Foraging Biology Lab Report

THE LAB REPORT

This is basically a short research paper, rather than a lab report, but you still use the Lab Report Guides available on Blackboard to structure your paper. It should be only up to 4-5 pages including one figure and one table with your raw data included in the Appendix section.

Introduction: There should be an introduction that states the importance and aims of the study. It gives some background on working with giving-up-densities (what a GUD is and why it is useful) and presents the three hypotheses that you tested.

Methods: Write all about the design and how you ran the experiment and the analysis (i.e., what descriptive and hypothesis-testing statistics did you use?)

Results: State the results in declarative ways without discussing what they mean. Do however interpret your statistics as outlined above. Include your figure in this section.

Discussion: Discuss your results and compare them to studies from literature (e.g., what do your results mean, what are the main conclusions, what did you learn about the rabbits or sparrows).

Handout: “Cottontail” foraging adventure Part I – The set-up
Getting started: For this experiment you will be working in groups of two or three. So the first thing
you need to do is find people you want to work with. Then you need to figure out what you want to test.
What are the possibilities? Hopefully, you already have some ideas after the lecture. But here are a few:
-The type of food (e.g. higher protein versus higher fiber).
-Patch Characteristics (e.g. tray size, amount of sand, location of food within the trays).
-Location of the patch (e.g. open versus cover).
-Time (e.g. dawn versus dusk, sunny versus cloudy days).
-Obstacles (garden fencing around the tray or not).
The general procedure for setting up food patches: Each group will get four trays (14”), four
additional trays (16”) with which to cover the experimental trays, and sand to fill the trays.
Design your experiment with a 2×2 factorial structure. That means you should pick two treatments, each
one with 2 levels.
Getting equipment: All the supplies you’ll need (trays, food, sand, sieve, scale) will be in the storage
room in SES 3315. This room will be open during the day next week on (although if you see it closed,
find one of the TAs to let you in). Please return the sieve to the room every time you use it!
Note that on the first day your group sets out the trays, email their location to your TA, and email him/her
again when you’ve returned the trays a few days later.
Fill the tray with the desired amount of dry sand (c. 1.5 – 2 liters). Your hypotheses may affect what the
desired amount is.
Put the desired amount (7 – 14 g depending on your question) and type of food in each of the trays and
thoroughly mix it with the sand. Again, this could vary slightly depending on your hypotheses.
Set the trays out (or uncover them if they’re already in place) during the afternoon and collect the
remaining food from them the next day. The time you set out and collect the food might be affected by
your hypotheses.
The remaining food is collected by passing the sand through a sieve (we’ll show you which ones to use).
Collect the pellets and weigh them (we’ll have a balance in the lab) to get the giving-up density (GUD).
You will need to repeat the previous step for a minimum of three nights. Mark where you put your trays
on the Google Earth picture of the UIC greenhouse.
Before we begin: First, if the sand is wet, we will need to dry it out first. The sand must be maintained
dry at all times during the experiment. Whenever it gets wet (from rain), you need to dry it before you
continue the experiment.
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More importantly, you will need to hand in a short proposal about what hypothesis (or hypotheses)
you want to test. It should be typed and no more than one page long. State what factor(s) you are
interested in testing, and why the treatments will test that. Also include your predictions about which
trays will have lower GUDs (that is left-over food). If the team members have alternative predictions, be
sure to include that in the proposal. Each team only needs to turn in one proposal. But as a reminder,
when it comes time to write the report for the experiment each person must write her or his own
report. When you turn in your proposal, we will look it over and tell you whether you are ready to start
the experiment.
See separate file in Blackboard for instructions on proposal submission.
Note: Each person must include in her or his report the days each of the group members set-up and/or
collected data, and include some photos showing team members at the study site, patch layout, and what
the patches look like when they have been foraged.
Team Name
Member 1
Member 2
Member 3
Handout: “House sparrow” foraging adventure Part I – The set-up
Getting started: For this experiment you will be working in groups of two or three. So the first thing
you need to do is find people you want to work with. Then you need to figure out what you want to test.
What are the possibilities? Hopefully, you already have some ideas after the lecture. But here are a few:
-The type of food (e.g. different crop seeds; or native versus crop seeds, large vs. small).
-Patch Characteristics (e.g. tray size, amount of sand, location of food within the trays).
-Location of the patch (e.g. open versus cover).
-Type of tree (spruce versus oak)
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-On the ground or above ground, obstacles around tray
The general procedure for setting up food patches: Each group will get four trays (12”), four
additional trays (14”) with which to cover the experimental trays, and sand to fill the trays.
Design your experiment with a 2×2 factorial structure. That means you should pick two treatments, each
one with 2 levels.
Getting equipment: All the supplies you’ll need (trays, food, sand, sieve, scale) will be in the storage
room in SES 3315. This room will be open during the day nest week on (in case you see it closed, find
one of the TAs to let you in). Please return the sieve to the room every time you use it!
Note that on the first day your group sets out the trays, email their location to your TA, and email him/her
again when you’ve returned the trays a few days later.
Fill the tray with the desired amount of dry sand (c.1-1.5 liters). Your hypotheses may affect what the
amount is.
Put the desired amount (1-3 g depending on your question) and type of food in each of the trays and
thoroughly mix it with the sand. Again, this could vary slightly depending on your hypotheses.
Set the trays out (or uncover them if they’re already in place) during the morning and collect the
remaining food from them that afternoon.
The remaining food is collected by passing the sand through a sieve (we’ll show you which ones to use).
Collect the seeds and weigh them (we’ll have a balance in the lab) to get the giving-up density (GUD).
You will need to repeat the previous step for a minimum of three days. Mark where you put your trays on
the Google Earth picture of the quad near SEO. (A good place to place trays are the spruce trees near
Morgan St.)
Before we begin: First, if the sand is wet, we will need to dry it out first. The sand must be maintained
dry at all times during the experiment. Whenever it gets wet (from rain), you need to dry it before you
continue the experiment.
More importantly, you will need to hand in a short proposal about what hypothesis (or hypotheses)
you want to test. It should be typed and no more than one page long. State what factor(s) you are
interested in testing, and why the treatments will test that. Also include your predictions about which
trays will have lower GUDs (that is, the left-over food). If the team members have alternative
predictions, be sure to include that in the proposal. Each team only needs to turn in one proposal. But as
a reminder, when it comes time to write the report for the experiment each person must write her or his
own report. When you turn in your proposal, we will look it over and tell you whether you are ready to
start the experiment.
See separate file in Blackboard for instructions on proposal submission.
3
Note: Each person must include in her or his report the days each of the group members set-up and/or
collected data, and include some photos showing team members at the study site, patch layout, and what
the patches look like when they have been foraged.
Team Name
Member 1
Member 2
Member 3
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Bios 331: Ecology
FORAGING EXPERIMENT DATA ANALYSIS
DESCRIPTIVE STATISTICS
To begin, you need to organize the data in your Excel spreadsheet to facilitate the analyses
(Table 1). Then use the Excel functions to calculate means and standard error.
Rich Food/
Risky place (e.g.,
away from tree)
Day
1
2
3
Average
St. Error
Rich Food/ Safe
place (e.g.,
nearer to tree)
Giving-up densities (grams)
5
1
6
2
7
3
Poor Food/ Risky
place
Poor Food/ Safe
place
4
8
8
5
5
5
Table 1. Example data table. Notice that there are two variables (Food Type & Spatial Location) each with
two levels (Rich food vs Poor Food & Risky Place vs Safe Place). This means that there are four possible
conditions, as expected of a 2×2 Factorial Design.
•
•
To calculate the average, you can use the Excel function “=AVERAGE(B4:B6)”. In the
parentheses, select the cells you wish to calculate an average from. In this example, my
data was in cells B4, B5, and B6.
Unfortunately, there is no function to calculate standard error, but recall that this statistic
is calculated by dividing the standard deviation by the square root of the number of
replicates. In other words: “=STDEV(B4:B6)/SQRT(COUNT(B4:B6))”
o STDEV: Calculates Standard Deviation
o SQRT: Calculates the Square Root
o COUNT: Counts the number of Cells
Recall that experimentally you used a 2×2 factorial design. We have two factors (food quality and
spatial location) and two levels of each factor (e.g., poor and rich for food quality). The design is
factorial because we have a treatment for each possible combination of the levels of the two
factors. This type of experimental design allows us to test the effect of food quality, spatial
location of trays, and their interaction, on the foragers’ giving up density (GUD). An interaction
describes non-additive effects of the two independent variables (e.g., food quality and spatial
location) on the dependent variable (that is, GUD, the leftover food).
Before you perform the statistical tests of your hypotheses, you should graph your data to get an
idea of the trends in the data set. This data set can easily be visualized using a bar graph with
standard error bars (Figure 1).
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Bios 331: Ecology
Figure 1. Example GUD figure. Notice that one of the variables is illustrated on the x-axis and the other
one as a legend. We could flip these variables to make the food type appear as the legend and the spatial
location on the x-axis and it would not affect the result. Error bars represent standard error.
HYPOTHESIS-TESTING STATISTICS
To test your hypotheses about each factor and interaction between them, we can use ANOVA
(analysis of variance). To conduct an ANOVA follow these steps:
1. Go to http://vassarstats.net/
2. On the left, click on “ANOVA”
3. Click on “Two-Way Factorial ANOVA for Independent Samples”
4. Scroll down to “Number of rows in analysis =” and type “2” in the box.
5. Scroll down to “Number of columns in analysis =” and type “2” in the box.
6. Click on “Setup”
7. Click on “Weighted”
8. Paste your raw data into the yellow boxes (Figure 2).
9. Click on “Calculate”
10. Scroll down to the box that says “ANOVA Summary”
11. The values for “Rows” correspond to the variable that varies by row. In my case, what
differ between Row 1 and Row 2 is spatial location. Same logic for “Columns”. The “r x
c” is telling you whether your variables interact or have additive effects (Table 2).
Figure 2. This is what the window where you
input your raw data will look like. Make sure you
know what box will hold what (example on the
left) data so you can interpret the output later.
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Bios 331: Ecology
Source
Rows
Columns
rxc
Error
Total
SS
10.08
24.08
4.09
14.67
52.92
df
1
1
1
8
11
MS
10.08
24.08
4.09
1.83
F
5.5
13.13
2.23
p
0.047
0.0067
0.1737
Table 2. This is what our example data ANOVA Summary table looks like. I know that “Rows” corresponds
to Spatial Location and “Columns” to Food Type because that is how we input the data earlier.
You will need the three F-values (F), Degrees of Freedom (df), and p-values (p) from the
ANOVA summary table.
INTERPRETING AND WRITING STATISTICS
If your p-value is below 0.05, then conclude that there is a significant effect of the factor. For
example, spatial location on the rows has a significant effect. Now look at your graph and see if
the direction of the effect agrees with your hypothesis. In this example, increased distance from
the tree yielded higher GUDs. In the lab report, you will report this result in a sentence with the
relevant statistical values in a parenthesis at the end.
For example: “Average GUDs were higher when the trees were placed closer to the trees (F =
5.5, df = 1, p = 0.047).”
Note that the interaction (r x c) is not significant. This means that although food quality and patch
location are significant, their effects are largely additive.
THE LAB REPORT
This is basically a short research paper, rather than a lab report, but you still use the Lab Report
Guides available on Blackboard to structure your paper. It should be only up to 4-5 pages
including one figure and one table with your raw data included in the Appendix section.
Introduction: There should be an introduction that states the importance and aims of the study.
It gives some background on working with giving-up-densities (what a GUD is and why it is
useful) and presents the three hypotheses that you tested.
Methods: Write all about the design and how you ran the experiment and the analysis (i.e., what
descriptive and hypothesis-testing statistics did you use?)
Results: State the results in declarative ways without discussing what they mean. Do however
interpret your statistics as outlined above. Include your figure in this section.
Discussion: Discuss your results and compare them to studies from literature (e.g., what do your
results mean, what are the main conclusions, what did you learn about the rabbits or sparrows).
Literature Cited: Use at least 3. Arrange them in alphabetical order, and make sure to follow
the instructions from Assignment 2. Also make sure that you actually cite them in the text!
Appendix: Include your raw data as a table here.
There’s no need for an Abstract, but make sure you make up a short title with a declarative
statement about the experiment you performed.
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Bios 331: Ecology
Important notes:
– Although team members will be using the same data, when it comes to writing the report
for the experiment each person must write his or her own report.
– Upload your completed report to Safe Assign on Blackboard.
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Behav Ecol Sociobiol (2013) 67:1541–1553
DOI 10.1007/s00265-013-1609-3
REVIEW
A practical guide to avoid giving up on giving-up densities
Miguel A. Bedoya-Perez & Alexandra J. R. Carthey &
Valentina S. A. Mella & Clare McArthur & Peter B. Banks
Received: 28 April 2013 / Revised: 11 July 2013 / Accepted: 13 July 2013 / Published online: 1 August 2013
# Springer-Verlag Berlin Heidelberg 2013
Abstract The giving-up density (GUD) framework provides
a powerful experimental approach with a strong theoretical
underpinning to quantify foraging outcomes in heterogeneous
landscapes. Since its inception, the GUD approach has been
applied successfully to a vast range of foraging species and
foraging scenarios. However, its application is not simple, as
anyone who has tried to use it for the first time might attest.
Limitations of the technique were noted at its conception, yet
only the artificiality of the patches, the appropriateness of the
food resource, and the possibility of multiple visiting foragers
were identified. Here we show the current uses of GUD and
outline the practical benefits as well as the often overlooked
limitations of the technique. We define seven major points that
need to be addressed when applying this methodology: (1) the
curvilinearity between harvest rate and energy, (2) the energetic state of the forager, (3) the effect of group foraging, (4)
food quality and substrate properties, (5) the predictability of
the patch, (6) behavioral traits of the forager, and (7) nontarget
species. We also suggest how GUD experiments can be enhanced by incorporating complementary methods (such as
cameras) to better understand the foraging processes involved
in the GUD itself. We conclude that the benefits of using GUD
outweigh the costs, but that its limitations should not be
ignored. Incorporating new methods when using GUD can
potentially offer novel and important insights into the study of
foraging behavior.
Keywords Foraging . Giving-up density . Landscape of fear .
Methodological limitations . Practical assumptions .
Supplementary approaches
Communicated by P. M. Kappeler
M. A. Bedoya-Perez (*) : A. J. R. Carthey : V. S. A. Mella :
C. McArthur : P. B. Banks
School of Biological Sciences, The University of Sydney,
Camperdown, NSW 2006, Australia
e-mail: miguel.bedoyaperez@sydney.edu.au
Introduction
By the time Ernst Haeckel (1873) proposed “Ökologie” as the
name of a new, emerging branch of science—modern ecology
—foraging was already an established and widely used term
that described the process of looking for food, defining where
and what an animal chooses to eat. However, the idea of
foraging as a mechanism for maximizing fitness was not
proposed until the 1960s (Emlen 1966; MacArthur and
Pianka 1966). This was followed by the development of optimal foraging theory (OFT), and a myriad of mathematical
models have since been constructed to help understand this
theory (Stephens and Krebs 1986; Stephens et al. 2007). Early
models describing how OFT worked were mostly theoretical
(Charnov 1976; Pyke et al. 1977), although some approached
foraging from a more practical, yet qualitative perspective
(Hay and Fuller 1981). In 1988, Joel S. Brown described an
elegant experimental and mathematical approach to quantitatively measure an animal’s foraging decisions in the wild based
on patch characteristics, using giving-up density (GUD). The
GUD framework is underpinned by an extension of the marginal value theorem (Charnov 1976), relying on the existence
of food patches as a depletable food source that foragers
exploit differentially in order to maximize fitness. Therefore,
the amount of food that foragers leave in a patch (i.e., the
GUD) reflects the perceived cost of foraging at that patch, such
that a lower GUD indicates a lower net cost.
According to this framework, in a depletable patch
where harvest rate (H) decreases as more food is consumed over time, the forager should quit the patch
when the benefits of harvesting no longer outweigh
the costs. This framework incorporates costs associated
with predation risk (P), searching and processing (i.e.,
handling and digesting) resources from that patch as
well as thermoregulatory costs (C) and missed opportunities elsewhere (MOC). The concept (Brown 1988) is
expressed as:
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H ¼ C þ P þ MOC
Subsequent authors have made modifications to the initial
model, for example, by adding a term that represents the
intensity of interference behavior (I) experienced by individuals of a given species, in order to explain habitat partitioning
(Kotler and Brown 1988),
H ¼ C ðI Þ þ P þ MOC
the cost of toxin compounds present in the food (T) (Schmidt
2000),
H ¼ C þ P þ MOC þ T
the benefits of water (W) near the patch for desert animals
(Shrader et al. 2008b),
H ¼ C þ P þ T ðW Þ þ MOC
the risk of injury (RI) during foraging at the patch (Berger-Tal
et al. 2009),
H ¼ C þ RI þ P þ MOC
and the foraging benefits of information (FBI) (Olsson and
Brown 2010),
H ¼ C þ P þ FBI þ MOC:
In a practical sense, most researchers have explored parameters in this framework by building surrogate patches in which
food is mixed through an inedible matrix, imposing an ever
increasing search cost as food is consumed. As a result, the
amount of food left by a forager at one of these surrogate
feeders reflects the composite costs associated with the characteristics of the food and the area surrounding the feeder.
Some notable exceptions from the use of artificial feeding
patches have been the use of the bite diameter at point of wild
forage by snowshoe hares (Lepus americanus) (Morris 2005),
and the ripe fruit left unpicked in a tree by blue (Cercopithecus
mitis) and red-tailed (Cercopithecus ascanius) monkeys
(Houle et al. 2006). In these cases, the measured GUD is
equally considered to reflect the costs associated with the
patch.
Although the framework was originally developed to investigate perceived predation risk while foraging (Brown
1988; Brown et al. 1988; Kotler and Brown 1988), it has since
become widely used as a methodological tool to explore other
components of foraging behavior. Several previous studies
have considered the theoretical strengths and weaknesses of
the foraging theory and the GUD framework (Nonacs 2001;
Behav Ecol Sociobiol (2013) 67:1541–1553
Price and Correll 2001; Olsson 2006). Here, we take a more
practical approach.
We briefly summarize the literature that has employed the
GUD framework in its methodology. Then, we discuss many
of the most critical practical considerations for researchers
using the GUD framework as part of their experimental design. Our goal in highlighting these issues is to enable future
researchers to foresee these potential problems and incorporate suitable protocols to address them. By doing so, we hope
to help speed up the development phase of GUD trial methods
and prevent ambiguity in the interpretation of results.
Addressing these issues should also open opportunities for
addressing interesting new questions using the GUD approach. We finish by offering possible approaches that can
be helpful in tackling these limitations.
How, and how widely, has the GUD methodology been
used?
In order to characterize how the GUD technique has been
used, we examined all research papers listed in Google
Scholar and published up to February 2013 (inclusive) that
cited Joel S. Brown’s 1988 original paper introducing the
GUD framework. Of the 683 papers citing this work, 28 %
(192) used the GUD methodology. Of these 192, 80 % (154)
are not authored by Brown, indicating that the method has
been widely adopted.
Measuring perceived predation risk remains the focus of
most research using the GUD framework (approximately
50 % of GUD papers published to date) (Table 1). Other
authors have used GUDs to explore topics affecting
harvesting costs (C) and missed opportunity costs (MOC),
ranging from the effect of physiological constraints such as
thermoregulation (Bozinovic and Vásquez 1999; Kilpatrick
2003; Orrock and Danielson 2009), immunochallenge
(Schwanz et al. 2011, 2012), food secondary compounds
(Schmidt et al. 1998; Kirmani et al. 2010; McArthur et al.
2012), and parasite loads (Raveh et al. 2011), to the consequence of interspecific and intraspecific competition (Brown
et al. 1997; Abramsky et al. 2001; Ovadia and Zu 2003) and
use of information (van Gils et al. 2003; Stenberg and Persson
2005; Amano et al. 2006; Vásquez et al. 2006) and the risk of
injury (Berger-Tal et al. 2009) while foraging (Table 1). The
typically brief description of the GUD method in these research papers suggests that its application is simple. In practice, it is often not as simple as it appears.
Challenges and opportunities of the GUD framework
Each of the authors of this paper has considered giving
up on the GUD technique due to difficulties in
Behav Ecol Sociobiol (2013) 67:1541–1553
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Harvest rate and energy gain have a curvilinear relationship
Costs
Term
Number of publications
Predation risk
Foraging cost
Missed opportunity cost
Harvest rate
P
C
MOC
H
108
46
31
21
Food toxin cost
Water effect
Foraging benefit of information
Risk of injury
Interference cost
Total publications that use GUD
T
W
FBI
RI
I
5
1
1
1
1
192a
As Brown (1988) originally stated, the patches created for
GUD experiments are commonly not natural, and as such, the
process of harvesting by the forager has particular characteristics that need to be accounted for. Theoretically, the energy
that a forager spends searching in one of these patches increases exponentially with every food item harvested, due to
the dependence of harvest rate on the ratio between inedible
matrix and food (Fig. 1). As a result, it is expected that at the
beginning of a harvesting bout, the energy spent searching is
relatively low and increases “slowly” until it reaches an area of
“rapid increase,” when the food items become scarcer within
the matrix. Fine changes in the perceived cost of foraging by
the individual can only be perceived during the “fast” phase,
making it very difficult, yet nevertheless critical, to create
patches with a ratio of inedible matrix to food that would
ensure this sensitivity (Fig. 1).
However, the harvest rate (H) does not behave in the same
way as the relative energy gained (Fig. 2). The relative energy
gained by a forager per food item decreases while harvesting
the patch. This can be driven by harvesting features such as
different efforts required to obtain different food items (e.g.,
those close to the surface vs. those buried deeply) (Olsson
et al. 2001a). As easily harvested food items are removed,
only the items that are harder to find remain. Moreover,
although the total energy gained while foraging increases,
the value that each new food item represents, given the food
already consumed, decreases exponentially (Fig. 2). This phenomenon is due to the change in energetic state of the forager
as it harvests food. As the individual consumes food, the next
food item becomes less crucial in imparting fitness to the
consumer. Although this is one of the theoretical foundations
of the methodology—since the cost of staying at the patch and
continue harvesting should be ever increasing—it also means
that there is an intimate link between the specific nutritional
a
The total number of publications that have used GUD in their methodology appears lower due to some publications encompassing more than
one term of the GUD equations
resolving a range of challenges; yet, we decided to
persevere and conquer the obstacles, rather than ignoring them and risk compromising the quality of our
research. These challenges arise from vital practical issues
that take time and ingenuity to solve, but that are essential
for correctly interpreting results obtained through this deceptively simple experimental approach. The GUD framework is
underpinned by a suite of assumptions which, if met, allow the
researcher to measure the final GUD and interpret it as a
representation of the study animal’s decision-making process
while foraging. However, the GUD model allows for multiple
inputs into fitness, and there may be many forms of foraging
costs; thus, it is not always easy to track what costs are
changing. As such, it is vitally important that researchers are
explicit about how they approach these challenges and assumptions in their experiments.
It is part of our own initiative to keep track of and share our
experience with the challenges and the best practice solutions
that arose during our own experiments using GUDs. A current
gap in the GUD literature is the recognition of these issues and
the suggestion of solutions or approaches for dealing with
these aspects of the framework. Brown (1988) himself recognized some of the potential limitations of his approach, namely “(i) the patches are not natural, (ii) the resource may not be
appropriate, (iii) the foragers may become satiated, and (iv)
the trays may be visited by more than one forager”. We believe
that there are in fact seven important issues that are rarely
articulated by researchers in their methods, yet are key to
clarity in the interpretation of a GUD. These are (1) the
curvilinearity between harvest rate and energy, (2) the energetic state of the forager, (3) the effects of group foraging, (4)
food quality and substrate properties, (5) the predictability of
the patch, (6) behavioral traits of the forager, and (7) nontarget
species.
Energy spent searching
Table 1 Distribution of uses or focal costs of the giving-up density
framework among the literature to date
Phase where fine
grained Δ in costs
are detectable
Food items harvested
Fig. 1 Conceptual graph showing the dependency between the energy a
forager spends searching and the energy gained through the harvesting
process in a typical giving-up density framework. The energy spent
searching increases exponentially with every food item that it is harvested
due to the decrease in food to inedible matrix ratio. The phase where fine
changes in the perceived cost of foraging are measurable is shown in the
shaded area
Behav Ecol Sociobiol (2013) 67:1541–1553
Relative energy gained
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Food items harvested
Fig. 2 Conceptual graph showing the relative energy gained by a forager,
per food item, while harvesting. Each new food item harvested is of
comparatively less value than the previous one
requirements of the forager exploiting the patch and the GUD.
However, in some cases, animals may not change energetic
state considerably while exploiting a patch, for example,
animals that cache food such as gray squirrels (Sciurus
carolinensis) (Schmidt and Ostfeld 2008), where the value
of the food is of future value, minimizing and even eliminating
changes in their energetic state while foraging.
In cases where the energetic state of the forager does
change considerably, diminishing returns may be less important since while H may not vary, associated costs such as C + P
+ MOC can increase, resulting in GUDs that are still meaningful. However, the change in relative energy gained during
foraging by one individual (and between individuals, see
below) can bias the measured GUD towards those individuals
whose state drives them to exploit the patch most thoroughly.
This can influence our interpretation of the data, especially in
nonmanipulative experiments when comparisons between
populations are being made.
Forager state will affect giving-up density
Forager state affects the relative benefits obtained from food
items. A classic example is the state-dependent energetic
benefits obtained from a meal in vampire bats (Desmodus
rotundus) (Wilkinson 1984). Wilkinson (1984) found that
individual vampire bats that were successful in night feeding
could afford to share their meal with starved conspecifics
because the relative gain in survival for the starved bats was
orders of magnitude higher than the cost incurred by the fed
bats sharing their meals. Since foragers would perceive the
patch differently according to the amount of food they have
already consumed, there is uncertainty in the relative value of
the patch for that forager, and yet, this value is assumed to be
represented in the final GUD obtained at that patch.
Brown (1992) defines the energetic state of the forager and
its marginal value of energy as dF/de, where F represents
fitness and e net energy, and theoretically demonstrated how
it can affect a forager’s response to predation risk (P) and
missed opportunity costs (MOC). Subsequently, this has been
demonstrated empirically. For example, starved Anderson’s
gerbils (Gerbillus andersoni allenbyi) and Egyptian fruit bats
(Rousettus aegyptiacus) tend to leave lower GUDs than
nonstarved animals, presumably because food items are more
beneficial to animals in a low energy state (Sánchez et al.
2008a; Berger-Tal and Kotler 2010; Berger-Tal et al. 2010).
Similarly, deer mice (Peromyscus maniculatus) (Morris 1997;
Davidson and Morris 2001) and house mice (Mus musculus
domesticus) (Ylönen et al. 2002) had lower GUDs when in
higher densities, argued to be a consequence of the reduction
of the forager’s energetic state due to high competition (i.e.,
environmental food shortage).
Forager state also includes other factors besides satiation,
such as a forager’s development, physiology, and reproductive
state. These states can alter the relative value of the energy
gain in a particular patch for a particular individual forager
and, as such, alter the GUD that we measure. For example,
pink salmon (Oncorhynchus gorbuscha) (Webster et al. 2007)
and Anderson’s gerbils (Raveh et al. 2011), when infected
with ectoparasites, reduce their harvest rate and time spent
foraging, allocating more time to parasite removal behaviors
and, therefore, increasing their GUDs. Immunochallenged
white-footed mice show lower GUDs and less patch selectivity, argued to be a result of the increase in energetic demands
for anti-infection metabolism (Schwanz et al. 2011, 2012). In
spring, during the reproductive season, Anderson’s gerbils and
greater Egyptian gerbil (Gerbillus pyramidurn) show lower
GUDs compared to in summer when they are not reproductive, presumably because they favor energy that can go into
reproduction against safety (Kotler et al. 2004).
Other aspects of an individual state, such as personality and
age, may also be capable of altering GUD. Younger animals
may have higher energy requirements for growth (Randall
et al. 2002); thus, they may perceive patches as higher in
value. Similarly, bolder animals may perceive a lower effect
of predation risk and be more liable to stay at a foraging patch
longer (Réale and Festa-Bianchet 2003). These elements are
yet to be included in the GUD equation.
An individual’s state matters because, as identified by
Brown (1988), while patches can be visited by more than
one forager, the GUD only measures the decision made by
the last forager at the patch (or the lowest GUD). It follows
then that GUDs may not be representative of the entire population of foragers, but be biased towards those individuals
whose state drives them to exploit the patch most thoroughly.
Understanding the state of those final foragers is therefore
very important for interpreting the final GUD. This is particularly important in nonmanipulative experiments where the
aim of the GUD experiment is to detect differences between
populations occupying different locations. Populations can
vary in their age structure, sex ratio, reproduction, etc., and
the locations in which they occur can also vary in their food
abundance or predation risk. If the state of the forager is not
Behav Ecol Sociobiol (2013) 67:1541–1553
considered, it would be easy to confound differences in their
marginal value of energy with changes in perceived predation
risk or indeed many other questions of interest.
Group foraging
Given that patches can be visited by more than one individual,
multiple foragers might simultaneously visit a patch. This has
implications for the perceived cost of that patch for individuals
and the amount of food that is left unharvested. Group foraging can reduce an individual’s net predation risk, either because risk is diluted should a predator attack (Hamilton 1971),
individuals need not be as vigilant to detect predators when
foraging in a group (the “many eyes” effect) (Pulliam 1973),
the group can defend a food patch as a unit (group defense)
(Alexander 1974; Hoogland and Sherman 1976), predators
may be unable to single out and attack a single animal in the
group (confusion effect) (Milinski 1977a, b; Landeau and
Terborgh 1986), and/or by offering individuals opportunities
to take advantage of the “selfish herd” effect and shield
themselves against predators (Hamilton 1971). The final
GUD may therefore represent the most intense bout of foraging by an earlier group, rather than the decisions of the last
forager. However, simultaneous same species exploitation of a
patch more commonly leads to interspecific competition.
Competition in a patch ultimately decreases the value of that
patch (increasing GUDs) by either increasing the cost of
foraging (C)—risk of injury while fighting conspecifics (also
known as cost of interference I, see earlier)—or decreasing the
cost of foraging elsewhere (higher MOC) where competitors
may be less abundant. Territorial red squirrels (Tamiasciurus
hudsonicus), for example, lower their GUDs when territory
intruders are removed, which may imply that territorial defense constrains their foraging (Vlasman and Fryxell 2002).
Similarly, when densities of Anderson’s gerbils are high, there
is an increase in aggressive interactions that interferes with
foraging, ultimately increasing GUD (by reducing time foraging) even when patch quality is high (high food abundance)
(Ovadia and Zu 2003). Further, males of this species visit food
patches early during the night and deny access of females to
high quality patches; thus, females are forced to exploit low
quality patches (decreasing GUDs on those patches) when
males are present (Kotler et al. 2005). In birds, spice finches
(Lonchura punctulata) are able to gather more seeds (lower
GUD) when foraging alone than in large numbers, argued to
be a consequence of shorter time foraging in groups due to
social cohesiveness (Livoreil and Giraldeau 1997).
As in same species groups, when individuals from different
species visit the patch, creating mixed species groups, predation risk is theoretically lowered and so is GUD (Pulliam
1973; Powell 1985; Thiollay 1999). However, in practice,
interspecific competition is more frequent, leading to aggressive interactions and altering the perceived cost of the patch,
1545
thus increasing GUD. For example, sympatric Anderson’s
gerbils and greater Egyptian gerbils (G. pyramidurn) engage
in aggressive interactions that interfere with foraging (increasing GUD), and this interference is stronger when competition
between species is higher (high abundance of food in patches
and high densities of both species), which further increases
GUD (Ovadia and Zu 2003). Further, the greater Egyptian
gerbil competes mostly with male Anderson’s gerbils, indirectly aiding females to overcome males’ monopolization of
feeding patches (Ovadia and Zu 2003). However, Anderson’s
gerbils GUD can also show lower GUDs when the greater
Egyptian gerbil is present, presumably because the latter
lowers food abundance which in turn increases the
Anderson’s gerbil’s marginal value of energy (lowering P)
(Ziv and Kotler 2003).
There are other examples of interspecific interactions affecting GUDs: nocturnal Cairo spiny mice (Acomys cahirinus)
impede nocturnal foraging by golden spiny mice (Acomys
russatus), forcing the latter to forage more intensively during
the day and increasing nocturnal GUD (Gutman and Dayan
2005). Olivaceous field mice (Akodon olivaceus) show lower
GUD when competitor species, degus (Octodon degus) and
Darwin’s leaf-eared mice (Phyllotis darwini) are excluded,
and for these species, competition has a stronger effect than
the presence of predators (Yunger et al. 2002). In contrast,
blue monkeys (Cercopithecus mitis) are aggressive towards
red-tailed monkeys (Cercopithecus ascanius), yet they are
also less efficient at exploiting fruiting trees (higher GUD),
allowing red-tailed monkeys to coexist by exploiting these
trees more thoroughly (lower GUD) when both species are
present (Houle et al. 2006). In a similar way, southern redbacked voles (Myodes gapperi) forage more intensively in
their preferred habitat type (decreased GUD) in the presence
of meadow voles (Microtus pennsylvanicus), presumably to
avoid competition in alternative food patches (Morris 2009).
Evidently, the perceived cost of foraging in a particular
patch is influenced by the density of the target species and
other sympatric species, but also depends on the biology and
ecology of the interacting species. More aggressive and territorial species may defend feeding patches, while submissive
species may opt to forage elsewhere to avoid injury through
aggressive interactions. Therefore, there is a need for greater
understanding of exactly which individuals are visiting food
patches and contributing to the GUD value. If we do not
consider the abundance of the study species and the presence
of other sympatric species, these factors may confound our
interpretation of differences in GUD between sample
populations.
Food and substrate qualities
Characteristics intrinsic to the experimental setup can also
affect the GUD obtained. Since patches are artificial, it is
1546
crucial to take into account how the food resource and inedible
substrate can affect the way a forager perceives the artificial
patch. Physical and chemical characteristics of the food have
been shown to alter how patches are perceived by altering the
cost of foraging (C). Increasing nutrient content decreases the
GUD left by both large (Kotler et al. 1994; Hochman and
Kotler 2006) and small mammals (Brown and Morgan 1995;
Leaver and Daly 2003). On the other hand, plant chemical
defenses have the opposite effect (i.e., increasing GUD) for
small mammals (Schmidt et al. 1998; Fanson et al. 2010), bats
(Sánchez et al. 2008a, b), primates (McArthur et al. 2012), and
marsupials (Bedoya-Pérez et al., unpublished). Foragers are
selective about the type and quality of the food they harvest.
For example, the sympatric rock elephant shrew (Elephantulus
myurus) and Namaqua mouse (Micaelamys namaquensis)
show species-specific preferences (lowest GUD) for particular
food and substrate combinations (mealworms in pebbles for
shrews and millet seeds in sand for mice), and these preferences are explained by morphological characteristics that promote coexistence by defining their ecological niche (Abu
Baker and Brown 2012). Food preferences can also change
across time, for example, village weavers (Ploceus cucullatus)
switch preferences between millet seeds and peanuts according
to the season, argued to be a consequence of an increase in
energy requirements for breeding (Molokwu et al. 2011).
Physical characteristics of food items influence handling
time and possible future benefits of different types of food,
thus shaping GUD. For example, fox squirrels (Sciurus niger)
show lower GUD in patches that have been supplemented with
storable food (unshelled hazelnuts) compared to patches
supplemented with nonstorable food (shelled hazelnuts), and
this is a consequence of the perceived future value of the food
(Kotler et al. 1999). Larger seeds are preferred (lower GUD) by
desert gerbils because of their higher encounter rates compared
with small seeds, regardless of higher handling time efficiency
on smaller seeds (Garb et al. 2000). Swamp wallabies show
lower GUD when offered food pellets with low concentration
of a volatile plant terpene compared to pellets without the
volatile terpene, due to the reduction in handling time achieved
when using odor while searching (Bedoya-Pérez et al.,
unpublished). Handling efficiency can also be a speciesspecific trait. Thick-billed weaver (Amblyospiza albifrons),
for example, show lower GUD for a wider range in seed sizes
than do four other sympatric species, except the smaller seeds,
while bronze mannikins (Spermestes cucullatus) showed the
opposite trend, with lower GUD when harvesting for the
smaller seed size (Soobramoney and Perrin 2008).
Substrate also greatly influences the perceived cost of
foraging; as a result, it is critical to test different foraging
matrix substrates for each new target species and environment,
which can often lead to delays in protocol development. The
substrate creates the decline in harvesting rate as a patch is
depleted. If this decline occurs too rapidly, or all food items
Behav Ecol Sociobiol (2013) 67:1541–1553
are removed, the GUD obtained is rendered useless. Several
studies have highlighted substrate effects on GUD. For instance, several gerbil species show lower GUD when sand is
used instead of rocks or loess (Kotler et al. 2001). Moreover,
the ratio between substrate and food can also alter GUD;
higher food densities result in lower GUD for desert kangaroo
rats (Dipodomys deserti) (Podolsky and Price 1990) and lesser
GUD for spotted woodpecker (Dendrocopos minor) (Olsson
et al. 1999; Olsson et al. 2001b), while bream (Abramis
brama) leave higher GUD under this scenario (Persson and
Stenberg 2006; Stenberg and Persson 2006). Similarly, water
birds increase GUD at greater water depths (Gawlik 2002;
Nolet et al. 2006).
In light of these issues, making methodological decisions
about food quality and physical characteristics, substrate type,
and food-to-matrix ratios can be a titanic task for researchers.
For example, high quality food presented in an easily searchable substrate can potentially mask the effects of predation
risk (P) and missed opportunity costs (MOC) by increasing
the perceived value of the patch (reducing foraging cost C).
On the other hand, low quality food may not provide sufficient
benefit to foragers to outweigh theses costs (MOC and P),
resulting in low rates of patch visitation (and hence low
numbers of replicates for researchers). Under such a scenario,
it becomes difficult to determine whether patches are not
harvested due to high perceived costs or because they were
not encountered by foragers at all. Nevertheless, the GUD
framework offers flexibility in its application, providing a
variety of ways of solving these problems. For example,
presenting an array of food items, either naturally occurring
(Lortie et al. 2000) or not, can help determine which is
appropriate for subsequent experimentation. Other components of the system can also be manipulated, such as the
dimensions of the feeder and the type of substrate.
The predictability of the patch (the “magic pudding” effect)
In Norman Lindsay’s (1918) iconic Australian children’s tale,
the Magic Pudding is a pie that—no matter how often it is cut
and a slice taken—magically reforms, allowing its owner to
“cut-and-come-again.” For foragers in GUD experiments,
artificial food patches have similar “magical” properties. In
nature, some food patches (e.g., sand dunes seed patches) may
partly replenish daily (Kotler et al. 2002), but others (e.g.,
shrubs, pasture, or fruiting trees) do not. GUD experiments
typically entail the consecutive (daily) replenishment of artificial patches, creating an unnatural predictability to the patch
in terms of its quality, location, and periodicity that could all
have a marked effect on the GUD by decreasing how the
forager perceives the associated costs (C and MOC). If a
forager is able to predict the profitability and the location of
a food patch, and the spatial distribution of several patches in
the surrounding area, MOC and C may be perceived more
Behav Ecol Sociobiol (2013) 67:1541–1553
accurately, yet not necessarily reflecting how these parameters
are perceived when foraging in naturally occurring patches
that may not be predictable. Local foragers may learn about
these artificial patches and begin to exploit them more thoroughly, whereas a less predictable (i.e., natural) patch is harder
to assess and tends to be either under- or overexploited
(Valone 1991; Kohlmann and Risenhoover 1998; van Gils
et al. 2003; Vásquez et al. 2006).
Having the possibility of manipulating temporal and spatial
predictability during GUD experiments can be advantageous
if the aim is to determine how a forager uses previous information during foraging (e.g., recognition time or Bayesian
foraging). For example, Inca doves (Columbina inca), bobwhite (Colinus virginianus), red knots (Calidris canutus), and
degus use past experience to assess the quality of a patch that
they have exploited previously (Valone 1991; Kohlmann and
Risenhoover 1998; van Gils et al. 2003; Vásquez et al. 2006).
Highly predictable food sources increase the value of foraging
and allow the forager to efficiently allocate time to other
fitness-enhancing behaviors, partitioning their activity patterns accordingly. Diurnal lesser spotted woodpeckers, for
example, feed on highly predictable wood-living insects in
dead tree branches, allowing them to allocate more time to
foraging in the afternoon (lower GUDs) and spend less time
feeding when food abundance increases (thus increasing the
marginal value of energy) (Olsson et al. 2000). On the other
hand, several other species such as the Namib desert gerbil
(Gerbillus tytonis) (Hughes et al. 1995) and goldfish
(Carassius auratus) (Stenberg and Persson 2005) do not vary
GUDs in response to patch predictability.
However, where the aim is not linked to the use of information during foraging, then the magic pudding effect must be
taken into account; otherwise, predictable patches can misrepresent natural foraging behaviors. How the target species
responds to patch predictability may not be known prior to
setting up a GUD experiment and can potentially shape GUD
results in unexpected ways. Understanding the patterns of
patch visitation by individuals can help identify any potential
magic pudding effects and help interpret GUD results.
Behaviors that can affect harvest rates
Foragers are capable of employing complex behavioral strategies to overcome the foraging costs associated with a particular patch in order to maximize their fitness (Brown 1999).
For example, Anderson’s gerbils use antipredator vigilance to
limit their exposure to predation risk (reducing P) at the patch,
and when vigilance is impeded, their GUDs increase (Embar
et al. 2011). On the other hand, when sight lines are blocked,
Nubian ibex (Capra nubiana) increase their vigilance rates
and their GUDs (Iribarren and Kotler 2012). The differences
between ibex and gerbils can be explained by the efficiency of
vigilance at reducing predation risk; vigilance by ibex is more
1547
effective at reducing predation risk than that of gerbils (Brown
1999). In a different example, Cotton rats (Sigmodon
hispidus) and the eastern chipmunk (Tamias striatus) eavesdrop on other species alarm calls while foraging to reduce
their own predation risk (again reducing P); this enables them
to leave low GUDs while avoiding the costs of vigilance
(Schmidt et al. 2008; Felts and Schmidt 2010). Similarly, deer
mice alter their food handling strategies (increasing H) in
ways that allow them to leave low GUDs even in patches with
high cost of injury by fire ants (high C) (Holtcamp et al. 1997).
As a consequence of the flexibility in behavioral traits shown
by foragers, patches with different perceived value might
actually be exploited to the same level (equal GUD), and
researches may not recognize the real effects that represent
characteristics of the patch.
Nontarget species
Nontarget species foraging from patches is an inconvenient
and almost inevitable complication when setting up GUD
experiments in a natural environment. The appeal of the
GUD methodology is its ability to measure the decisions of
free-living animals, but the drawback is that foraging stations
may also be available for many species to exploit. However,
there is little mention in the GUD literature of how to deal with
nontarget species. Most commonly, GUD papers report results
only for targeted species, disregarding, and in some case not
even mentioning, other species that may have visited the
artificial patch; approximately 58 % of GUD papers published
to date only reported one species using the patches. The
development of strategies to deter nontarget species while
maintaining access to the patch for target fauna can prove
extremely challenging. Mechanical barriers are not always
effective. Accounting for the degree of visitation made by
nontarget species in statistical models can sometimes be the
only option, although not all species leave signs of visitation
and it is difficult to ensure we detect all species that visit the
patches.
The seven issues described above illustrate how the final
measured GUD reflects many influential processes which
most researchers do not explicitly address when describing
experimental GUD methodology.
Solutions and additional techniques
One important approach to determine the ultimate causes
behind a GUD is to use ancillary measures of forager responses to the experimental setup. A large proportion of all
GUD studies to date (approx. 31 %) supplemented their work
with additional techniques in order to gather information that
allows broader ecological hypotheses to be tested (Table 2).
1548
Behav Ecol Sociobiol (2013) 67:1541–1553
Table 2 Supplementary techniques and approaches used alongside the giving-up density framework
Aspect addressed
Supplementary
technique
Species
Reference
Abundance
estimation
Trapping
Anderson’s gerbils (Gerbillus allenbyi)
Gideon et al. (2005), Kotler et al.
(2005), Wasserberg et al.
(2007), China et al. (2008)
Persson and Stenberg (2006),
Stenberg and Persson (2006)
Kovacs et al. (2012)
Dickman et al. (2011)
Pickett et al. (2005)
Kovacs et al. (2012)
Perrin and Kotler (2005)
Yunger et al. (2002)
Morris (1997), Davidson and
Morris (2001), Reed et al.
(2005), Rosemier and
Storer (2010)
Yunger et al. (2002)
Rosemier and Storer (2010)
Reed et al. (2005)
Abu Baker and Brown (2010)
Gideon et al. (2005),
Wasserberg et al. (2007),
China et al. (2008)
Reed et al. (2005)
Reed et al. (2005)
Ylönen and Ronkainen (1994),
Arthur et al. (2004)
Caccia et al. (2006)
Bream (Abramis brama L.)
Brown antechinus (Antechinus stuartii)
Brushed-tailed mulgara (Dasycercus blythi)
Brushtail possum (Trichosurus vulpecula)
Bush rat (Rattus fuscipes)
Bushveld gerbil (Tatera leucogaster)
Darwin’s leaf-eared mouse (Phyllotis darwini)
Deer mouse (Peromyscus maniculatus)
Degu (Octodon degus)
Eastern chipmunk (Tamias striatus)
Eastern wood rat (Neotoma floridana)
Grass mouse (Rhabdomys pumilio)
Greater Egyptian gerbil (Gerbillus pyramidurn)
Hispid cotton rat (Sigmodon hispidus)
Hispid pocket mouse (Chaetodipus hispidus)
House mouse (Mus musculus domesticus)
Long-tailed pygmy rice rat
(Oligoryzomys longicaudatus)
Meadow jumping mouse (Zapus hudsonicus)
Merriam’s kangaroo rat (Dipodomys merriami)
Oldfield mice (Peromyscus polionotus)
Olivaceous field mouse (Akodon olivaceus)
Olive grass mouse (Abrothrix olivaceus)
Ord’s kangaroo rat (Dipodomys ordii)
Prairie vole (Microtus ochrogaster)
Red squirrels (Tamiasciurus hudsonicus)
Sandy inland mouse (Pseudomys hermannsburgensis)
Southern red-backed vole (Myodes gapperi)
Spinifex-hopping mouse (Notomys alexis)
Striped mouse (Rhabdomys pumilo)
Thirteen-lined ground squirrel (Spermophilus
tridecemlineatus)
Western harvest mouse (Reithrodontomys megalotis)
White-footed mouse (Peromyscus leucopus)
Activity estimate
Photographic records
(field)
Radio tracking
Mule deer (Odocoileus hemionus)
Anderson’s gerbils (G. allenbyi)
Reed et al. (2005)
Herman and Valone (2000)
Orrock and Danielson (2005)
Yunger et al. (2002)
Caccia et al. (2006)
Stapp and Lindquist (2007)
Reed et al. (2005)
Vlasman and Fryxell (2002)
Kotler et al. (1998), Dickman
et al. (2011)
Andruskiw et al. (2008), Rosemier
and Storer (2010)
Dickman et al. (2011)
Perrin and Kotler (2005)
Reed et al. (2005)
Reed et al. (2005)
Schmidt and Ostfeld (2003),
Reed et al. (2005), Rosemier
and Storer (2010)
Altendorf et al. (2001), Hernández
et al. (2005)
Kotler et al. (2005), Wasserberg
et al. (2007)
Behav Ecol Sociobiol (2013) 67:1541–1553
1549
Table 2 (continued)
Aspect addressed
Supplementary
technique
Sand plots
Trapping
Video recording (field)
Behavioral responses
Passive inductive
transponder (PIT) tags
Video recording (captive)
Forager identity
Growth measurements
Habitat use
Species
Reference
Greater Egyptian gerbil (G. pyramidurn)
Anderson’s gerbils (Gerbillus allenbyi)
Bushveld gerbil (T. leucogaster)
Greater Egyptian gerbil (G. pyramidurn)
Striped mouse (R. pumilo)
Cairo spiny mouse (Acomys cahirinus)
Common voles (Microtus arvalis)
Golden spiny mouse (Acomys russatus)
Wasserberg et al. (2007)
Wasserberg et al. (2007)
Perrin and Kotler (2005)
Wasserberg et al. (2007)
Perrin and Kotler (2005)
Gutman and Dayan (2005)
Jacob and Brown (2000)
Gutman and Dayan (2005)
Natal multimammate mouse
(Mastomys natalensis)
Bank voles (Myodes glareolus)
Gray red-backed vole (Myodes rufocanus)
Anderson’s gerbils (G. allenbyi)
Anderson’s gerbils (G. allenbyi)
Bank voles (M. glareolus)
Goldfish (Carassius auratus)
Greater Egyptian gerbil (G. pyramidurn)
Mohr et al. (2003)
Video recording (captive)
Vocalization playbacks
Red fox (Vulpes vulpes)
Eastern chipmunk (T. striatus)
Eastern tufted titmouse (Baeolophus bicolor)
Behavioral observations
Trapping
Trapping (GIS)
Radio tracking
Village weavers (Ploceus cucullatus)
Cottid fish (Clinocottus acuticeps)
Grass mouse (R. pumilio)
Pygmy gerbil (Gerbillus henleyi)
Red squirrels (T. hudsonicus)
Brushtail possum (T. vulpecula)
Bush rat (R. fuscipes)
Slender-tailed dunnart (Sminthopsis murina)
Yellow-footed antechinus (Antechinus flavipes)
Fox squirrel (Sciurus niger)
Deer mouse (P. maniculatus)
Degu (O. degus)
Golden-mantled ground squirrel
(Callospermophilus lateralis)
Least chipmunk (Tamias minimus)
Cairo spiny mouse (A. cahirinus)
Golden spiny mouse (A. russatus)
Water voles (Arvicola terrestris)
Bush rat (R. fuscipes)
Anderson’s gerbils (Gerbillus allenbyi)
Cairo spiny mouse (A. cahirinus)
Golden spiny mouse (A. russatus)
Sand plots
Spool-and-line
Tracking tunnels
(fluorescent dye)
Handling time
Behavioral observations
Harvesting speed
Video recording (captive)
Behavioral tests
Morphological adaptations
Retinal histology
Mortality estimate
Age structure
Searching pattern
Stress levels
Radio tracking
Trapping
Video recording (captive)
Hormone essay
Pilot studies
The importance of pilot studies cannot be overemphasized and
must be factored in to any plan for GUD experimentation. No
matter how simple the GUD technique might appear, in our
Trebatická et al. (2008)
Trebatická et al. (2008)
Kotler et al. (2010)
Ovadia and Zu (2003)
Liesenjohann and Eccard (2008)
Stenberg and Persson (2005)
Ovadia and Zu (2003)
Berger-Tal et al. (2009)
Schmidt et al. (2008)
Schmidt et al. (2008)
Molokwu et al. (2011)
Alofs and Polivka (2004)
Abu Baker and Brown (2010)
Abramsky et al. (2005)
Vlasman and Fryxell (2002)
Pickett et al. (2005)
Strauβ et al. (2008)
Stokes et al. (2004)
Stokes et al. (2004)
Kotler et al. (1999)
Holtcamp et al. (1997)
Bozinovic and Vásquez (1999)
Smith (1995)
Smith (1995)
Kronfeld-Schor et al. (2001)
Kronfeld-Schor et al. (2001)
Carter and Bright (2003)
Spencer et al. (2005)
Dall et al. (2001)
Gutman et al. (2011)
Gutman et al. (2011)
experience, it has never worked immediately and has required
some period of tweaking the technique before the main experimental data could be collected. The preliminary information that can be derived from pilot studies allows researchers
to calibrate the type of food, the type of substrate, and the
1550
overall structure of the feeding station (e.g., tray, container,
box, and so on). In combination with techniques such as video
recording, pilot studies can also clearly identify which species
are foraging in artificial patches and must be further considered as the methodology is developed.
Pilot studies help to fine-tune GUD experiments in order to
prevent common initial problems such as GUDs reaching zero
or a lack of visitation at feeding stations. If feeders are not
visited, increasing the quality or the size of the food offered, or
decreasing the amount of substrate, will decrease the costs of
foraging (C) and the MOC, making the feeding stations more
attractive to foragers. On the other hand, if GUDs reach zero,
the perceived benefits of the patch (C) may currently be too
high—they can be reduced by increasing the amount of inedible matrix, reducing the amount of food offered, or decreasing the quality or size of the food, or as with goats (Capra
hircus) and klipspringers (Oreotragus oreotragus), adding a
physical obstacle (wires and fencing) in feeding stations that
makes foraging more difficult and can help prevent foragers
spilling inedible matrix from the feeder, thereby keeping the
volume constant (Shrader et al. 2008a, b; Druce et al. 2009).
This is an important consideration to ensure diminishing
returns with continued foraging. In summary, pilot studies
are the first practical step to measure and account for specific
characteristics of the target species and the system in which
the GUD framework will be applied; however, it is not a
solution per se, rather it is an approach that enables researchers
to refine the method for their research question and system.
Trapping
Trapping has often been used in combination with GUDs to
estimate local population density of the target species and to
survey the available pool of potential foragers. These data can
help researchers to assess the potential for intra- and interspecific interactions that might influence the GUD (Kotler et al.
1998; Reed et al. 2005; Kovacs et al. 2012). Trapping has also
been used to estimate a population’s age structure (Spencer
et al. 2005) and individual’s growth and fitness (Alofs and
Polivka 2004), which, as we have discussed, might bias
sampling if GUDs are left by individuals with the highest
energetic requirements (i.e., juveniles, reproductive and/or
sick animals). Trapping also allows information to be gathered
about the nontarget species in the area. Thus, the information
gained through trapping conducted concurrently or prior to a
GUD experiment can be incorporated into the analysis and
interpretation of GUD results.
Tracking
Tracking (e.g., GPS or radio transmitters) is another common
addition to GUD experiments, used to measure activity patterns, time partitioning (Kotler et al. 2005; Wasserberg et al.
Behav Ecol Sociobiol (2013) 67:1541–1553
2007), and habitat use (Vlasman and Fryxell 2002; Strauβ
et al. 2008), which can help address potential effects of interand intraspecific competition. Tracking forager activity patterns may identify whether group foraging occurs, but tracking can also reveal the identity of the last forager at a tray,
potentially revealing the link between GUD and individual
energetic requirements (i.e., age, health, reproductive state).
Moreover, real time spent at the patch can be calculated and
compared with the GUD in order to determine harvesting
efficiency and account for any changes that may have been
achieved through behavioral strategies. For example, Kotler
et al. (2010) was able to calculate harvest rate and construct
harvest rate curves by using time spent in the patch by individuals tracked through the use of passive integrated transponder (PIT tags). Nonintrusive tracking, such as sand plots, have
been used to determine forager species identity, nontarget
species, and activity patterns (Perrin and Kotler 2005;
Pickett et al. 2005).
Behavioral observations
In our opinion, only direct behavioral observations can address the consistent issues of who is visiting and how they
allocate their time at the patches to create the final measured
GUD. By observing foragers as they exploit the patch, we can
measure an entirely new set of data that provides context to the
GUD value obtained at the end of an experiment. The value of
behavioral observations, taken alongside GUDs, has been
demonstrated repeatedly through the use of direct observation
(Holtcamp et al. 1997; Kotler et al. 1999; Molokwu et al.
2011), video recordings of captive animals (Smith 1995; Dall
et al. 2001; Ovadia and Zu 2003), and importantly, in field
experiments (Mohr et al. 2003; Bytheway et al. 2013).
Infrared game cameras are being widely adopted in biodiversity surveys and offer the opportunity to capture detailed
behavioral observations of animals at GUD patches.
Cameras with remote video recording capabilities offer a
cost-effective, nonintrusive opportunity to identify which individuals are visiting GUD patches and what they do there in
order to demystify the final GUD value.
Conclusions
Even though applying the GUD framework for the first time can
be frustrating and discouraging, it continues to offer an elegant
and powerful tool to assess an almost infinite array of questions
in foraging and predation ecology. However, researchers need to
account for its potential limitations and realize that by addressing
these limitations, they are opening up opportunities for new and
interesting research to decipher how foraging decisions are
made. Novel and fascinating questions have already been asked
using the GUD, including how foraging partitioning (diurnal vs.
Behav Ecol Sociobiol (2013) 67:1541–1553
nocturnal) is linked with eye morphology (Kronfeld-Schor et al.
2001), how stress levels due to predation risk affect foraging
(Gutman et al. 2011), and how eavesdropping on interspecific
alarm calling affects time invested exploiting a patch (Schmidt
et al. 2008), just to name a few.
We do not suggest that researchers give up on the giving-up
density framework and methodology, but we do think that
there is a greater need to identify how these limitations might
affect experimental GUD results. The addition of cameras to
the GUD method offers a valuable solution that addresses
many of the issues we have raised here. This is because it
allows researchers to unobtrusively observe the animals at the
patch and attempt to relate the behavior displayed and the
process of foraging to the final measured GUD, something
which was not possible before remote sensing night vision
cameras became an affordable option for field ecologists. We
exhort our fellow researchers struggling with GUD methods
in the field to work through the issues using some of our
suggested solutions above and to explicitly address their solutions and approaches to these issues when describing their
methodology in future publications.
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Oikos 118: 1721
1731, 2009
doi: 10.1111/j.1600-0706.2008.17473.x,
# 2009 The Authors. Journal compilation # 2009 Oikos
Subject Editor: Thomas Valone. Accepted 7 May 2008
Patch area, substrate depth, and richness affect giving-up densities:
a test with mourning doves and cottontail rabbits
Mohammad A. Abu Baker and Joel S. Brown
M. A. Abu Baker (mabuba2@uic.edu) and J. S. Brown, Dept of Biological Sciences, Univ. of Illinois at Chicago, 845 W. Taylor St.
(M/C 066), Chicago, IL 60607, USA.
We compared the foraging behavior of mourning doves Zenaida macroura and cottontail rabbits Sylvilagus floridanus in
patches that varied in initial food abundance, surface area and substrate depth. We measured giving-up densities (GUD),
food harvest and proportion of food harvested to investigate their ability to respond to characteristics of resource patches.
GUDs have been analyzed in three ways: grams of per patch, grams per unit surface area (GUDAREA), and grams per unit
volume of sand (GUDVOL).
Mourning doves and cottontails exhibited similar responses to resource density and sand depth. Both foragers detected
and responded to variation in initial food abundance. The proportion of food harvested from a patch increased from
40.7, 43.8 to 48.3% (for the doves) and 34.9, 35.8 to 38.4% (for the rabbits) in patches of low, medium and high initial
food abundance, respectively. Deeper substrates reduced the foragers’ encounter probability with food, decreased patch
quality and resulted in higher GUDs (60% higher in the deepest relative to shallowest substrate) and lower harvests. A
significant interaction between initial food abundance and substrate depth showed that both species were willing to dig
deeper in patches with higher resource density. Patch size (surface area) had no effect on food harvest or the proportion of
food harvested. Consequently, GUDAREA and GUDVOL increased in patches with a smaller surface area. Smaller patches
appeared to hamper the dove’s and cottontail’s movement across the surface.
Our results revealed that mourning doves and cottontails forage under imperfect information. Both species were able to
respond to patch properties by biasing their feeding efforts toward rich and easy opportunities, however, mourning doves
were more efficient at food harvesting.
The interaction of patch area, volume and food abundance directly influenced food harvest. Such resource characters
occur under natural situations where food varies in abundance, area of distribution, and accessibility.
Food harvest represents an interplay between resource
characteristics and the animals’ ability to respond to these
characteristics. Diet choice, habitat selection and patch use
are the three main contexts of foraging theory aimed at how
feeding animals exploit opportunities and avoid hazards
(Stephens et al. 2007). Patch use theory considers how
much effort a forager should devote to depleting the
resources of a localized area before moving on in search
of a new ‘patch’ (Charnov 1976, Stephens and Krebs 1986).
Patch use applies to circumstances where foragers detect and
bias their efforts towards spatial aggregations of resources. A
forager may increase its benefits from the degree of
aggregation and its ability to detect and respond to these
aggregations (Brown 2000). Foragers should travel through
poor patches and harvest rich ones in order to maximize
their fitness rewards (Stephens and Krebs 1986). These
rewards may take the form of energy, safety (Sih 1980,
Brown and Kotler 2004), trace nutrients (Pulliam 1975),
and/or variance of intake rates (Caraco 1980).
Several patch characteristics are known to influence
resource harvest and forager effort (see Meyer and Valone
1999 for an example with multiple foraging costs). First and
foremost is initial food abundance. Foragers should aim to
harvest more from rich patches than poor. Yet, information
constraints may cause foragers to overutilize poor patches
while underutilizing rich patches (Valone and Brown 1989,
Valone 2006). Other patch characteristics include the type
of food (Brown and Morgan 1995), the type of substrate
from which the forager must detect and extract resources
(Price and Heinz 1984, Price and Podolsky 1989, Kotler et
al. 2001), climate (Kilpatrick 2003) and predation risk
(Brown et al. 1988). Less investigated, yet of likely
importance are substrate depth (Nolet et al. 2006), and
patch area (Schmidt and Brown 1996). Furthermore, what
happens when several factors simultaneously influence the
properties of a single patch? Here, we investigate simultaneously three of these important factors: initial food
abundance, substrate depth, and patch area. We show
how several factors acting together yield a more sophisticated and diverse set of predictions. The actual response of
the foragers then yields insights into how they assess and
respond to resource heterogeneity. We investigate changes
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in giving-up density (GUD), cumulative harvest, and
proportion of food harvested from patches that vary in
elements that create diminishing returns in natural situations, namely: area, substrate depth and initial food
abundance.
Foraging strategies and sensory perceptions such as visual,
olfactory and tactile cues allow for varying degrees of
information acquisition. Foragers use this information to
assess and respond to heterogeneity in prey abundance,
patch size and volume. For instance, the long beaks of storks,
the long-mobile snouts of elephant shrews, olfactory sense of
kangaroo rats and digging abilities of gerbils may allow them
to forage deeper in substrates while de-emphasizing the role
of vision. Whereas diurnal seed-eating birds may rely on
vision to find seeds, enhancing seed harvest from the surface
at the expense of seeds deeper in the substrate (Kotler and
Brown 1999, Vander Wall et al. 2003, Skinner and
Chimimba 2005).
Varying patch attributes has been used to investigate
environmental features that influence a forager’s patch use
behavior. Using behavioral titration approaches from
foraging theory, one can better elucidate how habitat
heterogeneity at both the macro- and micro-scales may
influence resource exploitation and reveal mechanistic bases
of patch use, diet choice, community organization and
species coexistence (Moermond 1986, 1990, Brown 1989,
Brown et al. 1994, Kotler and Blaustein 1995, Whelan
2001, Shochat et al. 2004). Diet choice can also be affected
by the distribution and abundance of different foods
(Brown and Morgan 1995). Fox squirrels were partially
selective on the food with higher initial abundance when
presented apart, while the higher encounter probability of
peanuts increased their selectivity at low quitting harvest
rates and the preference of sunflower seeds increased their
selectivity at high quitting harvest rates (Brown and Morgan
1995).
Studies on the effects of patch characteristics on foraging
behavior generally vary just initial prey abundance. Exceptions include studies that vary predation risk by placing
food patches in safe or risky habitats (Brown and Kotler
2004), or by changing the distribution of food within an
experimental food patch (e.g. micropatch partitioning,
Schmidt and Brown 1996). The relationship between
harvest and time spent exploiting a depletable food patch
(gain curve) directly affect the forager’s patch departure. A
gain curve emerges as an interaction between structural
resource properties, the abundance and distribution of
food within the patch, the forager’s sensory abilities and
mechanics of patch exploitation (Olsson et al. 2001, 2002).
When changing patch characteristics such as initial prey
abundance or within patch resource distributions, the
forager experiences a change in its gain curve that should
influence how long and how thoroughly the forager uses the
patch (Olsson et al. 2001, 2002).
As might be expected, increasing the initial prey
abundance elevates the gain curve (Olsson et al. 2001) and
increases the amount of food harvested before the patch is
abandoned. In general, both the amount of food harvested
and the giving up density increase with increasing initial prey
abundance (Valone and Brown 1989, Morgan et al. 1997).
Furthermore, as an indication of patch assessment by the
forager, the proportion of food harvested generally increases
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with initial food density. The failure to equalize GUDs
seems to result from imperfect patch assessment (Olsson
et al. 1999) or a gain curve in which the per time encounter
probability changes with patch depletion (the gain curve
deviates from the ideal of random search
see Olsson et al.
2002 for an example with starlings).
Foraging behavior is also influenced by the substrate
from which an animal feeds. Price and Podolsky (1989) and
Price and Hienz (1984) showed how coarser substrates than
sand reduce the harvest rates of desert granivores. Furthermore, adding gravel and small rocks to the surface of a
patch increased the GUD relative to their absence (Kotler
and Brown 1999). Davidson and Morris (2001) reported
on increased GUDs in fine sand due to its higher bulk
densities compared to coarse sand. Such a result is
consistent with quitting harvest rate rules under elevated
costs of foraging (Davidson and Morris 2001). Under
natural and experimental conditions, foraging behavior of
warblers gleaning insects appeared to be strongly influenced
by fine-scale foliage structure, mainly: the species of leaf,
accessibility to perches, and the leaf surface qualities
(Whelan 1989, 2001).
Often, giving-up density is used as a surrogate for
quitting harvest rate. However, when patches vary in
characteristics such as area, volume, and substrate depth
the simple notion of a giving-up density can be expressed in
different ways. Most studies present giving-up densities as
grams of food remaining per patch, where other patch
characteristics remain constant across experimental food
patches. But when patches vary in surface area, would it
be more appropriate to express giving-up densities as per
unit surface area, or as GUDs per unit volume when
patches vary in substrate depth? In fact, measuring GUDs
per patch, GUDs per unit area, and GUDs per unit volume
from patches that vary in surface area and in substrate
volume provides different and complementary ways of
viewing attributes of patch use under natural situations. We
used these metrics to examine and compare foraging
behavior and patch assessment by mourning doves and
cottontails for patches varying in depth, area and initial prey
abundance.
We measured the giving-up-densities (grams of food
remaining per patch) of mourning doves and cottontails in
experimental food patches. We studied the effect of patch
characteristics on foraging to reveal factors influencing
quitting harvest rates. We explored the reaction of two
very different species to the same patch properties to test for
characteristics of resource patches to which foragers can
respond and whether these characteristics are general or
species specific, and to evaluated patch design for measuring
GUDs. In what follows we develop testable predictions
from foraging theory. We then present our study methods
for measuring the giving-up densities of doves and then
cottontails from patches that varied with respect to initial
food density, surface area and sand depth, and then present
the results. Predictions and results are couched in terms of
how patch characteristics and the forager’s patch use
strategies may influence the amount of food harvested,
proportion of food harvested, GUD per patch, GUD per
unit surface area (GUDAREA), and GUD per unit volume of
sand (GUDVOL).
Predictions
In order to increase energy benefits and reduce effort,
foragers should bias their efforts towards patches with
smaller areas (higher concentration of food per unit area),
shallower substrates (greater ease of food encounter) and
higher total resource abundance (for a fixed area and
substrate depth). The model of Morgan et al. (1997) on the
effect of spatial scale on the functional response (food
harvest as a function of initial food abundance) of fox
squirrels, predicts that increasing patch richness should
increase proportion of food harvested. Here we consider
within-environment effects of increasing patch richness by
varying independently three properties of patch quality:
initial food abundance (traditional sense of patch richness),
patch size, and substrate depth.
Effect of initial food abundance (IPA)
If the foragers are able to detect and respond to the initial
abundance of food within the patch, they should bias their
efforts towards patches with higher initial resource abundances. The size of this bias and its effect on GUDs and the
proportions of food harvested depend on the forager’s
ability to accurately assess patch quality. At one extreme the
forager may be ‘prescient’ (sensu Valone and Brown 1989)
and able to use sensory cues such as vision and olfaction to
accurately estimate patch quality. As it forages, its estimate
of remaining density is simply the difference between its
initial estimate and its cumulative harvest from the patch.
Such a forager should leave each patch at the same quitting
harvest rate which under random search yields the same
GUD independent of initial food abundance. At the other
extreme, the forager may be unable to make any assessment
of current patch quality. Such a forager should devote the
same amount of search time to each patch resulting in the
same proportions of food harvested regardless of initial food
abundance. In between is the ‘ordinary’ forager that has
imperfect information, but which gains some insights into
patch quality as it forages the patch. Such a forager (e.g.
Bayesian) will not leave each patch at the same quitting
harvest rate (GUDs will often increase with initial food
abundance see Olsson and Brown 2006) but it will favor
rich patches with more search time (the proportion of food
harvested will also increase with initial food abundance).
Our data revealed (see results) that both mourning doves
and cottontails forage under imperfect information. Their
GUD, food harvest and proportion of food harvested all
increased significantly with …