1. What is Population Ecology? 1

2. Ecology is...  the study of interactions among organisms with each other and with their environment 2

3. 3 Ecology studies these interactions at different levels, called levels of organization.

4. 4 Level of Organization DefinitionExample Speciesindividuals that can breed with one another Populationall the individuals of the same species in an area Communityall the different species in an area

5. 5 Level of Organization DefinitionExample Ecosystem the community plus the physical factors in an area Biome large area that has a particular climate and particular species of plants and animals Biospherethe part of the earth that supports life

6. So....Population Ecology is 6  the study of how the population sizes of species living together in groups change over time and space

7. Population Characteristics  There are three characteristics that all populations have: 1) population density, 2) spatial distribution, 3) and growth rate. 7

8. 1. Population Density 8  Refers to the number of individuals in relation to the space Population Density =

9. 1. Population Density 9  Example: What is the density of a rabbit population of 200 living in a 5 km 2 range?  Solution: Population Density =

10. 1. Population Density 10  Example: What is the density of a rabbit population of 200 living in a 5 km 2 range?  Solution: Population Density =

11. 1. Population Density 11  Example: What is the density of a rabbit population of 200 living in a 5 km 2 range?  Solution: Population Density = Population Density = 40 rabbits / km 2

12. 1. Population Density 12  Density – dependent factors – factors that can affect a population growth because of its density. E.g. Food supply, competition for mates, spread of disease.  Density- independent factors – factors that can affect a population growth regardless of its density. E.g. Forest fires, Flood, Habitat destruction, Pollution

13. 2. Spatial Distribution 13  Refers to the pattern of spacing of a population within an area  3 types: clumped, uniformly spaced, and random

14. 2. Spatial Distribution 14  Results from dispersion – the spreading of organisms from one area to another Three types of barriers prevent dispersal:  Physical barriers (e.g. mountains, oceans)  Ecological barriers (e.g. grassland animals do not move into forests)  Behavioural barriers (e.g. birds that prefer tall trees over short trees will not disperse into short trees)

15. 3. Growth Rate 15  Refers to how fast a population grows 4 factors determine how a population changes:  Natality (birth rate)  Mortality (death rate)  Immigration (individuals moving into a population)  Emigration (individuals moving out of a population)

16. 3. Growth Rate 16  Population change can be calculated as: Birth rate – death rate + immigration - emigration

17. 3. Growth Rate 17  Example: In a given year, a pack of timber wolves experiences the birth of 3 pups and the death of a lone wolf. No animals moved into the pack but 1 wolf did leave.  Solution: Birth rate – death rate + immigration - emigration

18. 3. Growth Rate 18  Example: In a given year, a pack of timber wolves experiences the birth of 3 pups and the death of a lone wolf. No animals moved into the pack but 1 wolf did leave.  Solution: Birth rate – death rate + immigration - emigration = 3 – 1 + 0 – 1

19. 3. Growth Rate 19  Example: In a given year, a pack of timber wolves experiences the birth of 3 pups and the death of a lone wolf. No animals moved into the pack but 1 wolf did leave.  Solution: Birth rate – death rate + immigration - emigration = 3 – 1 + 0 – 1 = 1 Meaning that in this year, the wolf pack experienced a growth of 1 wolf.

20. 20 How do know how many organisms make up a population?

21. 21 Yes, we count them!

22. 22 But what about…?

23. Population Sampling Methods 23  When the number of organisms in a population is hard to count, scientists estimate the total population size  They do this by first sampling the population and then calculating a population size based on the data  There are 3 methods: Mark-Recapture, Quadrat, and Random Sampling

24. 1. Mark-Recapture Sampling  Also called ‘tagging’  A sample of organisms is captured and marked and then returned unharmed to their environment  Over time, the organisms are recaptured and data is collected on how many are captured with marks 24

25. 25

26. 1. Mark-Recapture Sampling 26

27. 1. Mark-Recapture Sampling 27 Example: In order to estimate the population of sturgeon fish in the river, biologists marked 10 sturgeon fish and released them back into the river. The next year, 15 sturgeon were trapped and 3 were found to have marks.

28. 1. Mark-Recapture Sampling 28 Example: In order to estimate the population of sturgeon fish in the river, biologists marked 10 sturgeon fish and released them back into the river. The next year, 15 sturgeon were trapped and 3 were found to have marks.

29. 1. Mark-Recapture Sampling 29 Example: In order to estimate the population of sturgeon fish in the river, biologists marked 10 sturgeon fish and released them back into the river. The next year, 15 sturgeon were trapped and 3 were found to have marks.

30. 1. Mark-Recapture Sampling 30 Example: In order to estimate the population of sturgeon fish in the river, biologists marked 10 sturgeon fish and released them back into the river. The next year, 15 sturgeon were trapped and 3 were found to have marks.

31. 1. Mark-Recapture Sampling  Best for mobile populations, such as fish and birds 31  Problems occur when no marked organisms are captured

32. 2. Quadrat Sampling  A sampling frame (quadrat, usually 1m 2 ) is used to count the individuals in a mathematical area  Population size is then estimated based on the data from the quadrat 32

33. 2. Quadrat Sampling Example: A biologist counts 13 mushrooms in a 1m x 1m quadrat. The area of study is 12m 2. 33

34. 2. Quadrat Sampling Example: A biologist counts 13 mushrooms in a 1m x 1m quadrat. The area of study is 12m 2. 34

35. 2. Quadrat Sampling Example: A biologist counts 13 mushrooms in a 1m x 1m quadrat. The area of study is 12m 2. 35

36. 2. Quadrat Sampling Example: A biologist counts 13 mushrooms in a 1m x 1m quadrat. The area of study is 12m 2. 36

37. Quadrat Sampling  Best for small stationary populations, such as plants & mushrooms 37  Problems occur when the quadrats are placed in ‘abnormal’ locations

38. 3. Random Sampling  The number of organisms within a small area (plot) is counted.  The plots are often placed randomly throughout the sampling area  Population size and density are then estimated based on the plot representation. 38

39. 3. Random Sampling 39 1. Find the average # of individuals in the areas you sampled.

40. 3. Random Sampling 40 1. Find the average # of individuals in the areas you sampled. 2. Multiply the average by the # of plots to find the population estimate

41. 3. Random Sampling 41 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 53 44 1

42. 3. Random Sampling 42 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 1. 53 44 1 per plot

43. 3. Random Sampling 43 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 1. 53 44 1 per plot

44. 3. Random Sampling 44 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 1. 53 44 1 per plot

45. 3. Random Sampling 45 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 2. 53 44 1 per plot

46. 3. Random Sampling 46 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 2. 53 44 1 per plot

47. 3. Random Sampling 47 Example: A sample was taken to count the number of silver maple trees in the forest. The number of trees counted in the grid is shown below. 2. 53 44 1 per plot

48. 3. Random Sampling  Best for large stationary populations, such as trees or coral 48  Problems occur when random sampling is not followed

49. Now it’s your turn… 49