Question

Wildebeest in the Serengeti area of east Africa have a very restricted calving season. All females give birth within a space of three weeks at the start of the rainy season (sinclair and Arcese 1995). How would you test the hypothesis that this restricted calving season is an adaptation to reduce predation losses of calves?

Short answer

Test hypothesis by analyzing calf predation rates during calving peak versus other times using observational data.

Step-by-step solution

Formulate the Hypothesis

The hypothesis suggests that the restricted calving season is an adaptation to reduce predation. To proceed with testing, we define the null hypothesis as: 'The restricted calving season does not reduce predation losses.' The alternative hypothesis would be: 'The restricted calving season does reduce predation losses.'

Design the Experiment

Design an observational study where you monitor a population of wildebeests during calving season. Include variables such as calf survival rates, predation rates, and time of birth within the season. If possible, include data from diverse ecological environments or control for factors that might influence predation, like availability of other prey.

Collect Data

Collect data over several years to get consistent results. Document the number of calves born and the number of calves lost to predation during the calving peak and outside the peak within the season. Also, record factors such as the abundance of predators and habitat conditions.

Analyze the Data

Analyze the collected data statistically. Use tests like chi-square to compare calf survival rates during the restricted calving season versus outside this period. Look for statistically significant differences that indicate reduced predation during the calving peak.

Interpret the Results

If the statistical analysis shows that calf survival rates are significantly higher during the restricted calving season, this would support the hypothesis that restricting births to this period reduces predation losses. If no significant difference is found, the hypothesis may not be supported.

Key concepts

Hypothesis Testing

Hypothesis testing is a method used by researchers to make inferences about a population, based on sample data. In ecological studies, this process starts with two key hypotheses: the null hypothesis and the alternative hypothesis.

- **Null Hypothesis (H0)**: This is a statement that indicates no effect or no difference. For the wildebeest study, it would state that "The restricted calving season does not reduce predation losses."
- **Alternative Hypothesis (H1)**: This suggests that there is an effect or a difference. In this case, "The restricted calving season does reduce predation losses."

The purpose of hypothesis testing is to determine which hypothesis is more supported by the data collected. Researchers use statistical tests to assess the evidence against the null hypothesis. When the results show, with a significant level of confidence, that the null hypothesis is unlikely to be true, it is rejected in favor of the alternative hypothesis.

Observational Study

An observational study involves monitoring subjects without influencing any variables. It’s essential when controlling certain factors isn't feasible, as is often the case in ecological environments. In our wildebeest example, conducting an observational study allows researchers to gather data passively.

- **Design**: Researchers should outline a detailed plan to track variables like calf survival rates, predation rates, and birth timings. All these without interfering with the natural behavior of the wildebeests or predators.
- **Considerations**: By including different ecological conditions or controlling potential confounding factors such as the presence of other prey or predator abundance, the study can provide more robust data.

Observational studies often take longer since researchers need to collect enough data over several seasons to account for variations. Despite challenges, this approach can be very powerful for understanding natural processes as they truly occur.

Data Analysis

Once data is collected, it's time for data analysis. This step is crucial to deriving meaningful conclusions from the study. In hypothesis testing, data analysis involves applying statistical tools to determine if the patterns observed could have occurred by chance.

- **Statistical Tests**: In this context, a chi-square test may be used to compare observed calf survival rates during the restricted calving season versus other times. This test is suitable for categorical data and can help determine the significance of survival rate differences.
- **Significance**: Researchers look for p-values, which indicate the probability that the observed results are due to chance alone. A p-value lower than a predefined threshold (commonly 0.05) denotes a statistically significant result, providing stronger evidence in favor of the alternative hypothesis.

Analyzing data correctly ensures that the conclusions drawn reflect real ecological relationships, not random variations.

Predation

Predation is a biological interaction where one organism, the predator, kills and eats another organism, its prey. It's a significant factor influencing wildlife populations and behaviors. In the context of the Serengeti wildebeest, understanding predation is key to testing the hypothesis.

- **Role in Ecology**: Predation maintains balance within ecosystems. Predator and prey dynamics can influence behaviors, such as timing of births, as prey species evolve survival strategies.
- **Calving Season**: The hypothesis suggests that a restricted calving period may overwhelm predators, reducing the chances of individual calves being targeted. This is because all the calves are born around the same time, making it harder for predators to hunt them effectively.

By studying predation patterns and their effects, ecologists can better understand how and why species like the wildebeest might adapt in specific ways for survival.