Download - ECOLOGICAL METHODS HANDBOOK.pdf

ECOLOGICAL METHODS

HANDBOOK

by

Chris Butler, Ph.D., and David Bass, Ph.D

Biology Department University of Central Oklahoma

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http://biology.uco.edu/personalpages/cbutler/BIO3551/Ecological_Methods_Lab_Manual.pdf

Table of Contents Syllabus

………………………………………………………………………………………… 3

Week 1

………………………………………………………………………………………… 6

Week 2

………………………………………………………………………………………… 14

Week 3

………………………………………………………………………………………… 24

Week 4

………………………………………………………………………………………… 30

Week 5

………………………………………………………………………………………… 50

Week 6

………………………………………………………………………………………… 68

Week 7

………………………………………………………………………………………… 81

Week 8

………………………………………………………………………………………… 82

Week 9

………………………………………………………………………………………… 83

Week 10

………………………………………………………………………………………… 93

Week 11

………………………………………………………………………………………… 96

Week 12

………………………………………………………………………………………… 101

Week 13

………………………………………………………………………………………… 123

Week 14

………………………………………………………………………………………… 143

Week 15

………………………………………………………………………………………… 144

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BIO 3551 – Ecological Methods

Description

This course introduces students to field, laboratory, and computer-based methods in ecology. It includes the study of abiotic and biotic components of terrestrial and aquatic ecosystems. This course emphasizes common methods used in modern ecological studies of terrestrial and aquatic environments. It consists of three hours of laboratory per week, and many exercises will involve field trips. For the “Central Six”, this class will advance discipline knowledge, problem solving (by engaging in research) and health and wellness (by engaging in field biology outside). Prerequisite(s): BIO 1204, 1225, 3543 and STAT 2103 all with a minimum grade of “C”.

Objectives

1. Learn ecological methods used to collect data in laboratory and field studies.

2. Investigate local terrestrial and aquatic environments through the proper collection of data.

3. Demonstrate the proper use of laboratory and field equipment. 4. Analyze data collected during class exercises. 5. Report results of the analyses.

Instructor

Dr. Chris Butler 301-G Howell Hall x5782 [email protected]

Class meets

Wednesdays 1:00-3:50 PM. Note that many classes will meet in the field – the instructor will let you know one week in advance where we will meet. Be sure to wear appropriate attire for the field.

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Required Reading

• Butler, C and D. Bass. 2011. Ecological Methods Handbook. Available from http://www.biology.uco.edu/personalpages/CButler/BIO3551/Ecological_Methods_Lab_Manual.pdf.

Assignments and Grading

Attendance: 50 pts Mid-Term Exam: 200 pts Final: 200 pts Assignments: 440 pts Total: 900 pts

Course Calendar Week 1 – Introduction, Plant walk Week 2 – Aquatic sampling Week 3 – Comparison of lentic vs. lotic systems Week 4 – Quadrat-based community sampling in a riparian community Week 5 – Quadrat-based community sampling in a cross-timbers community Week 6 – Quarter-based community sampling Week 7 – Mid-term Exam Week 8 – FALL BREAK Week 9 – Terrestrial invertebrates Week 10 – Mammals Week 11 – Birds Week 12 – Ecobeaker – Isle Royale Week 13 – Ecobeaker - Keystone Week 14 – THANKSGIVING BREAK Week 15 – Review Week 16 – Final exam Additional Information

ECOLOG-L listserv

The Ecological Society of America has a listserv devoted to the discussion of ecological issues as well as posting ecology-related research opportunities. What is a listserv? It's basically a loose network of e-mail users interested in a particular subject, in this case ecology. Once you are subscribed (it's free) you will be able to send a ecology-related question,

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observation, or message to a single address and it will quickly and automatically be delivered to all other subscribers to the list. Any replies to your message will also be delivered to all subscribers. To subscribe, google “ECOLOG-L”. The first option (https://listserv.umd.edu/archives/ecolog-l.html) will take you to the archives where you will see a link, “Join or leave the list (or change settings)”. Clicking on the link will take you to a page where you can subscribe to ECOLOG-L. You may also unsubscribe at any time.

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https://listserv.umd.edu/archives/ecolog-l.html

Name:

Week 1 - Plant Walk Due at the end of class

Common Name Field marks Sketch 1.

2.

3.

4.

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6.

7.

8.

9.

7


11.

12.

13.

14.

8


16.

17.

18.

19.

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Ecological Methods Plant Guide

American Sycamore Platanus occidentalis

American Sycamore Platanus occidentalis

Eastern Redcedar Juniperus virginiana

Eastern Redbud Cercis canadensis

Pecan Carya illinoinensis

Pecan Carya illinoinensis

Slippery Elm Ulmus rubra

Black Locust Robinia pseudoacacia

White Mulberry Morus alba

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Sugarberry Celtis laevigata

Trumpet Creeper Campsis radicans

Trumpet Creeper Campsis radicans

Honeylocust Gleditsia triacanthos

Honeylocust Gleditsia triacanthos

Ragweed Ambrosia sp.

Greenbrier Smilax sp.

Virginia Creeper Parthenocissus quinquefolia

Sandbar Willow Salix interior

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Eastern Poison Ivy Toxicodendron radicans

Chinkapin Oak Quercus muehlenbergii

Baldcypress Taxodium distichum

Grape sp. Vitis sp.

Blackjack Oak Quercus marilandica

Post Oak Quercus stellata

Eastern Cottonwood Populus deltoides

Black Walnut Juglans nigra

Black Walnut Juglans nigra

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Common Hackberry Celtis occidentalis

American Elm Ulmus americana

Silktree (Mimosa) Albizia julibrissin

Roughleaf Dogwood Cornus drummondii

Red Mulberry Morus rubra

Gum Bully (Chittamwood) Sideroxylon lanuginosum

Green Ash Fraxinus pennsylvanica

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Lab 2 – Aquatic Sampling

COMMON WATER SOURCES IN OKLAHOMA

To understand the ecology of an area or region one must be familiar with the water sources of the area. Rivers not only provide a ready source of water for the surrounding biota but may also represent a barrier to dispersal. Large reservoirs may also influence the local climates of an area as well as alter the life forms of the water course from which they were constructed. In Oklahoma there are approximately 500 named rivers and creeks, many of them short and intermittent during most of the year. There are several large streams like the Arkansas, Red, Washita, Cimarron, North Canadian, Canadian, Grand, and Verdigris, however, that carry millions of cubic feet of water through the state each year. All of northern Oklahoma and much of the central part of the state is in the drainage basin of the Arkansas River. The remainder of Oklahoma is in the drainage basin of Red River. Both are long streams, the source of the Arkansas being in the Rocky Mountains of Colorado and that of the Red on the High Plains of Texas. Parts of both rivers were used in defining the Boundaries of the Adams-Onis Treaty of 1819, which set definite limits of Spanish and American territory. Except for the rivers flowing from the Ozark Plateau or the Ouachita Mountains, the streams of Oklahoma flow in a general eastward direction. The chief tributaries of the Arkansas are the Cimarron and Canadian from the west, the Verdigris, Grand, and Illinois from the north and northeast, and the Poteau from the south. The North Canadian, one of the longest streams in the state, is the chief tributary of the Canadian. Principal tributaries of the Red River are the North Fork, Washita, Blue, Boggy, and Kiamichi. Rivers have been of primary importance in the development of Oklahoma. The Canadian, Arkansas, North Canadian, and many others formed parts of the boundaries of the Indian nations. Some served as routes of travel for early trails and others as sites for the location of pioneer settlements. A few appear in historical records under different names. The Cimarron has been known as the Red Fork of the Arkansas. The Little River of Cleveland and Pottawatomie counties were called the Cedar River by the Seminoles. The river now commonly called the North Canadian has been known as the Rio Nutrio and as the North Fork of the Canadian. The name North Canadian is often applied to Beaver Creek in the Panhandle, but the earliest maps show the North Fork of the Canadian to be formed by the confluence of Beaver and Wolf Creeks. The river now commonly called the South Canadian in Oklahoma is referred to the New Mexico and Texas, as well as in most government publications, as the Canadian. Gaines Creek, in Pittsburg County, now a part of the Eufaula Reservoir, was known as the South Canadian by the early Indian settlers. All of the large lakes in Oklahoma are man-made. They have been developed for such purposes as flood control, conservation, navigation, irrigation, recreation, production of hydroelectric power, and municipal water supply. In addition to these large reservoirs, many stock ponds also exist.

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Physicochemical Analysis Dissolved Oxygen (YSI Model 55 DO Meter) 1. Turn on meter, leaving probe in the damp compartment. 2. Allow meter to remain on for about 5 minutes before calibrating meter. 3. To calibrate meter, press and release both the Up Arrow and Down Arrow simultaneously. 4. After calibration is complete, place probe in sample to read both temperature and dissolved oxygen

concentration. pH (using digital meter) 1. Remove cap and immerse probe into sample. 2. Turn on switch and swirl meter for about 10 seconds, allowing reading to stabilize. 3. Record value and turn off switch. Specific Conductivity 1. Connect probe to meter. 2. Rinse the probe with distilled water. 3. Switch the meter on. 4. Immerse the probe in the water sample. 5. Read the conductivity from the meter in micromhos / cm. 6. Record the value and turn off the meter. Plankton Net 1. Tow net through the water at desired sampling depth or pour water from that depth through net. 2. Record distance towed or volume of water poured through the net. 3. Rinse contents of the net into a jar and preserve the sample. Petite Ponar Bottom Grab 1. Lock open the jaws of the Ponar grab. 2. Lower the Ponar grab to the bottom substrate. 3. As you pull upward the jaws close and a 6" x 6" sample is taken. 4. Empty the sample into a sieve bucket and wash away the silt. 5. Carefully transfer the sample to an appropriate container. 6. Preserve and label the sample. Dip Net 1. Sweep the net through the water three times. Make sure to disturb the substrate to a depth of one inch 2. Empty the sample to a sieve bucket by turning the net inside out in the bucket. 3. Carefully transfer the sample to an appropriate container. 4. Preserve and label the sample.

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1. Complete the following table (physicochemical conditions). Parameter Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

Depth (m)

Water Temperature (oC)

Dissolved Oxygen (mg/l)

pH

Conductivity

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2. Construct a species list of benthic macroinvertebrates (Morpho)Species Number Method

3. Which method resulted in the greatest number of morphospecies?

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4.) The Shannon-Weiner index is calculated as

𝐻ʹ = −�𝑝𝑖ln (𝑝𝑖)

For example, imagine a hypothetical scenario Organisms # found pi ln(pi) pi*ln(pi)Orthetrum 3 0.166666667 -1.791759469 -0.298626578Somatochlora 1 0.055555556 -2.890371758 -0.160576209Corxidae 2 0.111111111 -2.197224577 -0.244136064Enonchis 2 0.111111111 -2.197224577 -0.244136064Peltodytes 7 0.388888889 -0.944461609 -0.367290626Chrionomi 1 0.055555556 -2.890371758 -0.160576209Tonypodinae 2 0.111111111 -2.197224577 -0.244136064

18 -1.719477814H' 1.719477814

The Shanon-Weiner index for this site is 1.72. Calculate the Shannon-weiner Index for your aquatic samples. 5.) Simpson’s Diversity Index is calculated as 1 – D where 𝐷 = ∑𝑛(𝑛−1)

𝑁(𝑛−1) and where n = the total number of organisms of a species while N = the total

number of organisms for all species. For example, imagine a hypothetical scenario Organisms n n(n-1)Water boatman 1 0Hempitera 1 0Water strider 2 2Bivalvia 8 56Gastropoda 3 6Shiner 5 20Sunfish 1 0Sum 21 84 So D = 84

21(20) = 0.2

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And Simpson’s Diversity Index = 1 – D = 0.8. Calculate Simpson’s Diversity Index for your aquatic samples.

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Aquatic Sampling Equipment Know the names and how to use this equipment

Conductivity meter

Dip net

Seine

Dissolved oxygen meter

Mechanical flowmeter

Stream drift net

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Magnifying loupe

pH meter

Plankton net

Ponar grab

Spectrometer

Kick net

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Organisms from Arcadia Lake Site

Phylum: Arthropoda Class: Insecta Order: Diptera Midge larva

Phylum: Arthropoda Class: Insecta Order: Diptera Midge larva

Phylum: Arthropoda Class: Insecta Order: Hemiptera Water boatman

Phylum: Arthropoda Class: Insecta Order: Coleoptera Water scavenger beetle

Phylum: Arthropoda Class: Insecta Order: Coleoptera Beetle sp.

Phylum: Arthropoda Class: Insecta Order: Coleoptera Crawling water beetle

Phylum: Arthropoda Class: Insecta Order: Odonata Dragonfly nymph

Phylum: Arthropoda Class: Insecta Order: Odonata Dragonfly nymph

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Organisms from Chisholm Creek Site

Phylum: Arthropoda Class: Insecta Order: Coleoptera Predaceous Diving Beetle

Phylum: Arthropoda Class: Insecta Order: Ephemeroptera Mayfly nymph

Phylum: Arthropoda Class: Insecta Order: Hemiptera Water strider

Phylum: Arthropoda Class: Insecta Order: Hemiptera Toad bug

Phylum: Mollusca Class: Gastropoda Order: Pulmonata Pond Snail

Phylum: Mollusca Class: Gastropoda Order: Pulmonata Snail sp.

Phylum: Mollusca Class: Bivalvia Order: Veneroida Asiatic clam

Phylum: Chordata Class: Actinopterygii Order: Perciformes Sunfish

Phylum: Chordata Class: Actinopterygii Order: Cypriniformes Shiner

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Lab 3 – Comparison of Lentic and Lotic Systems

Lentic systems are still-water systems such as ponds, lakes, wetlands, etc. Lotic systems are flowing-water systems such as streams, rivers, springs, etc. For this assignment, you are going to compare the two systems using the data you gathered this week and last week. Before you begin, copy your values from last week into the tables below.

1.) Does the amount of dissolved oxygen differ between lentic and lotic systems? Use a t-test to answer this question (cite appropriate statistics!) and create a figure showing means and standard errors for dissolved oxygen.

a. Appropriate statistics include t-value, df, and p-value) i. Your sentence will say something like, “Dissolved oxygen was significantly higher

in the lotic system (t = ____, df = ______, p = ______; Fig. 1).” 1. Note that it is only appropriate to say that there is a significant

difference if your p-value is less than 0.05. ii. Directions for performing a t-test in Excel can be found at

http://www.excel-easy.com/examples/t-test.html b. To create a bar graph with standard error bars, you will first need to calculate mean and

standard error. i. Directions for calculating descriptive statistics (including mean and standard

error) can be found at http://www.excel-easy.com/examples/descriptive-statistics.html

c. Directions on creating a bar graph with custom error bars can be found at http://nathanbrixius.wordpress.com/2013/02/11/adding-error-bars-to-charts-in-excel-2013/

i. Note that you will be using the standard error values you calculated above, rather than the 95% confidence intervals in the example.

2.) Does the pH differ between lentic and lotic systems? Use a t-test to answer this question (cite appropriate statistics!) and create a figure showing means and standard errors

3.) Does the conductivity differ between lentic and lotic systems? Use a t-test to answer this question (cite appropriate statistics!) and create a figure showing means and standard errors

4.) Does the species diversity differ between the two systems? Calculate the Shannon-Weiner index for each system to answer this question.

5.) Does the species diversity differ between the two systems? Calculate Simpson’s index for each system to answer this question.

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http://www.excel-easy.com/examples/t-test.html

http://www.excel-easy.com/examples/descriptive-statistics.html

http://nathanbrixius.wordpress.com/2013/02/11/adding-error-bars-to-charts-in-excel-2013/

http://nathanbrixius.wordpress.com/2013/02/11/adding-error-bars-to-charts-in-excel-2013/

Lake Arcadia Parameter Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

Depth (m)



pH

Conductivity

Chisholm Creek Parameter Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

Depth (m)



pH

Conductivity

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Lake Arcadia (Morpho)Species Number Method

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Chisholm Creek (Morpho)Species Number Method

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Lentic (from wikipedia.org)

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Lotic (from wikipedia.org)

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Lab 4 – Quadrat-based Community Sampling in a Riparian Community

One of the most commonly used methods of vegetation sampling is the quadrat method. This method involves recording all plants within square or rectangular plots in a stand and results in a quantitative description of vegetation for that stand. There are two ways that quadrats may be located. One is by superimposing a grid on a map of the site, numbering each section of the grid, and randomly selecting sections to be sampled. The other method, often used by experienced field workers, is to subjectively place quadrats in areas that are felt to best represent, and cover the variation within, the study area. Because of time limitations, we will use the latter method. It is important that each individual quadrat be fairly homogeneous in its composition. The preferred quadrat size varies from one region to another, with a 1/40 acre (33 feet by 33 feet) or a hectare (10,000m2) plot being commonly used in temperate zone forests, and much smaller plots (1m2 or 0.25m2) being used in grasslands or prairies. Circular quadrats are also used. Materials Tape measurers Compass Data sheets Calculators Field Methods We will establish several square quadrats, each 10m x 10m (100m2 or 0.01 hectare), in a forest. The class will be divided into teams, each being responsible for collecting data for one or two quadrats. Measure out 10m using a tape measure to serve as a side of the square. Then use the tape to measure the remaining three sides. Mark the corners and edges of your quadrat with stakes or flags. Record relevant information about the quadrat, including the quadrat location and number, sampling date, and investigators. Briefly describe the topography and landscape, including the slope, aspect, and location relative to streams. Soil depth, drainage, texture, soil type, soil pH, and parent material should also be described, but we will not deal with those during this lab period. Any evidence of disturbance should also be noted, including logging, fire, or grazing, as well as old fences and roads, tree falls, or obvious diseases of dominant species. Record all tree species for each quadrat on the data sheet (Table 1). One person in each group should record data in the field for that group and share it with the rest of the group after returning to the laboratory. Measure the DBH (circumference) for each individual tree and record it. To be counted as a tree, an individual plant

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should have a circumference of 5cm and stand >2m tall. For this exercise, we will use a standard tape measurer and record the circumference (in cm) on the data sheet. If your tape measure is in inches, multiply by 2.54 to obtain the circumference in centimeters. There are several things to keep in mind as you collect your data. If a tree is dead, do not measure and record its circumference -- simply ignore it. If a tree exists on a boundary, count it only if more than one-half of the trunk lies within your quadrat. If a tree has two or more main trunks, measure the circumference of each trunk separately, sum those values, and count them as one tree. Take all measurements carefully and accurately! Calculations A) Convert DBH (on Table 1 data sheet) to basal area in cm2 for each tree. Use the equation: Basal area = Pi x (0.5 x DBH)2 Total the basal area covered by all trees of each species to obtain a total basal area per species. B) Each member of each team should calculate the following for each tree species (list by scientific name) for

the quadrat they sampled and list those results in Table 2. Be sure to show your work as done in the example!

1) Density = Number of trees 2) Total basal area (totaled in as above) 3) Relative density = No. of trees of one species / Total no. of trees of all species 4) Relative dominance = Total basal area of one species / Total basal area of all species 5) Importance value = (Relative density + Relative dominance) / 2 C) We will pool the data from all quadrats, calculate the following for each tree species, and record those

values on Table 3. 1) Number of quadrats of occurrence 2) Density = Number of trees 3) Total basal area

4) Relative frequency = No. quadrats of occurrence for a species / Total no. of quadrats of occurrence for all species

5) Relative density = No. of trees of a species / Total no. of trees of all species 6) Relative dominance = Total basal area of a species / Total basal area of all species 7) Importance value = (Relative frequency + Relative density + Relative dominance) / 3

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8) Total number of trees of all species combined per hectare (a hectare is 10,000m2) 9) Total basal area all species combined per hectare

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Table 1 - Transect Method Field Data (#1)

Location ________________________________________ Date _______________________________

Number of saplings in Quadrat ______________ Number of seedlings in Quadrat ______________

Members of group ___________________________________________________________________________

Number Species DBH Basal Area Coverage

(Circumference)

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


(Circumference)

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


(Circumference)

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Table 2 - Summary of Community Analysis of Single Quadrat by the Quadrat Sampling Technique (#1)

Location ________________________________________ Date _______________________________

Quadrat Number ______________ Quadrat Size ______________

Members of group ___________________________________________________________________________

Species Density Total Basal

Area Covered

Relative Density (D) Relative Dominance (Do) Importance

(D + Do) / 2

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Area Covered


(D + Do) / 2

Totals

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


Area Covered


(D + Do) / 2

Species Density Total Basal Relative Density (D) Relative Dominance (Do) Importance

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Area Covered (D + Do) / 2

Totals

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


Area Covered


(D + Do) / 2

Species Density Total Basal Relative Density (D) Relative Dominance (Do) Importance

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Area Covered (D + Do) / 2

Totals

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Table 3 - Summary of Community Analysis of All Quadrats by the Quadrat Sampling Technique

Location ___________________________ Date _____________________________

Number of Quadrats _____ Quadrat Size __________

Species Frequency

(No. of quadrats

of occurrence)

Density

(No. of

trees)

Total Basal

Area Covered

Relative

Frequency (F)

Relative

Density

(D)

Relative

Dominance

Importance

(F+D+Do)

3

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Species Frequency

(No. of quadrats

of occurrence)

Density

(No. of

trees)

Total Basal

Area Covered

Relative

Frequency (F)

Relative

Density

(D)

Relative

Dominance

Importance

(F+D+Do)

3

Totals

Average no. trees per quadrat Average Basal area per quadrat

Total no. trees / Total no. quadrats = Total basal area / Total no. quadrats =

Total no. trees per hectare = Total basal area per hectare =

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Questions

Do species with the highest relative densities also have the highest relative frequency and relative dominance values?

Do the species with the highest importance values contain saplings? What does this indicate about the future of this forest?

When comparing quadrats, evaluate whether the dominant species, or those with the highest importance values, are the same or different.

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How similar are the quadrats to one another in species composition?

How do relative dominance and relative density values compare for different species?

Are there differences between the seedling or sapling numbers in different quadrats?

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Plant Sampling Equipment Know the names and how to use this equipment

Compass

Dbh tape

Tape measure

Note that dbh (diameter at breast height) is sampled 1.4 m above the ground.

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Lab 5 – Quadrat-based Community Sampling – a Comparison of Riparian and Cross Timbers Communities

The term “Riparian” refers to the area adjacent to a river or stream. These areas are typically dominated by water-loving plants . Riparian areas are important for a number of reasons, not least of which is that they act as filters to reduce soil runoff into water bodies. The term “Cross Timbers” refers to the oak (predominantly Blackjack Oak Q. marilandica and Post Oak Q. stellata) woodlands that extend from Texas into extreme southeastern Kansas. This area was historically intermixed with tall-grass prairie.

(Image from the EPA)

Due to fire suppression, Eastern Redcedar is invading much of the Cross Timbers. In today’s lab, you will use the techniques that you learned last week (quadrat-based community sampling) to compare species diversity between riparian and Cross Timbers areas.

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


(Circumference)

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


(Circumference)

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


(Circumference)

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


Area Covered


(D + Do) / 2

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Area Covered


(D + Do) / 2

Totals

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


Area Covered


(D + Do) / 2

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Area Covered


(D + Do) / 2

Totals

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Location ________________________________________ Date _______________________________


Members of group ___________________________________________________________________________


Area Covered


(D + Do) / 2

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Area Covered


(D + Do) / 2

Totals

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Table 3 - Summary of Community Analysis of All Quadrats by the Quadrat Sampling Technique

Location ___________________________ Date _____________________________

Number of Quadrats _____ Quadrat Size __________

Species Frequency

(No. of quadrats

of occurrence)

Density

(No. of

trees)

Total Basal

Area Covered

Relative

Frequency (F)

Relative

Density

(D)

Relative

Dominance

Importance

(F+D+Do)

3

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Totals

Average no. trees per quadrat Average Basal area per quadrat

Total no. trees / Total no. quadrats = Total basal area / Total no. quadrats =

Total no. trees per hectare = Total basal area per hectare =

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Questions

What species had the highest relative frequency, density and dominance in each habitat (riparian vs. cross-timbers forest)?

Which habitat (riparian or cross-timbers) had the highest species richness?

Which habitat (riparian or cross-timbers) had the highest tree abundance?

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Create a Shannon-weiner and Simpson diversity index for each habitat. Which habitat (riparian or cross-timbers) had the highest diversity?

Calculating species-area curves.

One way to determine whether you have adequately sampled an area is to create a species-area curve. On the x-axis you plot your area, and on the y-axis you plot the total number of species recorded. For example, imagine you recorded the following data:

Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Plot 6

Species A Species A Species B Species A Species A Species A

Species B Species B Species D Species B Species C Species C

Species C Species D Species E Species D

Species E Species F Species E

Species F

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Your graph would then look like this:

A total of six species were detected. Because your figure levels off, you have adequately sampled the area. If the curve doesn’t level off, this indicates that you are still finding new species with each new site you visit and you have not yet adequately sampled the area.

Create species-area curves for each habitat. Did we adequately sample for species richness? How can you tell?

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6

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Lab 6 – Quarter-based Community Sampling

The quarter method is a widely used method of vegetation sampling in forests. It is a distance method, and uses points rather than quadrats or plots. The points can be 1) randomly located on a grid of the area to be sampled, 2) located by a stratified random method, such as at random points along evenly spaced transects, or 3) located systematically along a transect. Because of time constraints, we will use the last method. Materials Tape measures DBH tapes Clipboards Data sheets Methods We will establish several 50m transects through a forest. Each team will sample along one transect. Points will be located at 10m intervals (0, 10, 20, 30, 40 and 50m) along each transect. At each point, establish a line perpendicular to the transect, dividing the area around the point into four "quarters". In each quarter, locate the tree nearest the point that is >5cm DBH (or 15.7cm circumference). On a Table 1 data sheet, record the species, distance from the point to the center of the tree in meters, and DBH (or circumference) in centimeters for each of the four individual trees per point. Continue until all points have been sampled.

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Calculations Sample data Sampling Point Quarter Number Species Distance (m) DBH 0 m 1 Blackjack Oak 1.1 6 2 Post Oak 1.6 48 3 Sugarberry 2.3 15 4 Redbud 3.0 11 10 m 1 Post Oak 2.8 65 2 Sugarberry 3.7 16 3 Blackjack Oak 0.9 8, 6 4 Sugarberry 2.2 9 20 m 1 Blackjack Oak 2.8 4 2 Blackjack Oak 1.1 6 3 Blackjack Oak 3.2 6 4 Blackjack Oak 1.4 5 30 m 1 Redbud 1.3 19 2 Sugarberry 0.8 22 3 Sugarberry 0.7 12 4 Redbud 3.1 7 40 m 1 Blackjack Oak 1.5 7 2 Blackjack Oak 2.4 5 3 Post Oak 3.3 27 4 Post Oak 1.7 36

Total 40.9

You’ll be doing the same calculations as for the previous two labs. In addition, for quarter-based analysis you also need to calculate out

1.) 𝑚𝑒𝑎𝑛 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑠𝑢𝑚 𝑜𝑓 𝑡ℎ𝑒 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑠4𝑛

where n is the number of points sampled

a. So, in this example 𝑚𝑒𝑎𝑛 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 40.94∗5

= 40.920

= 2.05𝑚

2.) Density per hectare. This is given by 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑝𝑒𝑟 ℎ𝑒𝑐𝑡𝑎𝑟𝑒 = 1𝑚𝑒𝑎𝑛 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒

∗ 10,000

a. So, in this example, 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑝𝑒𝑟 ℎ𝑒𝑐𝑡𝑎𝑟𝑒 = 12.05

∗ 10,000 = 2380 𝑡𝑟𝑒𝑒𝑠/ℎ𝑎

And, in case, you need it, here are the formulas from the previous two labs. Convert DBH (on Table 1 data sheet) to basal area in cm2 for each tree. Use the equation: Basal area = Pi x (0.5 DBH)2 Total the basal area covered by all trees of each species to obtain a total basal area per species. Each team should turn in their data sheets to the instructor upon completion. Each student should then obtain a complete set of data from the instructor for all transects and perform the following calculations. A) For each individual species calculate: 1) Frequency = Number of points of occurrence

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2) Density = Number of individuals 3) Total basal area 4) Relative frequency = (No. of points at which a species occurs / Total no. of points of occurrence for all species) x 100 5) Relative density = (No. of individuals of a species / Total no. individuals of all species) x 100 6) Relative dominance = (Total basal area of a species / Total basal area of all species) x 100 7) Importance = (Relative frequency + Relative density + Relative dominance) / 3 Summarize these calculations in Table 2, as shown in the example on the last page of this exercise.

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Table 1 - Data Sheet for Recording Species, Coverage Area, and Point-to-Point Distances in Point-Quarter Sampling (site 1)

Location ___________________________________ Date _________________

Sampling Point Quarter Number Species Distance DBH 0 m

1

2

3

4

10 m 1

2

3

4

20 m 1

2

3

4

30 m 1

2

71

3

4

40 m 1

2

3

4

50 m 1

2

3

4

72


Location ___________________________________ Date _________________


1

2

3

4

10 m 1

2

3

4

20 m 1

2

3

4

30 m 1

2

3

73

4

40 m 1

2

3

4

50 m 1

2

3

4

74


Location ___________________________________ Date _________________


1

2

3

4

10 m 1

2

3

4

20 m 1

2

3

4

30 m 1

2

3

75

4

40 m 1

2

3

4

50 m 1

2

3

4

76

Table 2 - Summary of Community Analysis of All Points by the Quarter Sampling Technique

Location ______________________________________ Date ___________________

Species Frequency

(No. of points

of occurrence)

Density

(No. of

Trees)

Total Basal

Area Covered

Relative

Frequency

(F)

Relative

Density

(D)

Relative

Dominance

(Do)

Importance

(F+D+Do)

3

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Totals

Total distance (m) = Trees per hectare = Average basal area per tree (cm2) =

Mean distance (m) = Total basal area (cm2) = Basal area per hectare (cm2) =

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Questions

Do species with the highest relative densities also have the highest relative frequency and relative dominance values? If not, explain.

When comparing quadrats, evaluate whether the dominant species, or those with the highest importance values, are the same or different between the quadrats.

How similar are the quadrats to one another in species composition?

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How do relative dominance and relative density values compare for different species?

How do these data compare to those obtained by the quadrat?

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Week 7 – MidTerm Exam

Study guide for Ecological Methods Mid-term Exam

Plant walk lab

• Be able to visually identify the 19 species that were covered in the lab • Know the difference between simple and compound leaves • Be familiar with deciduous vs. evergreen

Aquatic ecosystems

• Be able to describe the difference between a lentic and lotic system • Be able to describe the difference between riffles and pools • Be able to identify the organisms captured in class • Be able to identify and use a dissolved oxygen meter, pH meter, conductivity meter • Be able to identify and describe how to use a ponar trap, dipnet, kick net, and plankton net • Be able to calculate the Shannon-Weiner index and the Simpson index • Be able to read the output for t-test results in Excel

Vegetation lab

• Be able to identify all 34 plant species • Be able to use dbh tape, compass, tape measure • Be able to read rarefaction curves • Compare riparian and cross timbers systems

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Week 9 – Fall Break

82

Week 8 – Terrestrial Invertebrates

It is difficult to accurately sample terrestrial invertebrate populations. These beasts occupy many different microhabitats and this requires several collecting techniques to be employed to make obtain suitable samples. All collecting methods are limited and some species will be missed. Therefore, several methods of collecting should be used. By comparing samples of the same kind, useful information, such as population changes and community patterns may be observed. We will sample two areas (prairie and forest) and compare the samples collected from each area. The collecting techniques we will use are rather general and may by modified for specific sampling requirements. Ground Macroinvertebrates 1. Place the 0.1m

2 wire loop on the ground. 2. Carefully remove all contents within the loop, including both litter and detritus, down to a depth of 5cm. 3. Place this sample into a jar, record its number, add preservative, and return it to the lab for sorting, identifying, and counting organisms. 4. Repeat this procedure. 5. Note: An alternative to field preservation is the use of Berlese-Tulgren funnels. Upon returning to the lab, place the sample into the Berlese-Tulgren funnel. After one week, some of the animals will have crawled away from the heat and drying soil, and fallen into the 80% ethanol in the jar below. 6. The animals in the sample may be sorted, identified, and counted. Cryptozoans 1. Drop boards (0.1m2) may be placed to collect animals that prefer cool, moist microhabitats. 2. Each drop board is left undisturbed for at least one week. 3. When sampling, lift each board carefully, but quickly. 4. Collect animals observed on the surface of the soil and leaf debris. 5. Place this sample into a jar, record its number, add preservative, and return it to the lab for sorting, identifying, and counting organisms. Foliage Invertebrates 1. Using an insect net, take 10 steps through the vegetation (do not sweep the same area more than once). 2. Place this sample into a jar, record its number, add preservative, and return it to the lab for sorting, identifying, and counting organisms. 3. Repeat this procedure.

83

Ground Macroinvertebrates (Prairie)

Taxa 1A 3A 5A 7A 9A

No. of individuals

No. of species

Species diversity

84

Ground Macroinvertebrates (Forest)

Taxa 2A 4A 6A 8A 10A

No. of individuals

No. of species

Species diversity

85

Cryptozoans (Prairie)

Taxa 1B 3B 5B 7B 9B

No. of individuals

No. of species

Species diversity

86

Cryptozoans (Forest)

Taxa 2B 4B 6B 8B 10B

No. of individuals

No. of species

Species diversity

87

Foliage Invertebrates (Prairie)

Taxa 1C 3C 5C 7C 9C

No. of individuals

No. of species

Species diversity

88

Foliage Invertebrates (Forest)

Taxa 2C 4C 6C 8C 10C

No. of individuals

No. of species

Species diversity

89

Questions

1. Which area (prairie or forest) contained the most number of individuals?

2. Which area (prairie or forest) contained the most number of species?

3. Which area (prairie or forest) had the highest species diversity value?

4. Which microhabitat contained the most number of individuals?

5. Which microhabitat contained the most number of species?

6. Which microhabitat contained the highest species diversity value?

7. Explain the above results.

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Terrestrial invertebrates list

Phylum Athropoda

Class Insecta

Order Odonata (dragonflies, damselflies)

Order Blattodea (cockroaches)

Order Mantodea (praying mantis)

Order Orthoptera (cricket, grasshopper, katydid)

Order Hemiptera (true bugs)

Order Coleoptera (beetles)

Order Diptera (flies)

Order Lepidoptera (butterflies, moths)

Order Hymenoptera (ants, bees, wasps)

Class Arachnida

Order Araneae (spiders)

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Terrestrial Invertebrate Sampling Equipment Know the names and how to use this equipment

Berlese-Tulgren funnel

Insect net

Wire loop

Drop board

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Week 10 – Mammals

There are several different techniques for studying mammals. The three techniques you will be exposed to in today’s lab include Sherman live mammal traps, plaster tracks and radiotelemetry. Sherman live mammal traps are essentially box traps that are used to catch mammals. They come in a variety of sizes, but are most frequently used to catch small mammals (typically rodents). The traps are usually baited with oats or peanut butter and then left overnight. They are checked in the morning and the mammals that have been caught are either released or sacrificed. Many mammals are too large or infrequent to be easily captured with a Sherman live mammal trap. In this case, an easy way to determine their presence is to look for tracks. Plaster tracks are made using plaster of paris and can be used as references. To make a plaster track, mix two parts of water to one part plaster of paris. Put a plastic circle around the track and pour the resulting mixture into the circle. As the plaster is hardening, push a stick (or small pole) through the plaster, leaving a small hole. (This hole is used to attach a tag to the plaster track.) The plaster should harden within approximately ½ hour. Another common method for studying mammals is using radiotelemetry. A radio tag is attached to the mammal. The researcher will have a Yagi antenna connected to a receiver. The intensity (i.e. “loudness”) of the “beep” that emerges from the receiver will depend upon both distance and direction to the radio tag. If you are pointed at the radio tag and close the “beep” will be relatively loud. If you are further away, the “beep” will be relatively soft. Researchers adjust both the gain and volume on the receiver to help identify the direction to the tag.

Homework

1.) Get plaster of Paris casts from three different mammal species (domestic mammals excluded!) (30 pts)

2.) Describe five methods to determine what mammal species are present in an area (10 pts)

93

Mammal Study Guide For Ecological Methods, you will need to know how to operate the traps and the common names of the following species.

Sherman Live Mammal trap

Hispid Cotton Rat

Sigmodon hispidus

House Mouse Baeolophus bicolor

North American Deermouse Peromyscus maniculatus

Plains Harvest Mouse

Reithrodontomys montanus

Nine-banded Amradillo Dasypus novemcinctus

Virginia Oppossum Didelphis virginiana

North American Porcupine

Erethizon dorsatum

White-tailed Deer

Odocoileus virginianus

Raccoon

Procyon lotor

Gray Fox

Urocyon cinereoargenteus

American Beaver Castor canadensis

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Coyote Canis latrans

Cougar Puma concolor

White-tailed Deer

Odocoileus virginianus

Virginia Oppossum Didelphis virginiana

Raccoon

Procyon lotor

Raccoon

Procyon lotor

Nine-banded Amradillo Dasypus novemcinctus

Cougar

Puma concolor

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Week 11 – Birds

Although most birds are diurnal, they tend to avoid coming in close to people. Consequently, studying birds in the field typically involves the use of binoculars. Binoculars vary in both their magnification and their field of view. Most binoculars will have numbers separated by an “x” on them, such as 10 x 50 or 7 x 42. The first number is the magnification. If the bird is 35 m away, a 10x binocular will make it appear that it is only 3.5 m away, while a 7x binocular will make it appear as though it was 5 m away. The field of view is how much of an area you can see when looking through the binoculars and it is inversely related to magnification. The higher the magnification, the smaller the field of view. Consequently, people who watch small, active birds in forests (such as warblers) tend to use 7x and 8x binoculars. In contrast, people who watch big, distant birds on the beach tend to use 10x binoculars and/or telescopes (called spotting scopes in birding circles).

The second number on the binoculars is the diameter of the objective lens (the big lenses). This affects brightness. The best measure of the binocular’s brightness is the exit pupil diameter. This is found by dividing the diameter of the objective lens by the magnification. So for a 10 x 40 binocular, the exit pupil size is 4 mm. The pupil of the human eye changes in response to light conditions. In bright light, it contracts down to ~2 mm, while in very dim conditions it expands to ~7 mm. Consequently, while a 10 x 40 binocular would be perfectly fine for use on a bright, sunny day, images would appear to be very dark indeed as the sun sets. Astronomers and other people who watch the night sky tend to use 7 x 50 binoculars.

In some cases, it is possible to attract birds by pishing, squeaking or using a soundbox. Pishing involves going “pish… pish… pish…” can be fairly effective at attracting small Passerines. It is speculated that it mimics the alarm call of the Tufted Titmouse (Baeolophus bicolor) and other birds will fly in to see what the fuss is about. Squeaking involves making a high-pitched squeak by kissing the back of your hand. This may also mimic an alarm call and tends to be fairly effective at getting sparrows (and other birds) to appear for a few seconds. A soundbox is created by cupping your two hands together and blowing across the knuckles on your thumb, causing the air inside to resonate. This is a good technique for imitating owls and doves.

Birds can also be studied in the hand. Much of our knowledge of avian migration comes from bird banding, where licensed bird banders attach a metal ring to the leg of a bird. This band has a unique numeric code as well as contact information for the national Bird Banding Laboratory (BBL). Banders submit their records annually to the BBL. The BBL will then inform banders when their birds are re-encountered. Because all native birds are protected by law (the Migratory Bird Treaty Act of 1918), it is necessary to obtain permits to band birds. A common method of catching birds is using mist nets (which

96

superficially resemble volleyball nets). The birds become entangled in the nets and are then carefully removed by the bird bander. Because birds are small and fragile (due in part to their pneumatized bones) it requires great delicacy to safely extract a bird captured in a mist net.

97

Bird Study Guide For Ecological Methods, you will need to know the common names of the following ten bird species.

Blue Jay Cyanocitta cristata

Carolina Chickadee

Poecile carolinensis

Tufted Titmouse

Baeolophus bicolor

Mourning Dove

Zenaida macroura

Eurasian Collared-Dove Streptopelia decaocto

Northern Cardinal Cardinalis cardinalis

98

European Starling

Sturnus vulgaris

Red-tailed Hawk

Buteo jamaicensis

Eastern Screech-Owl

Megascops asio

House Sparrow

Passer domesticus

99

In lab today, you learned how to “pish” and “squeak”. For your homework, visit four locations (separated by at least 100 m). At each location, record the number of birds that you see during two minutes. Then pish for one minutes and record the number of birds that you see during the next two minutes. Finally, squeak for one minute and record the number of birds that you see.

Is there a significant difference in the number of birds you see? Use a one-way ANOVA to test this and create a figure to illustrate the average number of birds detected using each method.

Site Control Pish Squeak

1

2

3

4

100

Week 12 – Ecobeaker – Isle Royale

The Wolves and Moose of Isle Royale

If you were to travel on Route 61 to the farthest reaches of Minnesota and stand on the shore of Lake Superior looking east, on a clear day you would see Isle Royale. This remote, forested island sits isolated and uninhabited 15 miles off of the northern shore of Lake Superior, just south of the border between Canada and the USA. If you had been standing in a similar spot by the lake in the early 1900s, you may have witnessed a small group of hardy, pioneering moose swimming from the mainland across open water, eventually landing on the island. These fortunate moose arrived to find a veritable paradise,

devoid of predators and full of grass, shrubs, and trees to eat. Over the next 30 years, the moose population exploded, reaching several thousand individuals at its peak. The moose paradise didn’t last for long, however.

Lake Superior rarely freezes. In the 1940s, however, conditions were cold and calm enough for an ice bridge to form between the mainland and Isle Royale. A small pack of wolves found the bridge and made the long trek across it to the island. Once on Isle Royale, the hungry wolves found their own paradise — a huge population of moose. The moose had eaten most of the available plant food, and many of them were severely undernourished. These slow-moving, starving moose were easy prey for wolves.

The Isle Royale Natural Experiment The study of moose and wolves on Isle Royale began in 1958 and is thought to be the longest-running study of its kind. The isolation of the island provides conditions for a unique natural experiment to study the predator-prey system. Isle Royale is large enough to support a wolf population, but small enough to allow scientists to keep track of all of the wolves and most of the moose on the island in any given year. Apart from occasionally eating beaver in the summer months, the wolves subsist entirely on a diet of moose. This relative lack of complicating factors on Isle Royale compared to the mainland has made the island a very useful study system for ecologists.

The EcoBeaker® Version of Isle Royale During this lab, you will perform your own experiments to study population dynamics using a computer simulation based on a simplified version of the Isle Royale community. The underlying model includes five species: three plants (grasses, maple trees, and balsam fir trees), moose, and wolves. If you were

© 2010, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved 101

actually watching a large patch of moose-free grass through time, you would observe it slowly transforming into forest. Likewise, the simulated plant community exhibits a simple succession from grasses to trees. While the animal species in the Isle Royale simulation are also simplified compared with their real-world counterparts, their most relevant behaviors are included in the model. Moose prefer to eat grass and fir trees. Wolves eat moose, more easily catching the slower, weaker moose. Each individual animal of both species has a store of fat reserves that decreases as the individual moves around and reproduces, and increases when food is consumed. Both moose and wolves reproduce; however, for simplicity, the simulation ignores gender. Any individual with enough energy simply duplicates itself, passing on a fraction of its energy to its offspring. Death occurs when an individual’s energy level drops too low. Because weaker moose move at slower speeds, they take longer to find food and move away from predators, so their chance of survival is lower than for healthier moose. In the EcoBeaker™ simulation, wolves hunt alone, whereas in the real world, wolves are social animals that hunt in packs. These simplifications make the simulation tractable, while still retaining the basic qualitative nature of how these species interact.

Some Important Terms and Concepts

Population Ecology Population ecology is the study of changes in the size and composition of populations and the factors that cause those changes.

Population Growth Many different factors influence how a population grows. Mathematical models of population growth provide helpful frameworks for understanding the complexity involved, and also (if the models are accurate) for predicting how populations will change through time. The simplest model of population growth considers a situation in which limitations to the population’s growth do not exist (that is, all necessary resources for survival and reproduction are present in continual excess). Under these conditions, the larger a population becomes, the faster it will grow. If each successive generation has more offspring, the more individuals there will be to have even more offspring, and so on. This type of population growth is described with the exponential growth model.

The exponential growth model assumes that a population is increasing at its maximum per capita rate of growth (represented by ‘rmax’) also known as the “intrinsic rate of increase”. If population size is N and time is t, then:

The notation ‘dN/dt’ represents the “instantaneous change” in population size with respect to time. In this context, “instantaneous change” simply means how fast the population is growing or shrinking at any particular instant in time. The equation indicates that at larger values of N (the population size), the rate at which the population size increases will be greater. The following graph depicts an example of exponential population growth. Notice how the curve starts out gradually moving upwards and then becomes steeper over time. This graph illustrates that when the population size is small, it can only increase in size slowly, but as it grows, it can increase more quickly.


Exponential Population Growth

Carrying Capacity In the real world, conditions are generally not so favorable as those assumed for the exponential growth model. Population growth is normally limited by the availability of important resources such as food, nutrients, or space. A population’s carrying capacity (symbolized by ‘K’) is the maximum number of individuals of that species that the local environment can support at any particular time. When a population is small, such as during the early stages of colonization, it may grow exponentially (or nearly so) as described above. As resources start to run out, however, population growth typically slows down and eventually the population size levels off at the population’s carrying capacity.

To incorporate the influence of carrying capacity in projections of population growth rate, ecologists use the logistic growth model. In this model, the per capita growth rate (r) decreases as the population density increases. When the population is at its carrying capacity (i.e., when N = K ) the population will no longer grow. Again, using the ‘dN/dt’ notation, if the maximum per capita rate of growth is rmax, population size is N, time is t, and carrying capacity is K, then:

When the population size (N) is near the carrying capacity (K), K-N will be small and hence, (K-N)/K will also be small. The change in the population size through time (dN/dt) will therefore decrease and approach zero (meaning the population size stops changing) as N gets closer to K. The following graph depicts an example of logistic growth. Notice how it initially looks like the exponential growth graph but then levels off as N (population size) approaches K (carrying capacity).


Logistic Population Growth

While the logistic model is more realistic than the exponential growth model for most populations, many other factors can also influence how populations change in size through time. For example, the growth curve for a recently-introduced species might temporarily overshoot the population’s carrying capacity. This would happen if the abundance of resources encountered by the colonizing individuals stimulated a high rate of reproduction, but the pressures of limited resources were soon felt (i.e., individuals might not start dying off until after a period of rapid reproduction has already taken place). Graphs based on real population data are never such smooth, neat curves as the ones above. Random events almost always cause population sizes and carrying capacities to fluctuate through time. Interactions with other species, such as predators, prey, or competitors, also cause the size of populations to change erratically. To estimate carrying capacity in situations such as these, one generally calculates the median value around which the population size is fluctuating. More Information Links to additional terms and topics relevant to this laboratory can be found in the SimBio Virtual Labs™ Library which is accessible via the program’s interface.

Starting Up

[ 1 ] Read the introductory sections of the workbook, which will help you understand what’s going on in the simulation and answer questions.

[ 2 ] Start SimBio Virtual Labs™ by double-clicking the program icon on your computer or by selecting it from the Start Menu.

[ 3 ] When the program opens, select the Isle Royale lab from the EcoBeaker® suite.

IMPORTANT! Before you continue, make sure you are using the SimBio Virtual Labs version of Isle Royale. The splash screen for SimBio Virtual Labs looks like this:


If the splash screen you see does not look like this, please close the application (EcoBeaker 2.5) and launch SimBio Virtual Labs. When the Isle Royale lab opens, you will see several panels: ––The ISLAND VIEW panel (upper left section) shows a bird’s eye view of northeastern

Isle Royale, which hosts ideal moose habitat. ––The DATA & GRAPHS panel to the right displays a graph of population sizes of

moose and wolves through time. ––The SPECIES LEGEND panel above the graph indicates the species in the simulation; the buttons link to the SimBio Virtual Labs™ Library where you can find more information about each. [ 4 ] Click ‘Moose’ in the SPECIES LEGEND panel to read about moose natural history, and then answer

the following question (you can read about other species too, if you wish) [ 4.1 ] Based on what you find in the Library, answer the following: could a moose swim fast

enough to win a swimming medal in the Olympics (where the fastest speeds are around 5 miles / hour)? Yes No (Circle one)

[ 5 ] Examine the bottom row of buttons on your screen. You will use the CONTROL PANEL buttons to control the simulation and the TOOLS buttons (to the right) to conduct your experiments. These will be explained as you need them; if you become confused, position your mouse over an active button and a ‘tool tip’ will appear.


Exercise 1: The Moose Arrive

In this first exercise, you will study the moose on Isle Royale before the arrival of wolves. The lab simulates the arrival of the group of moose that swam to the island and rapidly reproduced to form a large population.

[ 1 ] Click the GO button in the CONTROL PANEL at the bottom of the screen to begin the simulation. You will see the plants on Isle Royale starting to spread, slowly filling up most of this area of the island.

Grass starts out as the most abundant plant species, but is soon replaced with maple and balsam fir trees. The Isle Royale simulation incorporates simplified vegetation succession to mimic the more complex succession of plant species that occurs in the real world. After about 5 simulated years, the first moose swim over to the island from the mainland and start munching voraciously on the plants.

[ 2 ] You can zoom in or out using the ZOOM LEVEL SELECTOR at the top of the ISLAND VIEW panel. Click different Zoom Level circles to view the action up closer or further away. After watching for a bit, click on the left circle to zoom back out. You can zoom in and out at any time.

[ 3 ] Reset the simulation by clicking the RESET button in the CONTROL PANEL. Confirm that the simulation has been reset by checking that the TIME ELAPSED box to the right of the CONTROL PANEL reads “0 Years”.

[ 4 ] Click the STEP 50 button on the CONTROL PANEL, and the simulation will run for 50 years and automatically stop. Watch the graph to confirm that the size of the moose population changes dramatically when the moose first arrive, and then eventually stabilizes (levels out).

��You can adjust how fast the simulation runs with the SPEED slider to the right of the CONTROL PANEL.

[ 5 ] Once 50 years have passed (model years — not real years!), examine the moose population graph and answer the questions below. (NOTE: if you can’t see the whole graph, use the scroll bar at the bottom of the graph panel to change the field of view.)

[ 5.1 ] What is the approximate size of the stable moose population? ________

[ 5.2 ] What was the (approximate) maximum size the moose population attained? ________ [ 5.3 ] Using the horizontal and vertical axes below, roughly sketch the population size graph showing the simulated moose population changing over time. Label one axis “POPULATION SIZE (N)” and the


other one “TIME (years)”. You do not need to worry about exact numerical values; just try to capture the shape of the line.

[ 5.4 ] Examine your graph and determine the part that corresponds to the moose population

growing exponentially. Draw a circle around that part of the moose population curve you drew above.

[ 5.5 ] The moose population grew fastest when it was:

Smallest Medium-sized Largest (Circle one)

[ 5.6 ] What is the approximate carrying capacity of moose? Draw an arrow on your graph that indicates where the carrying capacity is (label it “K”) and then write your answer in the space below:

[ 6 ] The following logistic growth equation should look familiar (if not, revisit the Introduction):

[ 6.1 ] What does “dN/dt” mean, in words?

[ 6.2 ] Think about what happens to dN/dt in the equation above when the population size (N) approaches the carrying capacity (K)? Think about the case when the two numbers are the same (N = K). Rewrite the right-hand side of the equation above, substituting K for N. Write this new version of the equation below:

dN/dt = _________________ when N=K


[ 6.3 ] Look at the equation you just wrote and figure out what happens to the right-hand side of the equation. Then complete the following sentence by circling the correct choices.

According to the logistic growth equation, when a growing population reaches its carrying capacity (N = K),

dN/dt = 0 / 1 / K / N / rmax (Circle one),

and the population will

grow more rapidly / stop growing / shrink (Circle one)

[ 7 ] Look at the graph that depicts an example of logistic growth and compare that to your moose population growth graph.

[ 7.1 ] Sketch both curves in the spaces provided below. (Don’t worry about the exact numbers; just show the shapes of the curves. Be sure to label the axes!)

[ 7.2 ] How do the shapes of the curves differ? Describe the differences in terms of population sizes and carrying capacities. [7.3 ] Provide a biological explanation for why the moose population overshoots its carrying capacity when moose first colonize Isle Royale. (HINT: consulting the Introduction might help.)

[ 7.4 ] At year 50 or later, with the moose population at its carrying capacity, what would happen if an extra 200 moose suddenly arrived on Isle Royale? How would this change the population graph over the next 20 to 30 years? In the space provided, draw a rough sketch of what you think the graph would look like under these conditions. Be sure to label the axes.


[ 8 ] Now you will test your prediction by increasing the number of moose on the island. Click the ADD

MOOSE button in the TOOLS panel. With the ADD MOOSE button selected, move your mouse to the ISLAND VIEW, click and hold down the mouse to draw a small rectangle. As you draw, a number at the top of the rectangle tells you how many moose will be added. When you release the mouse, the new moose appear inside your rectangle. Add approximately 200-300 moose.

��HINT: To obtain the exact moose population size from the graph, click the graph to see the x and y data values at any point (population size is the y value).

[ 9 ] Click GO to continue running the simulation for 20 to 30 more years and watch what happens to the moose population. Click STOP to pause the simulation. Then answer the following questions:

[ 9.1 ] Did you predict correctly in question 8.3? ________

[ 9.2 ] What is the carrying capacity of moose on Isle Royale after adding 200-300 new moose?

________


Exercise 2: The Wolves Arrive One especially cold and harsh winter in the late 1940s, Lake Superior froze between the mainland and Isle Royale. A small pack of wolves travelled across the ice from Canada and reached the island. In this second exercise, you will investigate how the presence of predators affects the moose population through time.

[ 1 ] To load the next exercise, select “The Wolves Arrive” from the SELECT AN EXERCISE menu at the top of the screen.

[ 2 ] Click STEP 50 to advance the simulation 50 years. You will see moose arrive and run around the island eating plants as before. Next, you will add some wolves to the island, but first answer the following question:

[ 2.1 ] How do you predict the moose population graph will change with predatory wolves in the system? Will the moose population grow or shrink?

[ 3 ] Activate the ADD WOLF button in the TOOLS panel by clicking it. Add 20-40 wolves to Isle Royale by drawing small rectangles on the island (they will fill with wolves) until you have succeeded in helping the wolf population to get established.

[ 4 ] Run the simulation for about 200 years (you can click STEP 50 four or five times). Observe how the moose and wolves interact, and how the population graph changes through time. (To better observe the system you can try changing the simulation speed or zoom level.)

[ 4.1 ] In the space below, copy the moose-wolf population graph starting with the time when wolves were established. Make sure you label the axes.


��NOTE: if you have trouble estimating the wolf population size from the graph, hold down your mouse button and move the pointer along the graph line to see the x and y values represented.

[ 4.2 ] Did the introduction of wolves cause the moose population size to decrease or increase? If so, how much smaller or larger (on average) is the moose population when wolves are present?

[ 4.3 ] You should have noticed that the populations of moose and wolves go through cycles. (If not, run the simulation for another 100 years.) Describe the pattern and provide a biological explanation for what you observe. Does the moose or the wolf population climb first in each cycle? Which population drops first in each cycle?

[ 5 ] If you haven’t already, click STOP.

[ 6 ] The MICROSCOPE tool lets you sample animals to determine their current energy reserves. Activate the MICROSCOPE tool by clicking it. Then click several moose to confirm that you can measure their ‘Fat Stores’. These reserves are important health indicators for moose; the greater a moose’s fat stores, the more likely it will survive the winter and produce healthy, viable offspring.

[ 6.1 ] All else being equal, which do you think would be healthier (on average), moose on an island with wolves or moose on an island without wolves? Explain your reasoning.

[ 7 ] You will now test your prediction. RESET the simulation and then click GO to run the simulation without wolves until the moose population has stabilized at its carrying capacity. Click STOP so you can collect and record data. Decrease your zoom level to see as much of the island as possible. [ 8 ] Randomly select 10 adult moose and use the MICROSCOPE tool to sample their fat stores. Record

your data on the left-hand side of the table below. Do NOT sample baby moose; they are still growing and so do not store fat as adults do.


[ 9 ] When you are done, activate the ADD WOLVES button as before, and add 10-20 wolves. Click GO and run the simulation until the moose and wolf populations have cycled several times. STOP the simulation when the moose population is about midway between a low and high point (i.e. at its approximate average size).

[ 10 ] Randomly select another 10 adult moose and use the MICROSCOPE tool to sample their fat stores.

[ 10.1 ] Record the values on the right-hand side of the table.

[ 10.2 ] Calculate and record the mean fat stores of adult moose with wolves absent and present in the table above. (You can open your computer’s calculator by clicking the CALCULATOR button near the lower right corner of your screen.) Provide a biological explanation for any differences you have observed.


Exercise 3: Changes in the Weather You have probably heard that scientists are concerned about climate change and the effects of global warming due to increasing atmospheric greenhouse gases. Recent evidence suggests that temperatures around the world are rising. In particular, the average yearly temperature in northern temperate regions is

expected to increase significantly. This change will lead to longer, warmer spring and summer seasons in places like Isle Royale. The duration of the growing season for plants will therefore be extended, resulting in more plant food for moose living on the island.

How would a longer growing season affect the moose and wolf populations on Isle Royale? Would they be relatively unaffected? Would the number of moose and wolves both increase indefinitely with higher and higher temperatures, and longer and longer growing seasons?

One way ecologists make predictions about the impacts of global warming is by testing different scenarios using computer models similar to the one you’ve been using in this lab. Even though simulation models are simplifications of the real world, they can be very useful for investigating how things might change in the future. In this exercise, you will use the Isle Royale simulation to investigate how changes in average yearly temperature due to global warming may affect the plant-moose-wolf system on the island.

[ 1 ] Use the SELECT AN EXERCISE menu to launch “Changes in the Weather”.

[ 2 ] Click STEP 50 to advance the simulation 50 years. You can zoom in to view the action up close. The moose population should level out before the simulation stops.

[ 3 ] Activate the ADD WOLF button in the TOOLS panel. Add about 100 wolves by holding down your mouse button and drawing rectangular patches of wolves. Remember to look at the number at the top of the rectangle to determine how many wolves are added.

[ 4 ] Advance the simulation 150 more years by clicking STEP 50 three times. Watch the action. The simulation should stop at Year 200.

[ 4.1 ] Estimate the average and maximum sizes for moose and wolf populations after the wolves have become established. Record these values below:

Maximum moose population size: _________________

Maximum wolf population size: _________________

Average moose population size: _________________


Average wolf population size: _________________

[ 5 ] In the PARAMETERS panel below the ISLAND VIEW you will see “Duration of Growing Season” options where you can select different scenarios. The default is Normal, which serves as your baseline – this is the option you have been using thus far.

––The Short option simulates a decrease in the average annual temperature on Isle Royale. The growing season is shorter than the baseline scenario, which results in annual plant productivity that is about half that of Normal.

––The Long option simulates a warming scenario in which the growing season begins earlier in the spring and extends later in the autumn. Plant productivity is almost double that of Normal.

[ 5.1 ] Predict how moose and wolf population trends will differ with the Short growing season compared to the Normal scenario. Will average population sizes be smaller or larger? Why?

[ 6 ] Without resetting the model, select the ‘Short’ growing season option.

[ 7 ] Advance the simulation another 100 years by clicking STEP 50 twice (total time elapsed should be ~300 years).

[ 7.1 ] Estimate the maximum and average sizes for moose and wolf populations after several cycles with a ‘Short’ growing season. Record these values below:





[ 7.2 ] How do these numbers compare to those you observed with the Normal growing season (Step 4 above)?

[ 8 ] In the Short growing season, the plant growth is half of what it was before.

[ 8.1 ] Based on your measurements, how much do you think the moose carrying capacity changed, and why?

[ 9 ] Now it’s time to consider the warming scenario.


[ 9.1 ] How do you predict that moose and wolf population trends will differ with a Long growing season, and why?

[ 10 ] Without resetting the model, select the ‘Long’ growing season option from the PARAMETERS panel.

[ 11 ] Click GO and monitor the graph as the populations cycle. If you watch for a while you should notice something dramatically different about this scenario, in which the plant productivity is high.

[ 12 ] Click STOP and estimate the maximum and average size for moose and wolf populations under the Long growing season scenario

[ 12.1 ] Record these values below:





[ 12.2 ] If you watched for a while, you probably saw some species go extinct. If you didn’t observe extinctions, you can continue to run the simulation until you see this dramatic phenomenon. Explain why you think extinction is more likely in this scenario than the other two (this is known as the “paradox of enrichment”).

[ 12.3 ] Looking at your results from running the simulation under the normal climate conditions and the two alternative scenarios, were your predictions correct? Provide biological explanations for the trends and differences that you observed. Pay particular attention to how the population cycles changed (e.g., increased, decreased, became less stable) as the rate of plant growth changed.


[ 12.4 ] [Optional] If you have already talked about global warming and climate change in class, provide another example of how increased yearly temperature can affect an animal or plant population. In particular, think about pests, invasive species, disease, or species of agricultural importance.


Extension Exercise: What’s the Difference? In Exercise 2 you conducted an experiment comparing health of moose with wolves absent to health of moose with wolves present. You probably observed at least a small difference between the samples, but does that really indicate that moose have greater fat stores when wolves are present? The difference could be related to wolves, but it could also have arisen simply by chance. You might have accidentally selected very healthy moose one time and unhealthy moose the other. How can you know whether the difference in means between two samples is real?

The short answer is that you can’t. But you can make a good guess using statistics. In fact, “inferential statistics” were invented to allow us to better uncover the truth and answer these sorts of questions. In this section, you will perform a simple statistical test, called a t-test, to decide whether or not the wolves’ presence had a significant effect on moose fat stores. If we were to be very thorough and formal in our t-test lesson, we would include a lengthy discussion of such concepts as random variables, sampling distributions, standard errors, and alpha levels. These are important, but to keep this short, we will just focus on the core ideas underlying the t-test.

You start with a question: Is the mean moose fat stores different when wolves are present versus absent? The null hypothesis is a negative answer: there is no real difference. Under the null hypothesis, the difference in your samples arises from chance. The alternative hypothesis is that there is an effect of wolves on moose fat stores. In order to know which hypothesis your samples support, we examine the difference in means relative to the variability you observed.

[ 1 ] Look back at Exercise 2 where you measured the fat stores of adult moose with wolves absent and present, and record those values here. Note that the subscript ‘p’ represents samples with wolves present, while ‘a’ represents those with wolves absent.

[ 1.1 ] Mean fat stores of adult moose, wolves present ( ) : _________

[ 1.2 ] Mean fat stores of adult moose, wolves absent ( ) : __________

[ 1.3 ] Calculate the difference in mean fat stores ( ) : ___________

[ 2 ] Look at the following three hypothetical graphs. Each graph shows two distributions of moose fat stores, one with wolves present (lighter gray line) and one with wolves absent (darker line). Note that in each graph, mean moose fat stores are represented by dashed vertical lines, and the difference in means is the same for all three. However, the variation in fat stores is smaller in the distributions on the left, and larger in those on the right.


[ 2.1 ] Which of the above graphs (A, B, or C) would make the most convincing argument that the difference in fat stores is real, and not just due to chance?

[ 2.2 ] Explain your choice:

If there is a lot of variability in the data sets you are comparing, you will more likely see a difference in their means just by chance, supporting the null hypothesis. Only if the difference in means is large compared to the amount of variability in the data do you suppose that the difference might be real. A statistic called t formalizes this intuition – in fact, t is calculated as a ratio of ‘difference in means’ to ‘amount of variability’. Here is its formula (with the ‘p’ and ‘a’ subscripts referring to moose energy with wolves present vs. absent):

In the formula above, the mean values of the two samples is given by and . The variability of values within the sampled data sets is incorporated into the denominator, where ‘SE’ stands for the ‘standard error of the sample-mean difference’ (a fancy-sounding phrase for a simple concept: variability). Calculating this value is straightforward but requires a few steps if you are doing it ‘by hand’; the formula is:

Here, varp and vara are the variances for each sample, a measure of the amount of variability in the values. Finally, np and na are the number of samples in each data set. If you have never calculated variance before, don’t fret – this exercise will walk you through the calculation. Combining the two above equations yields the following formula for t:

[ 3 ] Examine the formula for t.

[ 3.1 ] Draw a square around the part of the formula for t that compares the means of the two data sets.

[ 3.2 ] Draw a circle around the part of the formula for t that describes the amount of variability in the data.

[ 3.3 ] If the means are close together, and the variability is high (so that the difference in means could more easily have arisen by chance), will the value of t be low or high?


[ 4 ] You probably noticed a difference in the health of moose when wolves were present versus when

they were absent. To find out whether this difference is large enough to distinguish it from the null hypothesis, you have to calculate the t statistic for your moose fat stores data. Start by estimating the variance in each population (with and without wolves) as follows.

[ 4.1 ] Go back to Section 2 and look at your table of adult moose fat stores. Copy the values from that table into the table below, in the column labeled ‘Fat Store’. (Do this for both samples — with and without wolves.)

[ 4.2 ] Focus first on your samples WITHOUT WOLVES. For each fat store value in that sample, subtract the mean fat store with wolves absent (𝒙�a from step [1.2] above), and enter this ‘difference from the mean’ in the column labeled x - 𝒙�a. Remember you can click the CALCULATOR button near the lower right corner to open your computer’s calculator.

[ 4.3 ] Square each ‘difference from the mean’ and enter the squared value in the column labeled (x - 𝒙�a)2.

[ 4.4 ] Sum the squared differences. Enter the ‘sum of squares’ at the bottom of the table.


[ 4.5 ] Divide the sum of squares by the ‘sample size’ minus 1 (na-1). Here, na is the number of moose whose fat stores you sampled. (Note that ‘sample size’ is different than ‘population size’.) You will use the estimated variance in the t-test:

vara = (sum squared differences)a /(na-1) = __________

[ 4.6 ] Repeat the above steps ([4.2] through [4.5]) to calculate the variance for moose fat stores WITH WOLVES present. Remember this time to use the mean fat store with wolves present ( from step [1.1] above).

varp = (sum squared differences)p /(np-1) = __________

[ 4.7 ] Now that you have calculated variances, plug these values into the equation for the ‘standard error of the sample-mean difference’ to calculate an overall measure of variability in your samples. (And yes, you will divide by the sample sizes again!)

[ 4.8 ] What is the value t of the t-test, given the difference in means and the standard error of the sample-mean difference you calculated above?

The higher the value of t, the more confident you can be that the difference did not result from chance. But how confident are you? A common protocol is to call something ‘significant’ if the probability is less than 0.05 that the difference is due to chance alone. This probability is dubbed the ‘p-value’.

[ 5 ] Given the value of t, and something called the “degrees of freedom” in your data, you can determine the p-value (the probability of the difference occurring by chance) using a statistical table, or, better yet, using SimBio Virtual Lab’s handy-dandy t-test p-value calculator.

[ 5.1 ] The number of degrees of freedom in your t-test is equal to the total number of samples (20 in this case) minus 2. That is, ‘degrees of freedom’=np+na-2. How many degrees of freedom do your moose fat stores data have? __________.

[ 5.2 ] Launch the t-test p-value calculator by clicking the ‘t-test’ button on the TOOLS panel (very bottom right of your screen).


[ 5.3 ] In the dialog that appears, type in your t value and the degrees of freedom, and press the CALCULATE button. What is the probability of the null hypothesis being correct (i.e., that the difference was due to chance alone)?

[ 5.4 ] What can you say about moose fat stores with wolves absent vs. present, after performing the t-test?


Key Publications A few researchers have studied the population dynamics of wolves and moose on Isle Royale for a very long time, resulting in an exceptional continuity in research approach and data collection. The research program is currently directed out of Michigan Tech by John Vucetich and Rolf Peterson, both of whom have published extensively on moose-wolf population dynamics. Below are a few references regarding moose and wolves on Isle Royale, the contribution of Isle Royale studies to broader ecological issues, and the scientific and conservation challenges involved.

Peterson, R.O., & Page, R.E.. 1988. The Rise and Fall of Isle Royale Wolves, 1975-1986. Journal of Mammology, 69: 89-99.

Peterson, R.O. 1995. The Wolves of Isle Royale: A Broken Balance. Willow Creek Press, Minocqua, WI.

Vucetich, J.A., R.O. Peterson, & C.L. Schaefer. 2002. The Effect of Prey and Predator Densities on Wolf Predation. Ecology, 83(11): 3003-3013.

Vucetich, J.A., & R.O. Peterson. 2004. Long-Term Population and Predation Dynamics of Wolves on Isle Royale. In: D. Macdonald & C. Sillero-Zubiri (eds.), Biology and Conservation of Wild Canids, Oxford University Press, pp. 281-292.


Week 13 – Ecobeaker – Keystone

Introduction A diversity of strange-looking creatures makes their home in the tidal pools along the edge of rocky beaches. If you walk out on the rocks at low tide, you’ll see a colorful variety of crusty, slimy, and squishy-looking organisms scuttling along and clinging to rock surfaces. Their inhabitants may not be as glamorous as the megafauna of the Serengeti or the bird life of Borneo, but these “rocky intertidal” areas turn out to be great places to study community

ecology.

An ecological community is a group of species that live together and interact with each other. Some species eat others, some provide shelter for their neighbors, and some compete with each other for food and/or space. These relationships bind a community together and determine the local community structure: the composition and relative abundance of the different types of organisms present. The intertidal community is comprised of organisms living in the area covered by water at high tide and exposed to the air at low tide.

This laboratory is based on a series of famous experiments that were conducted in the 1960’s along the rocky shore of Washington state, in the northwestern United States. Similar intertidal communities occur throughout the Pacific Northwest from Oregon to British Columbia in Canada. The nine species in this laboratory’s simulated rocky intertidal area include three different algae (including one you may have eaten in a Japanese restaurant); three stationary (or “sessile”) filter-feeders; and three mobile consumers.

Ecological communities are complicated, and the rocky intertidal community is no exception. Fortunately, carefully designed experiments can help us tease apart these complexities, providing insight into how communities function. As will become apparent, understanding the factors that govern community structure can have serious implications for management. In this laboratory, you’ll use simulated experiments to elucidate how interactions between species can play a major role in determining community structure. You will apply techniques similar to those used in the original studies, in order to experimentally determine which species in the simulated rocky intertidal are competitively


dominant over which others. You’ll then analyze gut contents and use your data to construct a food web diagram. Finally, you’ll conduct removal experiments, observing how the elimination of particular species influences the rest of the community. When you’ve completed this lab, you should have a greater appreciation for the underlying complexity of communities, and for how the loss of single species can have surprisingly profound impacts.

Food Chains, Food Webs and Trophic Levels You probably know that herbivores eat plants and that predators eat herbivores. The progression of what eats what, from plant to herbivore to predator, is an example of a food chain. Omnivores eat both plants and animals. Within a community, producers, herbivores, predators, and omnivores are linked through their feeding relationships. If you create a diagram that connects different species and food chains together based on these relationships, the result is called a food web diagram.

Ecosystems can also be represented by a pyramid comprising a series of “trophic levels”. A species’ trophic level indicates its relative position in the ecosystem’s food chain. Producers (including algae and green plants) use energy from the sun to produce their own food rather than consuming other organisms, thus they occupy the lowest trophic level. Since herbivores consume the producers, they occupy the next trophic level. Predators eat the herbivores, thus occupying the next higher trophic level. Omnivores occupy multiple trophic levels. The highest level is occupied by top-level predators, which are not eaten by anything (until they die). Generally, but not always, lower trophic levels have more species than do higher levels within a community.

Competition Among community relationships, predation is perhaps the most obvious but certainly not the most important. Two species may also compete with each other for space or food. Stationary organisms in particular must often compete intensively for limited space. When one species is better at obtaining or holding space than another, or is able to displace the second species, the ‘winner’ is said to be competitively dominant. In the same way that you can draw a food web, you can also construct a diagram to illustrate which species are superior competitors within a community, called a competitive dominance hierarchy. In this lab, you will create competition dominancy hierarchy and food web diagrams to help you understand the community structure of the intertidal zone. Dominant versus Keystone Species In many communities, there is one species that is more abundant in number or biomass than any other, often referred to as the dominant species. For example, in a dense, old-growth forest, one type of late successional tree is often the dominant species. As you might imagine, such species greatly affect the nature and composition of the community.

However, a species does not have to be the most abundant to have the greatest impact on the community. Imagine an archway made of stones. The one stone at the top center of the arch supports all the other stones. If you remove that stone, called the “keystone”, the arch crumbles. In some communities, the presence of a single species controls community structure even though that species may have relatively low abundance. These organisms are known as keystone species. An important characteristic of a keystone species is that its decline or removal will drastically alter the structure of the local community. For example, many keystone species are top predators that keep the populations of lower-level consumers in check. If top predators are removed, populations of the lower-level organisms can grow, dramatically changing species diversity and overall community structure, sometimes resulting in the collapse of the entire community.


The EcoBeaker® Model If you’re curious about how the simulated intertidal community works, here’s the basic idea. In EcoBeaker models, each individual belongs to a “species” which is defined by a collection of rules that determine that species’ behavior. For example, species that are mobile consumers follow rules that dictate how far they can move in a time step, what they can eat, how much energy they obtain from their prey, how much energy they use when they move, etc. When an individual consumer’s energy runs out, it dies. Individuals within species all follow the same rules, but because the rules defining species include some random chance (e.g., which direction to turn), you will notice variability in what individuals are doing at any given time. Different species behave differently because they don’t have the same “parameters” assigned for their rules (e.g., they might eat different species or move slower or faster).

The six stationary species in the model—the three algae and the three filter feeders (or “sessile consumers”)—are modeled differently than the mobile consumers. The simulation uses a transition matrix for these six stationary species. The transition matrix is a set of probabilities that determine what happens from one time step to the next on a particular space on the rock. For each species, the transition matrix lists the probability of an individual of that species settling on top of bare rock, the probabilities of being replaced by each of the other species, and the probability of dying (and being replaced by bare rock). In addition, the transition matrix includes the probability of bare rock remaining bare. For example, a patch of rock that contains Nori Seaweed (Porphyra) may do one of three things each time step: host a different species (that is, another organism displaces Nori Seaweed), continue to be occupied by Nori Seaweed, or become bare rock (the Nori Seaweed dies and is not replaced). Each of these changes, or transitions, has a probability associated with it included within the transition matrix. If one species out-competes another for space, this will be reflected in the relevant transition probability.

More Information Links to additional terms and topics relevant to this laboratory can be found in the Keystone Predator Library accessible via the SimBio Virtual Labs™ program interface.

Starting Up If you’ve explored tide pools (a fun thing to do if you visit a rocky coast), you likely know that many of the plants and animals living in them are unusual. This section will introduce you to the different species you’ll encounter in this lab.

[ 1 ] Make sure that you have read the introductory section of the workbook. The background information and introduction to ecological concepts will help you understand the simulation model and answer questions correctly.

[ 2 ] Start the program by double-clicking the SimBio Virtual Labs™ icon on your computer or by selecting it from the Start Menu.

[ 3 ] When SimBio Virtual Labs™ opens, select the Keystone Predator lab from the EcoBeaker™ suite.

IMPORTANT!

Before you continue, make sure you are using the SimBio Virtual Labs version of Keystone Predator. The splash screen for SimBio Virtual Labs looks similar to this:


If the splash screen you see does not look like this, please close the application (EcoBeaker 2.5) and launch SimBio Virtual Labs.

You will see a number of different panels on the screen:

––The left side of the screen shows a view of an intertidal zone area. This is where you will be conducting your experiments and observing the action.

––A bar graph on the right shows the population sizes of all of species in the intertidal area.

––Above the graph is a list of the plant and animal species included in the simulation. You can switch between Latin and common names of each species using the tabs above the species list. The workbook will refer to common names.

––In the bottom left corner of the screen is the CONTROL PANEL. To the right of the Control Panel is a set of TOOLS that you will use for doing your experiments. These will be described in the following exercises, as you need them.

[ 4 ] Click on the names in the SPECIES LEGEND in the upper right corner of the screen to bring up library pages for each species. Use the library to answer the following question:

[ 4.1 ] If you slipped on a rock while exploring a tide pool and your knee became inflamed, which of the three algal species might help reduce the swelling?

Nori Black Pine Coral Weed (Circle one)

[ 4.2 ] Use the information in the Introduction and Library pages to fill in the blank spaces in the table at the top of the next page.

��HELPFUL HINT: “producers” are organisms that generate their own food using energy from the sun, “filter-feeders” are consumers that extract food particles out of the water, and “stationary” (or “sessile”) organisms do not actively move around (at least not as adults) — they permanently adhere to substrates such as rocks.


[ 5 ] When you are done completing the above table, start the simulation by clicking the GO button in

the CONTROL PANEL. Watch the action for a bit. Notice how the mobile consumers clear off areas of rock by eating, and how the stationary species recolonize those areas.

[ 6 ] Click the STOP button to pause the simulation.

Look at the population graph. The “Population Size Index” represents the number of individuals of each species present in the simulation. For the three algal species, a more appropriate measure of relative abundance might be percent cover or biomass, because in the real world, a single alga can grow quite large. The EcoBeaker model simulates algal growth as individuals multiplying, which, though not exactly realistic, makes possible the comparison of population sizes for the three algal and six animal species.

[ 7 ] Click the RESET button to return the community to its initial state. Then move your mouse over to the population graph and click on one of the bars. You will see the population size for that species displayed.

[ 7.1 ] Which species in the simulation has the largest population? ___________

[ 7.2 ] What is the size of the population for that species? ___________

[ 7.3 ] Which species in the simulation has the smallest population? ___________

[ 7.4 ] What is the size of the population for that species? ___________


Exercise 1: Flexing Your Mussels In the competitive dominance hierarchy diagram below, the arrows point from weaker to

stronger competitors. For example, the arrow pointing from Nori Seaweed to Black Pine indicates that Black Pine is dominant over (i.e., can displace) Nori Seaweed. The diagram also shows that any of the sessile consumers (the barnacles and the mussel) can out-compete any of the algae.

[ 1 ] According to the figure above, which algal species is the strongest competitor?

Nori Seaweed Black Pine Coral Weed (Circle one)

The diagram does not indicate the dominance hierarchy among the three sessile consumers. Fortunately, you have some tools that will let you experimentally determine their competitive relationships. Your approach will involve creating patches of each sessile consumer on an intertidal rock and observing which of the other two sessile consumers successfully invades those patches. [ 2 ] Select “Flexing Your Mussels” from the Select an Exercise menu at the top of the screen to load the

experimental system.

You should now see only the six stationary species in the simulation (three algae and three sessile consumers)—this is because your assistant is patrolling the shore and keeping the


mobile consumers out of your experimental area. Excluding these predators will help you determine the competitive relationships among the sessile consumers.

[ 3 ] Find and click the ADD SESSILE CONSUMER button in the TOOLS PANEL. If you have trouble finding a particular button in the lab, move your mouse over buttons and ‘tool tips’ will appear. The ADD SESSILE CONSUMER button will initially have an Acorn Barnacle picture on it. If a different species appears, click the downward arrow next to the picture and select the Acorn Barnacle.

[ 4 ] Click somewhere in the Intertidal Zone area, hold down the mouse button, and then drag out a rectangle to create a solid patch of Acorn Barnacles. The rectangle should fill at least a third of the Intertidal Zone area and have no other species inside.

[ 5 ] Use the STEP button to advance the simulation one week at a time for ten weeks and monitor which other species displace Acorn Barnacles through time. These species are stealing rock space from the Acorn Barnacles, and thus are competitively dominant.

[ 5.1 ] Circle the species which are competitively dominant over Acorn Barnacles:

[ 6 ] RESET the simulation. Click the ADD SESSILE CONSUMER button and select the Goose Neck

Barnacle.

[ 7 ] Repeat steps 4 and 5 for Goose Neck Barnacles. [ 7.1 ] Circle the species that are competitively dominant over Goose Neck Barnacles:

[ 8 ] RESET the simulation. Click the ADD SESSILE CONSUMER button and select the Mussel. [ 9 ] Repeat steps 4 and 5 for Mussels.

[ 9.1 ] Circle the species that are competitively dominant over Mussels:


[ 10 ] Construct a competitive dominance hierarchy diagram using the information you have gathered from your experiments. Next to each species name, indicate how many arrows point to that species. The highest number indicates the most highly ranked and aggressive, or “best”, competitor. If you do this correctly, each species should have a different rank.


Exercise 2: You Are What You Eat Now you will continue to investigate this intertidal community by determining what each of the mobile consumer species eats. One trick ecologists use to find out what creatures eat is to look at what’s in their guts or excrement. If you eat something, it hangs around in your stomach for a little while, and then (later) the undigested parts come out the other end. Normally, when researchers look at gut contents they have to kill the animal, cut it open, and examine what is inside (people get paid to do this!). Within SimBio Virtual Labs there is a kinder and gentler method for determining gut contents.

[ 1 ] Select “You Are What You Eat” from the Select an Exercise menu at the top of the screen.

[ 2 ] You will now see all nine species in the simulation: three algae, three sessile consumers, and three mobile consumers: Starfish, Whelk, and Chiton.

[ 3 ] Start running the simulation (click GO).

[ 4 ] Watch the action for about 100 weeks and monitor the abundance of species in the population graph. Notice how the population index for each species fluctuates and eventually settles at a relatively stable level.

[ 5 ] STOP the simulation.

[ 6 ] Click the MICROSCOPE (“VIEW ORGANISM”) button in the TOOLS PANEL to activate your mobile “Gut-o-Scope” (patent pending).

[ 7 ] Click your favorite Starfish, Whelk, or Chiton — your choice!

A window will appear with gut content information for that individual, either identifying the predator’s last prey item or indicating that the gut is empty (because the creature has not eaten recently). Note that if you click on organisms that don’t have guts (algae or filter feeders), you won’t see gut contents.

[ 8 ] You will now conduct a survey of the three mobile consumers to learn which species they eat. The data forms below will help you record and summarize your findings. The forms are divided into three sections, one for each mobile consumer.

[ 8.1 ] In each data table section, record gut content data for 10 randomly-selected individuals of that species. Ignore individuals that have not eaten recently (indicated by gut contents labeled “empty”). If recorded correctly, each row of the data form should have one species circled. At the bottom of each section, record the total number of individuals circled in each column.


[ 8.2 ] For each mobile consumer species below, record its prey and the percentage of diet each prey species comprises for that consumer (e.g., 4 out of 10 samples = 40%). The numbers in each column should add up to 100%.

[ 9 ] You now have enough information to construct a food web diagram from your findings. Consider the hypothetical example below. In a forest, both deer and rabbits eat the plants. Wolves, the predators in the system, eat both deer and rabbits. We can draw these feeding relationships like this, with arrows pointing to the consumer:


[ 9.1 ] Use your data on feeding relationships to construct a food web diagram for the organisms that live in the simulated intertidal zone. Link the species names below with arrows that point from prey to consumer. (Unlike the simple four-species example on the previous page, your nine-species diagram will look more complicated, with many crossing lines.)


Experiment 3: Who Rules the Rock? Based on your studies so far, you know important details about the competitive and feeding relationships among the species in your simulated intertidal community, and these relationships, when integrated, define the role played by each. One way to more fully elucidate the importance of a species to its community structure is to remove it from the environment and observe what happens. In this exercise, you will experimentally determine how removing each of the highest trophic level species (the mobile consumers) affects the rocky intertidal community structure.

[ 1 ] Before you start your experiments, first make some predictions. Refer back to your data to inform your answers. [NOTE: only one species will be removed in each experiment.]

[ 1.1 ] In the spaces provided below, predict which other species in the community will be impacted the most by each removal and explain your reasoning.

Predicted impact of removing Whelk and explanation:

Predicted impact of removing Chiton and explanation:

Predicted impact of removing Starfish and explanation:

[ 1.2 ] One removal experiment will have a more dramatic impact than the other two. Write down which one you predict this will be, and why: [ 2 ] Select “Who Rules the Rock?” from the Select an Exercise menu.

[ 3 ] Your first step is to record population sizes BEFORE REMOVALS. To make sure the simulation is initialized correctly, click the RESET button. A data table is provided on the next page for recording your results.

��HELFUL HINT: if you click on the colored bars in the Population Size graph, the numbers (population sizes) that the bars represent will pop up!


[ 3.1 ] In the table on the next page, record the population size of each species at ‘Time Elapsed = 0 Weeks’ in the BEFORE REMOVALS column.

[ 4 ] After recording data BEFORE REMOVALS, you are ready to remove mobile consumers. Find the REMOVE WHELK button (which is round and depicts a Whelk with a slash through it) in the TOOLS PANEL. When you click this button, all Whelk will vanish from the Intertidal Zone.

[ 5 ] For each removal experiment, you will run the simulation for 200 weeks (in model time, not real time!). To do this, first make sure that the Time Elapsed = 0 weeks (RESET if not), and then click the STEP 200 button in the CONTROL PANEL..

��HELPFUL HINT: If your computer is a little slow, you can speed things up using the Speed Slider to the right of the CONTROL PANEL

[ 6 ] Confirm that the simulation stopped at (or near) 200 weeks. If so, click the bars in the Population Size graph and record the abundance of each species.

[ 6.1 ] In the data table, record the population size of each species in the AFTER WHELK REMOVAL column.

[ 7 ] RESET the simulation and confirm that Time Elapsed = 0 weeks. Then click the REMOVE CHITON button to remove all Chiton from the Intertidal Zone.

[ 8 ] Click the STEP 200 button to run the simulation for 200 weeks.

[ 8.1 ] When Time Elapsed = 200 weeks, record the population size of each species in the AFTER CHITON REMOVAL column.

[ 9 ] Finally, RESET the simulation and use the REMOVE STARFISH tool and the STEP 200 button to repeat the experiment for Starfish.

[ 9.1 ] In the data table, record the population size of each species in the AFTER STARFISH REMOVAL column.

[ 10 ] When your data table is complete, answer the following questions. Try to be as quantitative as

possible with your answers, indicating by approximately how much each species increased or


decreased in size (e.g., “The Starfish population more than doubled”; “The population of Coral Weed decreased to about half its original size.”).

[ 10.1 ] What were the most dramatic changes to the community after Whelk were removed?

[ 10.2 ] What were the most dramatic changes to the community after Chiton were removed?

[ 10.3 ] What were the most dramatic changes to the community after Starfish were removed?

[ 10.4 ] Which removal had the greatest impact upon the rest of the community? [ 10.5 ] Referring back to your competitive dominance hierarchy and food web diagrams, try to explain what happened in the removal experiment that had the greatest impact on community structure. Why was the effect so pronounced? [ 10.6 ] Look back at what you predicted would happen when you removed each of the three mobile consumer species in Step 1 above. Were you correct for all three? If not, describe what you think you missed in each case.


The Keystone Species Concept As described in the Introduction, a “keystone” is the stone in the middle of the top of an arch that supports all the other stones. If you remove the keystone, the whole arch falls down. In many ecological communities, one species can play a particularly important role in determining and supporting community structure. Remove this species and the community structure changes radically. When you removed one mobile consumer from the intertidal simulation, it had a much larger impact on the rest of the community compared to the removal of the other two mobile consumers. This species is an example of a Keystone Species, which has a disproportionately large impact on its ecological community compared to its relatively low abundance.

The keystone predator in the intertidal zone you studied occupies a position at the top of the food chain, which is common for keystone species. They also tend to be susceptible to both natural and human disturbance. Other examples of the dramatic effects of keystone species removals in the real world include deer populations rapidly increasing with the local extinction of predatory wolves, kangaroo rats dominating rodent communities with the removal of coyotes, and sea urchins decimating kelp forests when predatory sea otters go into decline.

Species Reintroduction [ 11 ] Conservationists sometimes advocate reintroducing native species that have gone extinct locally. A

well-known recent example is the reintroduction of wolves into Yellowstone National Park.

[ 11.1 ] If you were to reintroduce Starfish into the Intertidal Zone, what do you think would happen to the intertidal community? Write your prediction in the space provided below:

[ 12 ] RESET the simulation, REMOVE STARFISH, and STEP 200 weeks forward (this returns the simulation to its state at the end of the previous exercise). Click the ADD MOBILE CONSUMER tool and select Starfish. [ 13 ] Move your mouse into the Intertidal Zone and click six or seven times to add some Starfish back

into the community.

[ 14 ] Use the STEP 200 button to advance the simulation another 200 weeks and watch what happens to the population sizes of the different species.

[ 14.1 ] Briefly describe the changes you observed in abundance of different species.


[ 14.2 ] Look at the population sizes and describe how they compare to your BEFORE REMOVAL population data — when you first ran the simulation before removing any of the mobile consumers.


Biocontrol Organisms that are introduced to eliminate or otherwise limit population growth for unwanted pest species are known as “biocontrol agents”. Biocontrol agents are often parasites, predators, or pathogens. Ideally, when biocontrol agents are introduced into an ecological community, the unwanted species will be controlled without further intervention and the system will be self-sustaining. Use of biocontrol agents is not without risk however, and biologists must be very careful that the introduced biocontrol organism will not further negatively impact the native species and become a pest itself. There are plenty of nightmare examples from our past where introduced biocontrol agents have become a bigger problem than the species they were introduced to control. Famous examples include the mongoose in Hawaii, which was introduced to eat rats but preferred eating the native bird fauna; and cane toads in Australia, which were introduced to control a sugar cane beetle, but instead had devastating impacts on indigenous amphibian and reptile communities and did almost nothing to control the insect pests.

There is a parasitic barnacle, Sacculina, which research has suggested might be useful as a biocontrol agent for invasive European green crabs. The larvae of Sacculina settle on crabs, piercing the exoskeleton. This type of infestation slows down the crabs’ ability to reproduce and therefore, over time should lead to a decrease in the crab population size.

[ 2.2 ] Having investigated the impacts of the European Green Crab on the intertidal zone community, the Department of Fish and Wildlife is considering using parasitic barnacles (Sacculina) to control the crab. Given what you know about the importance of species interactions to community structure, what would you suggest should be learned about this parasitic barnacle species before it is introduced into the intertidal community you have been studying?


Key Publications This laboratory was inspired by the following classic papers by R.T. Paine, whose work on intertidal communities originated the idea of keystone predation:

Paine, R.T. 1966. Food Web Complexity and Species Diversity. The American Naturalist 100: 65-75

Paine, R.T. 1969. The Pisaster-Tegula Interaction: Prey Patches, Predator Food Preference, and Intertidal Community Structure. Ecology 50: 950-961.

The following review article addresses the idea of keystone species from a more modern perspective:

Power, M.E., D. Tilman, J.A. Estes, B.A. Menge, W.J. Bond, L.S. Mills, G. Daily, J.C. Castilla, J. Lubchenco, R.T. Paine. 1996. Challenges in the Quest For Keystones: Identifying Keystone Species is Difficult — But Essential To Understanding How Loss of Species Will Affect Ecosystems. BioScience 46: 609-620.


Week 14 – Thanksgiving


Week 15 – Review


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