Skip to main content
Biology LibreTexts

Food Choice Lab

  • Page ID
    7713
  • Adapted by Staci Forgey, Tidewater Community College biology faculty, with permission from Niagara University’s Dr. William Edwards Food Choice Lab.

    Learning Objective

    By the end of this section, you will be able to:

    Identify the basic tenets of Ecology and how they apply to population success.


    Food choice tz from Lumen Learning

    Foraging is the act of an animal searching for food. An animal’s choice of food should reflect energetic considerations such as maximizing net energy gain per unit time or net gain per cost expended in foraging. Animals want the most energy return for their caloric investment. They seek out food sources that will give them the most energy reward for the least amount of energy expended. We will be examining the foraging behavior of birds in our quad. 

    Question

    1. What factors might influence seed selection in the birds we will be watching? Name three factors and their effects.

    Given these factors, we will now form hypotheses and predictions that we will test by observing birds in the courtyard area. After, we will perform a Chi-Square statistical test on our data, and revise our hypotheses and thought processes to fit the new data.

    Many birds will visit our feeders and collect seeds. Our feeders are stocked with three types of seeds. The Feathered Friend black oil seed is very soft hulled. The Lyric Sunflower gray striped sunflower seeds are large and thick hulled.  Gray safflower seeds are also small and soft hulled. 

    Question

    1. Look at the three seed types. Feel them and try to break them open. Note the differences you observe between the seed types.   

    The table below summarizes measurements for the 3 types of seeds. This shows the average kernel (food part of the seed), hull (outside covering of the seed) and entire seed weight. The kernel/hull ratio for each seed type, and the average caloric (energy) content of the seeds. Use these values and your observations to make predictions about optimal foraging behavior for various bird types that visit the feeders. 

    Table 1: Mass and caloric content of sunflower seed components.
    Seed (mg) Kernel (mg) Hull (mg) Entire seed (mg) Kernel/Hull Cal/g Cal/seed
    Black oil 28.8 ± 3.8 12.2 ± 1.6 41.0 ± 0.5 2.37 ± 0.3 5400 ± 240 150
    Safflower 26.8 ± 3.9 10.4 ± 3.3 38.3 ± 6.9 2.603 ± 0.1 6200 ± 240 161
    Striped 61.7 ± 4.7 59.1 ± 2.7 120.8 ± 6.0 1.045 ± 0.1 5600 ± 240 350

    Now, take a look at the birds that are commonly found in our area during this time period. You will have a list of birds on your lab bench.

    Your lab group will propose two experimental hypotheses about avian seed choice and discuss these with your instructor. Provide a rationale for your testable hypotheses. Clearly state each of your ideas in the form of an “If . . . then . . .” hypothesis. If your hypothesis is correct, which type of seed do you expect the birds to choose? Why?

    Questions

    1. Clearly state a testable hypothesis explaining why birds will choose or not choose the different seeds. 
    2. Would this hypothesis change with different birds?  Why or Why not?
    3. What is the alternative(s) to your hypothesis? (i.e., if you are wrong)
    4. If your hypothesis is right, what would you predict the birds will do at the feeders?
    5. If your alternative is right, what would you predict?

    Check in with your instructor before continuing after this point!


    In order to test your hypothesis, we need to think a bit about experimental design. Variables are important to consider because they will help us evaluate our hypothesis. For example, if we were interested in the height at which giraffes eat their food from, we might propose a hypothesis that giraffes will eat food from high areas in a tree. Each time the giraffe ate, the height at which the food was taken from would need to be recorded. This gives us two variables: the height at which the mouthful of vegetation came from and the mouthful height. This height is measurable. We will look at what is called “categorical” data. Each of our data points will fit into a category. We will have a bird taking a seed (making a food choice), and the type of seed chosen. 

    Questions

    1. What is the independent variable (what we are manipulating)?
    2. What is the dependent variable (what we are measuring)?
    3. Design a data table to record the species of bird and type of seed each bird selects during our lab time. You can use hash marks to record visits. We will be visiting our feeders for a 20 minute time span. 

    Discuss your data sheet with your instructor before you begin collecting data!


    When we return, you’ll need to transfer the data from your table into the following table as totals. 

    Bird Type Safflower Striped Sunflower Black Oil
           
           
           
           
           
           
    TOTALS      

    Bird Observation

    Stay well away from the feeders at all times and keep as quiet as possible. One noisy or clumsy move can scare all the birds away, leaving you without any data!

    QUESTION

    1. Describe the behavior of the birds with the seeds.
    2. How long does it take an individual bird (record the species of bird and the type of seed eaten) to eat a seed and return to the feeder for another?  Use your binoculars to follow individual birds and record the time from when a bird takes a seed to when it returns for another.  Do the birds do anything unusual with the seeds?
    3. Compare and contrast the seed handling behavior of the birds visiting the feeders.  How do the different birds crack each of the seeds?  Do they have difficulty opening any of them? 

    Statistical Analysis

    Using your feeder choice data, perform a chi-square Goodness of Fit test to determine if the birds show a preference for any given seed.

    A Chi-Square test involves testing the probability that your categorical data differs from random enough to have confidence that the data does not come from chance alone.  Typically, we accept that significance is 0.05 (called alpha).  This means that there is only a 1 in 20 chance of the data arising from chance alone.  When doing a chi-square, we typically call the boring hypothesis (that all the birds would randomly select seeds and your hypothesis is wrong) the “null” hypothesis, abbreviated HO.  The hypothesis where the data support your idea is called the alternate hypothesis, abbreviated Ha.  Note:   this is a different type of hypothesis, used to fit specifically into statistical tests.  This may not match perfectly with your description of hypotheses above.  A chi-square also doesn’t tell you exactly where the differences causing significant deviation from the expected.  You would need more involved statistics for that.  We will visually pick out our differences in data. 

    First, transfer your choice data into a table like the following table.  We will calculate the expected values from the data. 

    Add the data for each bird horizontally, then for each seed vertically.  The total row and total column should now be filled.  These represent how many birds and seeds by type were actually eaten and recorded.  Now we will calculate the expected values based on how these would be distributed by random chance.  We will take the total for each species, multiply it by the total number of seeds for that seed type, and divide by the total number of seeds

    For example, \(\frac{111 \times 178}{532} = 37.13\)

    Put the answer into the table.  This is how many safflower seeds would be expected to be eaten by the chickadees through chance alone.  Complete your own table.

    SAMPLE TABLE
      Safflower Striped Black Oil Total
    Black-capped chickadee 13 23 75 111
    (Expected) 37.13    
    Tufted titmouse 21 32 23 76
    (Expected)      
    House finch 45 23 43 111
    (Expected)      
    American goldfinch 0 0 0 0
    (Expected)      
    Nuthatches 0 0 0 0
    (Expected)      
    Junco 76 32 32 140
    (Expected)      
    House sparrows 23 27 44 94
    (Expected)      
    TOTALS 178 137 217 532

     

    Bird Type Safflower Striped Black Oil Total
             
    (Expected)        
             
    (Expected)        
             
    (Expected)        
             
    (Expected)        
             
    (Expected)        
             
    (Expected)        
             
    (Expected)        
             
    (Expected)        
    TOTALS        

    To calculate the chi-square value, or \(\chi^2\)​​, we simply add the square differences, divided by the expected, of all the observed and expected.  In mathematical terms:

    \(\chi^2=\sum \frac{(O-E)^2}{E}\)

    So for our example from the previous sample table, the first \(\frac{(O-E)^2}{E}\)​​​​ would be

    \(\frac{(13-37.13)^2}{37.13}=15.7\).

    We would then add to this value all of the other \(\frac{(O-E)^2}{E}\) in the table to get the \(\chi^2\)​ value. 

    Question

    1. Compute the \(\chi^2\) value for your data. (Use an extra sheet of paper if necessary)

    In order to find something to compare this number with, we need to calculate the degrees of freedom, or the number of different comparisons that can be made within the table.  The degrees of freedom are the number of columns (M) minus one times the number of rows (N) minus one.  Degrees of freedom = (M–1) (N–1)

    Questions

    1. How many ways can this two by two table be broken into individual comparisons? Hint: Use the formula above.
        A     B  
        C   D
    2. Calculate the degrees of freedom in your experiment. 

    Now we can compare against the Chi distribution for the likelihood that our data is generated by chance.  Remember, we want a 0.05 or less value to say that it’s not chance, but our hypothesis that’s causing the choices.

    Questions

    1. Compare to the Chi-distribution table at the end of the lab with your instructor’s assistance. What is the p value or probability that your data came out by chance? Is this less than 0.05? Did your results come about by chance?
    2. Describe and summarize what you observed in the field. Are some parameters more difficult to measure than others? If so, why? Which predictions did your data support? Interpret you results as they relate to your hypotheses and discuss your interpretation. 
    3. How could you re design your experiment to better measure energy gains, handling time, and energetic costs of foraging, and thus more accurately test the predictions of optimal foraging theory? Think of other hypotheses regarding seed choice by birds? Propose a follow up study that would allow you to test a related idea about avian foraging behavior. Make clear the ways in which your proposed study is an extension of or improvement upon the study on which you report here. 

    Chi Square Distribution Table:

    DF/P 0.995 .990 0.975 .950 .900 .750 .500 .250 .100 .050 .025 .010 .005
    1 0.00004 .00016 0.001 0.004 0.016 0.102 0.455 1.323 2.706 3.841 5.024 6.635 7.879
    2 0.010 0.020 0.0506 0.103 0.211 0.575 1.386 2.773 4.605 5.991 7.378 9.210 10.597
    3 0.072 0.115 0.216 0.351 0.584 1.213 2.366 4.108 6.251 7.815 9.348 11.345 12.838
    4 0.207 0.297 0.484 0.711 1.064 1.923 3.357 5.385 7.779 9.488 11.143 13.277 14.860
    5 0.412 0.554 0.831 1.145 1.610 2.675 4.351 6.626 9.236 11.070 12.833 15.086 16.750
    6 0.676 0.872 1.237 1.635 2.204 3.455 5.348 7.841 10.645 12.592 14.449 16.812 18.548
    7 0.989 1.239 1.690 2.167 2.833 4.255 6.346 9.037 12.017 14.067 16.013 18.475 20.278
    8 1.344 1.647 2.180 2.733 3.490 5.071 7.344 10.219 13.362 15.507 17.535 20.090 21.955
    9 1.735 2.088 2.700 3.325 4.168 5.899 8.343 11.389 14.684 16.919 19.023 21.666 23.589
    10 2.156 2.558 3.247 3.940 4.865 6.737 9.342 12.549 15.987 18.307 20.483 23.209 25.188
    11 2.603 3.053 3.816 4.575 5.578 7.584 10.341 13.701 17.275 19.675 21.920 24.725 26.757
    12 3.074 3.571 4.404 5.226 6.304 8.438 11.340 14.845 18.549 21.026 23.337 26.217 28.300
    13 3.565 4.107 5.009 5.892 7.042 9.299 12.340 15.984 19.812 22.362 24.736 27.688 29.819
    14 4.075 4.660 5.629 6.571 7.790 10.165 13.339 14.114 21.064 23.685 26.119 29.141 31.319
    15 4.601 5.229 6.262 7.261 8.547 11.037 14.339 18.245 22.307 24.996 27.488 30.578 32.801
    16 5.142 5.812 6.908 7.962 9.312 11.912 15.339 19.369 23.542 26.296 28.845 32.000 34.267
    17 5.697 6.408 7.564 8.672 10.085 12.792 16.338 20.489 24.769 27.587 30.191 33.409 35.718
    18 6.265 7.015 8.231 9.390 10.865 13.675 17.338 21.605 25.989 28.869 31.526 34.805 37.156
    19 6.844 7.633 8.907 10.117 11.657 14.562 18.338 22.18 27.204 30.144 32.852 36.191 38.582
    20 7.434 8.260 9.591 10.851 12.443 15.452 19.337 23.848 28.412 31.410 34.170 37.566 39.997
    21 8.034 8.897 10.283 11.591 13.240 16.344 20.337 24.935 29.615 32.671 35.479 38.932 41.401
    22 8.643 9.542 10.982 12.338 14.041 17.240 21.337 26.039 30.813 33.924 36.781 40.289 42.796
    23 9.260 10.196 11.689 13.091 14.848 18.137 22.337 27.141 32.007 35.172 38.076 41.638 44.181
    24 9.886 10.856 12.401 13.848 15.659 19.037 23.337 28.241 33.196 36.415 39.364 42.980 45.559
    25 10.520 11.524 13.120 14.611 16.473 19.939 24.337 29.339 34.382 37.652 40.646 44.314 46.928
    26 11.160 12.198 13.844 15.379 17.292 20.843 25.336 30.435 35.563 38.885 41.923 45.642 48.290
    27 11.808 12.879 14.573 16.151 18.114 21.749 26.336 31.528 36.741 40.113 43.195 46.963 49.645
    28 12.461 13.565 15.308 16.928 18.939 22.657 27.336 32.620 37.916 41.337 44.461 48.278 50.993
    29 13.121 14.256 16.047 17.708 19.768 23.567 28.336 33.711 39.087 42.557 45.722 49.588 52.336
    30 13.787 14.953 16.791 18.493 20.599 24.478 29.336 34.800 40.256 43.773 46.979 50.892 53.672

    LICENSES AND ATTRIBUTIONS

    CC LICENSED CONTENT, ORIGINAL