Ecological Surveys of Littorina littorea Populations in Mount Hope Bay, Bristol, Rhode Island
Conducting biological surveys is invaluable to scientists attempting to understand changes in ecological systems, population dynamics, and organism behavior. Depending on the species and locations being observed, the most ideal survey methods may vary. For example highly condensed populations might require more samples to achieve a representative result, and larger organisms need a larger area to survey for each sample.
Some of the first characteristics an ecologist might want to determine about a population include density and distribution patterns. These can both be determined by conducting population counts. Since doing a total count of all species in a population is often very difficult if not impossible, population numbers can be estimated fairly accurately by counting smaller, representative samples and scaling them to accommodate the total area that the species would encompass. In order to ensure that these smaller samples are indeed representative of the larger whole, a few general guidelines should be followed. Ideally, the location of samples should be chosen randomly to minimize bias, allowing equal opportunity for any location to be chosen. Locations can be selected systematically as well, for instance, every five meters heading west, but if this pattern coincides with a natural pattern, the results become biased. Also, the sample area must be appropriately sized for the organism, for example if you are sampling rabbit populations, the sample area would need to be larger than that for bacterial populations. The sample size must be taken into consideration as well. In general, the more samples the better, but there are labor, time, and cost restrictions associated with sampling procedures. Since we cannot always do as many samples as would be best, we must determine what amount would constitute a representative sample size. This is commonly done in two ways, both requiring that preliminary samples be taken. The first method is to construct a performance curve and note where the change in mean is altered negligibly with the addition of another sample. The second method is to use the formula for a confidence interval and solve for the sample size.
Littorina littorea (common periwinkle) is an ecologically important organism in rocky shore intertidal ecosystems for a number of reasons. For one, they increase biological diversity by grazing on the most competitive algae; that is, soft, fast growing green algae (Lein 1980, Lubchenco 1978). This allows less competitive algae to obtain more nutrients and habitat and have increased growth and prominence (Lubchenco 1978). This grazing activity also helps to control algal populations, preventing harmful algal blooms and subsequently fish kills. During grazing, they will also often displace attached organisms such as barnacles, thus altering ecosystem structure and causing interactions (Petraitis 1983). L. littorea is also an important prey item for many predators, especially crabs. The larvae are a food source for filter feeders and carnivorous planktivores including fish larvae. Another important ecosystem role played by the periwinkle is that of carbon fixation. When they form their CaCO₃ shells, it removes CO₂ from the water column and holds it in the shell, thus affecting atmospheric and water composition. The effects of these organisms are surely not limited to just these things alone, so understanding the population dynamics of this species holds a lot of value.
This study aims to assess the density and distribution patterns of L. littorea populations in Mount Hope Bay and to determine the amount of samples necessary to attain a representative sample. The null hypothesis is that the distribution pattern is random.
Materials and Methods:
The methods and analysis used here were modeled after the strategies suggested by Bellehumeur et al. 1998 and Pillar 1998. A ¼ meter quadrat was tossed haphazardly along the edge of the Mount Hope Bay, using both the shore and the water as possible sample areas. The spot at which the quadrat landed was used as a sample area. All L. littorea individuals in the quadrat were counted and recorded. Rocks and debris were moved aside to locate all organisms on the surface layer, but sediment was left undisturbed. This process was repeated until five samples had been taken. These data were compiled with the data from eight other groups to make a total sample size of 45 quadrats. An average of this new data with standard deviation and standard error was calculated. Confidence intervals were then calculated at probabilities of 90%, 95%, and 99%. Spatial dispersion of the species was then described using the variance divided by the mean. If this number was one, the population is randomly dispersed, if less than one, the population is uniformly dispersed. If greater than one, the population likely has a clumped dispersal pattern.
The experimentally determined average density of L. littorea was about 21 +/-16 organisms per ¼ meter. Standard error was found to be 2.36.
Table 1. Lower and upper limit of confidence intervals at 90%, 95%, and 99% probability
Table 1 shows the intervals at which we can be confident that our actual population average lies within. As the percent confidence increases, the range which the actual value lies within increases.
The spatial dispersion of L. littorea was found to be clumped. This was determined by dividing the variance by the mean, then comparing that value against one. Since the value (y) was found to be 12.17, and thus greater than one, the dispersion pattern was clumped.
y = (SD^2) / x
Figure 1 displays the average density of organisms as the number of samples increases. As more samples are added, the change in average density becomes less with the addition of each new sample. Around about 25 samples, the addition of new samples causes insignificant change to the new mean, thus giving an n value of 25 using this method. Also by looking at figure 1, we noticed that the mean density of L. littorea is much different after 45 samples than after just a few samples. As more samples were collected, the cumulative mean became more representative of the true population mean.
Using the two-step sampling approach, the number of samples required to obtain an adequate result was calculated (Table 2) at 1%, 5%, and 10% of the total average density using the following formula:
n = (s^2 * t^2) / (L^2)
(s= 15.86, t=1.96, L= % * total average density)
Table 2. Number of samples required based on two-step sampling technique
Number of samples required
As the percent of the average density increased, the number of samples required decreased (Table 2).
The spatial distribution of L. littorea was found to be clumped, thus rejecting the null hypothesis that spatial distribution is random. This result was reinforced in the field, as L. littorea was generally found in clusters and rarely singly or evenly dispersed. It could be that these organisms congregate where food is abundant, or where environmental conditions are more favorable (i.e. shade or protection from predators). Dispersal patterns could also be different based on sampling time, though this was not taken into account as all samples were taken during the daytime in autumn.
Using the first method to calculate n, if only a few samples were collected, an uncommon result could be mistaken as an accurate representation of average density. As more samples are taken, these occasional areas of very high or very low density become less significant to the average of the population as a whole, allowing for a more realistic view of how many individuals are actually in the population. After about 25 samples, the addition of new samples caused little change to the mean value, implying that somewhere around that number of samples would be adequate to achieve a mean that is representative of the population. Overall, the more samples that can be taken, the more accurate the results will be, but taking many samples is not always an option as it can take large amounts of time, money, and resources to collect and store samples. This is why taking a pre-sample is a good idea so that you can determine how many samples would be needed to obtain relatively representative results.
Looking at table 2, the calculated number of samples required for adequate results was shown to be much higher than that of the regression line. Pre-sampling as opposed to calculating sample numbers is probably more accurate in a situation like this were the desired results are relative to the situation.
By knowing and understanding population information, we could hypothetically determine contributing causes of certain algal species proliferating more than usual, or causes for a decrease in algal abundance. We could also make inferences on barnacle success, because if dislodging rates are high, this could contribute to how many barnacles are present, or where they are present. We could also calculate carbon fixation rates and compile it with data from other organisms that create CaCO₃ shells to make predictions about the rates at which carbon is removed from the atmosphere through the ocean by mollusks. Combining this with data about aquatic photosynthetic activity would offer a wider scope on the effects of the ocean on the carbon cycle. Understanding population dynamics of one species not only tells a lot about that organism, but about that organism’s interactions with the surrounding ecosystem.
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Pillar, V.D. “Sampling Sufficiency in Ecological Surveys.” Abstracta Botanica 22 (n.d.): 37-48. Department of Plant Taxonomy and Ecology, 1998. Web. <http://ecoqua.ecologia.ufrgs.br/arquivos/Reprints&Manuscripts/Pillar_1998_AbtractaBot.pdf>.
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