents a simplified view of the real world. Models should not be used without validation!

  12. Example: Mars Rover Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Newtonian physics

  13. Example: Mars Rover Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Newtonian physics • Validation by many experiments

  14. Example: Mars Rover Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Newtonian physics • Validation by many experiments • Result: • Safe landing

  15. Example: Financial Markets Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Financial and credit markets are independent • Financial institutions are all independent

  16. Example: Financial Markets Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Financial and credit markets are independent • Financial institutions are all independent • Analysis: • Isolated failures and acceptable risk

  17. Example: Financial Markets Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Financial and credit markets are independent • Financial institutions are all independent • Analysis: • Isolated failures and acceptable risk • Validation??

  18. Example: Financial Markets Real World approximation Conceptual Model derivation Mathematical Model validation analysis • Conceptual Model: • Financial and credit markets are independent • Financial institutions are all independent • Analysis: • Isolated failures and acceptable risk • Validation?? • Result: Oops!!

  19. Forecasting the 2012 Election • Polls use conceptual models • What fraction of people in each age group vote? • Are cell phone users “different” from landline users? • and so on

  20. Forecasting the 2012 Election • Polls use conceptual models • What fraction of people in each age group vote? • Are cell phone users “different” from landline users? • and so on • http://www.fivethirtyeight.com (NY Times?) • Uses data from most polls • Corrects for prior pollster results • Corrects for errors in pollster conceptual models

  21. Forecasting the 2012 Election • Polls use conceptual models • What fraction of people in each age group vote? • Are cell phone users “different” from landline users? • and so on • http://www.fivethirtyeight.com (NY Times?) • Uses data from most polls • Corrects for prior pollster results • Corrects for errors in pollster conceptual models • Validation?? • Very accurate in 2008 • Less accurate for 2012 primaries, but still pretty good

  22. (2) Resource Management • Why have natural resources, such as whales or bison, been depleted so quickly? • How can we restore natural resources? • How should we manage natural resources?

  23. (2A) General Biological Resource Model Let X be the biomass of resources. Let T be the time. Let C be the (fixed) number of consumers. Let F(X) be the resource growth rate. Let G(X) be the consumption per consumer. Overall rate of increase = growth rate – consumption rate

  24. (2B) • Logistic growth • Fixed environment capacity Relative growth rate R K

  25. Holling type 3 consumption • Saturation and alternative resource

  26. The Dimensional Model Overall rate of increase = growth rate – consumption rate

  27. The Dimensional Model Overall rate of increase = growth rate – consumption rate This model has 4 parameters—a lot for analysis! Nondimensionalization reduces the number of parameters.

  28. The Dimensional Model Overall rate of increase = growth rate – consumption rate This model has 4 parameters—a lot for analysis! Nondimensionalization reduces the number of parameters. X/A is a dimensionless population; RT is a dimensionless time.

  29. The Dimensional Model Overall rate of increase = growth rate – consumption rate This model has 4 parameters—a lot for analysis! Nondimensionalization reduces the number of parameters. X/A is a dimensionless population; RT is a dimensionless time.

  30. Dimensionless Version

  31. Dimensionless Version k represents the environmental capacity. c represents the number of consumers.

  32. Dimensionless Version k represents the environmental capacity. c represents the number of consumers. (Decreasing A increases both k and c.)

  33. (2C)

  34. (2C) The resource increases

  35. (2C) The resource increases The resource decreases

  36. A “Textbook” Example Line above curve: Population increases

  37. A “Textbook” Example Line above curve: Population increases Low consumption – high resource level

  38. A “Textbook” Example Curve above line: Population decreases

  39. A “Textbook” Example Curve above line: Population decreases High consumption – low resource level

  40. A “Textbook” Example Modest consumption – two possible resource levels

  41. A “Textbook” Example Population stays low if x<2 (curve above line) Modest consumption – two possible resource levels

  42. A “Textbook” Example Population becomes large if x>2 (line above curve) Modest consumption – two possible resource levels

  43. (2D) Whale Conservation • Can we use our general resource model for whale conservation?

  44. (2D) Whale Conservation • Can we use our general resource model for whale conservation? • Issues: • Model assumes fixed consumer population.

  45. (2D) Whale Conservation • Can we use our general resource model for whale conservation? • Issues: • Model assumes fixed consumer population. • We’ll look at distinct stages.

  46. (2D) Whale Conservation • Can we use our general resource model for whale conservation? • Issues: • Model assumes fixed consumer population. • We’ll look at distinct stages. • Model assumes harvesting with uniform technology.

  47. (2D) Whale Conservation • Can we use our general resource model for whale conservation? • Issues: • Model assumes fixed consumer population. • We’ll look at distinct stages. • Model assumes harvesting with uniform technology. • Advanced technology should strengthen the effects found in the model.

  48. Stage 1 – natural balance

  49. Stage 2 – depletion Consumption increases to high level.

  50. Stage 3 – inadequate correction Consumption decreases to modest level.

More Related