A simple ecological study: Proposed studiesRead the opening chapters in your lab manual (read all the sections on experimental design and hypothesis testing) and the material below before coming to class, and come prepared to go out in the field.  If you need to review mean, standard error, and t-tests, do it. We will meet in the lab for a brief discussion of the scientific method and a planning session for our field work.  Then, we will go to the nature reserve and you will begin making observations for your simple ecological study.  Some of you may begin collecting data this week, but you will also have next week to continue.  At the end of lab you will   Hand in: Fill in the worksheet A simple ecological study: Proposed Studies and turn it in at the end of lab.  5 points based on (1) the clear statement of a testable descriptive hypothesis, (2) an appropriate sampling regime, and (3) a reasonable functional hypothesis.

General Ecology Lab #4        A Simple Ecological Study

In this lab you will conduct a simple ecological study that your group will initiate, design, and carry out over the next two labs.  The goals of this lab are for you to understand basic ecological studies, design a sampling regime, conduct statistical analysis of your results, and prepare a concise report of what you did.

Designing an Ecological Study:  An ecological study usually begins with an observation of a pattern in nature.  For example, you might observe that red squirrels are common in one area, while gray squirrels dominate another.  At this point your initial observation is just subjective; you need to conduct a formal descriptive study to document the pattern you observed.  For this descriptive phase you first need to formally state your question or descriptive hypothesis:  Are red squirrels dominate in Area A?  (or, stated as a hypothesis: Red squirrels are dominate in area A, while gray squirrel are dominate in area B.)  Now you would need to design a sampling protocol that would accurately assess the squirrel populations (see section on Ecological Sampling) followed by statistical analysis of the data (see section on statistical analysis).

 At this point you may have objectively shown that a pattern does exist, and it is time to formulate a functional hypothesis that can account for the pattern you documented.  For example, you might suspect that the tree species present determined the distribution of the squirrels, or perhaps the presence of a predator or disease influenced the squirrels in different ways.  You will need to state a functional hypothesis that is phrased to include a cause and effect relationship.  For example, you might state your hypothesis as "if oaks are the dominate tree species in an area, then gray squirrels will be the more abundant than in areas dominated by pines".  These "if, then" statements are a common way to express a functional hypothesis, but in ecology it is also common to state your hypothesis by posing a question to be addressed.  For example, "are gray squirrels more common in forest areas dominated by oaks than in forest areas dominated by pines"?  In ecology the "null hypothesis" unstated even though it is always implied.  After forming a functional hypothesis you will need an experiment to test it.  Perhaps you could introduce gray squirrels to area A; do they survive? 

 Making initial observations:  Many comparisons can be made, but in this lab you should focus on comparing one species in two different situations.  For example, compare the individuals of one species in different habitats.  Some features you could compare:

abundance--the density of individuals in a habitat might depend on the level of some resource that varies in distribution

morphology--the size, shape, or color of individuals may be influenced by the environment (or you could have genetic differences in different habitats).

reproductive output--the number of offspring produced is a critical ecological feature, and it could be influenced by different habitats or the presence or absence of predators, herbivores, or parasites

 Keep in mind the tools you will have for measuring the differences you observe: rulers, meter tapes, and of course you can count things.

 Formulate a hypothesis:  For the descriptive phase of your study, your hypothesis will be simple:  e.g., The number of individuals of Japanese honeysuckle is greater in the shade than in the sun.  A functional hypothesis will include a mechanistic explanation: e.g., Seed germination in sunny habitats is limited by rapid desiccation compared to shade. 

Ecological sampling:  Ecologists often gather information about a population (i.e., number of seeds produced), community (i.e., number of species), or a habitat (i.e., organic matter in the soil).  We can't measure the seed production of every individual plant in the field, so we need to sample--we take a statistical sample. 

            You must know what you are sampling; e.g., if you measure the seeds on an individual in August, have you missed seeds that were produced back in June and have since fallen off? have some seeds been eaten by mice?  will more seeds be produced in September?  If you want to sample seed production by an individual plant, you must be sure to actually sample what you think you are sampling.

            You must not bias your samples.  Which individuals will you sample and which will be ignored?  Random sampling involves use of a random number table to select random plots to sample.  The table will give you a number--say 6-- and you sample plot 6.  The problem with random numbers is that everything you want to sample must have a number (i.e., you need to grid off the study area so all points are numbered).  Haphazard sampling involves, for example, throwing a stake out and sampling the closest plant. 

            Your samples must be replicated. 

 Statistical analysis of data:

 Two-sample comparisons:   This procedure allows you to draw conclusions about the similarity or difference between the means of two sampled populations.   For example, you might compare the seed output of smooth sumac growing in the shade and in the sun. Calculate the mean (x), standard deviation (s), and standard error (sx or SE) for each sample.  Use the standard deviation to express the population variability and the standard error to provide an estimate of the interval where the true population mean is to be found (the interval:  x + 1.96 * standard error, will contain 95% of the sample means for large samples.

            A student's t-test will allow you to statistically contrast the two samples--in other words objectively answer the question of whether the samples are the same or different.  Small t values indicate a high probability that the two population means are the same; by contrast, large t values imply lower probability.  The calculated t value and probability (p)are given in SYSTAT.  Ecologists consider a difference between population means to be significant when the probability that they are the same is less than 5% (p < .05)

 Regression analysis:  You can use a regression analysis to constrast two different variables measured on one species.  For example, you might observe that a taller plant produced more fruit than shorter plants.   You could test this descriptive hypothesis by measuring the height of the plant and the number of seeds it produced.  Your lab manual will tell you how to do a regression analysis, and it is easy to do in SYSTAT.