Monitoring is a key aspect of any management plan, as it is needed to measure effects of conservation strategies on the managed resource. Quantitative monitoring of spawning aggregations documents location, timing, and trends in the relative numbers and sizes of fish for the following purposes1:
- To measure impacts of management regulations and provide feedback for adaptive management;
- To measure impacts of fishing where management is not in place, or where effective enforcement is lacking;
- To assess trends (declines or recovery) in aggregation populations;
- To provide predictive power for other sites and species;
- To provide some insight into reproductive biology;
- To deter poaching by maintaining a field presence; and
- To encourage sense of ownership by resource users and managers.
Monitoring is a means by which MPA practitioners can obtain answers to pressing management questions. The first step, therefore, in setting up a monitoring program, is to identify the questions you want answered. The second step is to develop a monitoring program that is effective, and efficient in answering these questions.
Developing an Effective Monitoring Program
The ultimate goal of monitoring should be to inform managers about trends and utility of management approaches. The key to any monitoring program is a properly designed sampling technique. This will ensure that the results are useful to answer the questions at hand. When a sampling protocol is carried out, enough members of the population must be sampled in order to accurately represent that population. For example, if a population of fish contains 100 individuals, one is more likely to understand the structure or characteristics of that population by sampling 50 fish, rather than 5 fish. Therefore, increasing sample size increases the sample design’s power, and the probability that the sample is accurately representing the true population.
The more variability that is inherent in a population, the more intensive the sampling methodology needs to be to obtain significantly relevant results. For example, it would be more difficult to measure a declining trend in an FSA, where the number of fish spawning at the FSA each year was highly variable (regardless of fishing pressure). The example below demonstrates this concept.
Imagine that the purpose of a monitoring study is to determine whether the average size of fish spawning at an FSA is declining over time. A sample of fish is taken in 2000, another sample is taken in 2005, and the two samples are compared.
In the first comparison below, Sampling Example 1, one can see that the mean fish length declines from 23 cm to 17 cm. However, the size of the fish are so widely variable, that one cannot tell whether the sizes are truly decreasing, or if these differences are due to random sampling of smaller individuals in a large, highly variable population. In the chart to the right, Sampling Example 2, note that the average lengths over time are the same as in the first example. In this case however, the variability between fish of different sizes is much smaller, and therefore it can be more reasonably assumed that differences in the fish length from 2000 to 2005 may have in fact been a result of a decrease in average size.
In the first case, sample size would have to be increased to accommodate the highly variable population sizes.
|Sampling Example 1||Sampling Example 2|
|Fish lengths (cm)||Fish lengths (cm)|
A good sample design is crucial to any monitoring program. One may have large amounts of data, but if the methods used to collect the data are poor, then the entire data set is relatively useless. Designing a monitoring program with maximum statistical power is important, given the time, effort, and resources required to collect the data. Managers may wish to ask for outside assistance from local universities or scientists regarding sampling protocols, to ensure that monitoring programs are designed effectively and resources are used efficiently. Preliminary surveys, or any existing data, will be valuable to help scientists and managers work together to develop an effective monitoring protocol.
See the Monitoring Tools page for more information on monitoring protocols and data collection.