Recall in the Week 5 lab activity we searched the scientific literature for information on the residency effect. This week we will exploring this effect in house crickets. Based on what we learned last week regarding this effect, as you read through the description of the experiment and activity below, determine what would be a good biological rational/scientific hypothesis for this behavior in crickets. You will be using this in your Peerceptiv 3 assignment.
In a classic animal behavior paper, Richard Alexander (1961) described agonistic cricket behavior with an ethogram. Ethograms are descriptive behavioral catalogues of all behaviors performed by an animal.
Types of agonistic behavior
• Cerci raise (CR): Abdomen is raised in direction of another cricket
• Mandible flare (MF): Mandibles are spread
• Stridulation (SD): Wings are rubbed together to produce “chirping” sound
• Shake (SK): Entire body shakes side to side or forward to back
• Head butt: Cricket rams its head into a second cricket
• Fore leg punch: Cricket strikes a second cricket with its foreleg
• Antennae lash (AL): Antennae are whipped at a second cricket
• Stridulation lash: Antennae are whipped while cricket chirps
• Mandible spar: Two crickets spread and make contact with their mandibles
• Head charge (CH): Similar to head butt, but no contact between crickets
• Kick: One cricket strikes a second cricket with its hind leg
• Mandible lunge: Mandibles are spread as cricket lunges at a second cricket
• Wrestle (WR): Two crickets lock mandibles and attempt to flip each other over
Source: Alexander, R. D. (1961) Aggressiveness, territoriality, and sexual behavior in field crickets (Orthoptera: Gryllidae). Behaviour 63: 130-223.
(Note: You are not required to read this paper, this is just the reference for the ethogram we are using)
Scoring Cricket Interactions
A pairwise interaction between the crickets is defined as when the two insects come into close proximity. A “dominance point” is awarded when one of the crickets stands its ground during the interaction, and one of the crickets turns away from the other cricket. The point is awarded to the cricket that stood its ground. Sometimes there is no clear outcome, in which case no dominance point should be recorded.
To practice viewing cricket interactions in a similar format to the one that you will view when collecting data, watch the following video that Dr. Bouwma, an instructor in the BI 20x series, recorded a few years ago (This is the same video that is available in the Canvas Lab Discussion this week):
• Pairwise Cricket Interaction
In this cricket contest, the two crickets were very evenly matched, so the contest continued at high intensity for some time. The slightly smaller cricket (which starts out on the right side of the screen) is the eventual winner. As an example of dominance scoring, the smaller cricket earns several dominance “points” during the time span from 0 to 0:15. Between 6:00 and 6:40, a clear winner emerges in the contest.
Lab Activity
Once you are comfortable with understanding how to score dominance points, proceed to the following activity where you will collect data from video-recorded cricket contests
• Residency Effect Lab (BI 204)
Note: If you receive a message for “OSU Login – Stale Request” when attempting to access the website, you may need to manually cut-and-paste the site link into your browser’s address bar. The website address is https://courses.ecampus.oregonstate.edu/bi204/residency-effect/
Each pair-wise contest begins when a male cricket is introduced into a territory that is occupied by a second male cricket. Crickets were supplied by a local pet store. The territory owner has been a sole resident of its 15 cm x 25 cm x 15 cm plastic container for at least 24 hours. The intruder comes from its own identical plastic container, where it has also been isolated for at least 24 hrs. You will be able to identify the intruder cricket by a red mark that will appear on its thorax every several minutes. In the intervening time, you will need to carefully watch the crickets so you do not lose track of their identities. No individual cricket was used more than once.
Some of the observation containers contain Flukers brand water replacement gelatin (provides hydration) in a 2 cm diameter dish, while they were removed from other containers before filming. There was no specific reason as to why the Flukers was left in or removed.
Data Collection
Over each 10-minute trial period, you will record the number of dominance points earned from every pairwise interaction between the crickets. You can use the green and red buttons on the screen to keep track of points during the contest. The OVERALL winner of the contest will be the cricket with the largest number of “points” in 10 minutes. For example, if the intruder cricket and the resident meet 15 times during the 10-minute contest and the intruder scores 8 points while the resident scores 7 points, then the intruder cricket would be the overall winner.
Links to the 3 videos:
- https://www.youtube.com/watch?v=tzeXbtB5pzY&feature=youtu.be
- https://www.youtube.com/watch?v=tzeXbtB5pzY&feature=youtu.be
- https://www.youtube.com/watch?v=ZirXpj0LkuA&feature=youtu.be
Record your data here by circling the overall winner for each trial. The number of points scored by each cricket do not need to be recorded.
• Trial 1 Winner: Resident or Intruder
• Trial 2 Winner: Resident or Intruder
• Trial 3 Winner: Resident or Intruder
You will need these data next week when writing for Peerceptiv Assignment 4.
Note: The ‘Next Step Button’ is from an older version of the lab and it is no longer functional.
Introduction to Statistics and Data Analysis
Let’s plan our analysis for next week because we need to both state our statistical hypotheses and indicate what analysis we will conduct in this week’s Peerceptiv Assignment 3. You have already been exposed to much of the following information during the Week 5 Lab.
First, we must formulate our statistical hypotheses. Statistical hypotheses are actually predictions that are specific to a particular experimental design.
• The null statistical hypothesis (H0) assumes that there is no relationship between our independent and dependent variables.
• The alternative statistical hypothesis (HA) assumes that there is a relationship between our independent and dependent variables.
For our experiments with cricket aggression, the null hypothesis is that residency status has no effect on the likelihood that a cricket will win a contest. Therefore, when observing crickets over several minutes, the number of times that residents win contests should be equal to the number of times that intruders win contests.
We could just look at our data and determine whether residents and intruders won equal numbers of contests. What if our data indicate that win numbers were close? How do we know if they are different enough to warrant concluding that there is an effect of residency on the likelihood of winning? This is precisely why we use a statistical test: to provide a quantitative assessment of our hypotheses.
Statistical tests produce a p-value based on your data. This p-value, ranging between 0 and 1, is the probability of obtaining your results (or an even more extreme result) if the null hypothesis is supported. In other words, it is the probability that you obtained your results solely due to chance. A smaller p-value is stronger evidence against the null hypothesis. By convention in biology, we reject our null hypothesis if the p-value (p) is 0.05 or less. With this indication that we would have a 5% of a chance of getting our results due to chance, we can say that our results were statistically significant. In biology, we reserve using the word, “significant” for when we are talking about results that obtained p < 0.05.
In this cricket-residence-effect study, you will conduct a statistical test called a Chi-square test for “goodness of fit,” in order to determine whether our data fit an expected distribution that assumes the chance of winning is 50:50 and not dependent on residency status. In other words, the Chi-square test will tell you whether you can reject the null hypothesis, based on your results. (Note: you are not expected to understand the underlying mathematical constructs of the Chi-square test. Rather, the focus is on reporting and interpreting the test results.)