At shocking time, the staff worker pulls the tray of eggs and prepares to load the Jensort. They find that the rejection rate is very high, somewhere around 75%. That tray gets turfed. The next one or two are fine, but there are several more like the first. There seems to be no rhyme or reason to the good and bad eggs. There’s no way around it; the boss must be told.

Deerstalker cap on head, meerschaum pipe in hand, the hatchery manager surveys the situation. The problem is clear; the eggs are junk. The question is how did they come to be that way? Thus the mystery begins.

This is not an isolated incident in the salmon-farming world. Nor is it unknown in the culture of other fishes. With apparently no rhyme or reason, eggs from individuals or groups of eggs will have poor performance.

The process that most veterinarians and indeed, physicians use is the process of ‘Rule Out’. In this process, all factors that could have contributed to the observed problem are considered. Then, certain factors are dismissed as impossible contributors to the problem, or ‘ruled out’. This is a great system if it results in an answer.

Record keeping will play a large role in the rule out scheme. If records are accurate and up to date it will go along way to simplifying the rule outs. As well, a standardized reporting form for all conditions of culture can not be overemphasized. This means following Standard Operating Procedures not just this year, but every year.

Environmental record will probably get the closest and immediate scrutiny. The important parameters such as temperature, pH, hardness and TGP are at the frontline, but others such as organic load are also important. The frequency and periodicity of the measurements are important as well. This means analyzing when (as in time of day, everyday) the parameter was measured. In fry or parr tanks, oxygen levels are lowest at dusk and dawn when fish are most active. Measuring oxygen at high noon won’t disclose fluctuations. Likewise with water quality from surface sources; natural inputs can cause fluctuations. After a rainfall, pH may vary widely due to freshette or watershed variations. So, the water quality data is only as good as the protocol allows.

Handling and treatments are next on the order paper for rule outs. Hatchery records will disclose if the eggs/fry/parr in question have been recently treated. Consider not only the treatment, but why the fish were treated. If fish were sick or afflicted with fungus, then there are two effectors: the reason for treating and the treatment. Both must be considered. This analysis will sometime elucidate a spatial disparity in mortality. That is, if eggs or fish have been treated several times for a condition it is plausible that attrition and cumulative treatment may cause the observed mortality. The final treatment put the eggs/fish over the edge. Sometimes the treatment is worse than the disease.

Combinations of factors both environmental and physical could contribute to weird patterns in mortality. The combination of water quality changes and treatments can have adverse effects. As well, changing treatment types or using a fresh batch of treatment can have effects. These can confuse the issue.

The next step in the process is from outside influences to physical ones. The source of the eggs can be important. That is, it is important to establish if the eggs were from different supplies and not kept separate. Again, records become important.

Different crosses have different performance characteristics. One breed of salmon may have better performance than another. There are inherent performance characteristics of the stock that may influence egg quality. In some cases, the observed mortality may be a reflection of variability in the survival rate, although on an extreme end of the curve.

If communication in a company is good, then access to seawater records can be useful in determining the cause of latent mortality in the offspring. If batches of eggs arrived from different sites, there could be an explanation of varied mortality. Of use are both environmental records and stock history. In many cases, this information can lead to solutions.

The next line of enquiry is to look for trends both physical and physiological. Trends or links can reveal themselves when seemingly disparate data are looked at in a cause and effect relation. Examples of this are a decline in water quality that parallels production increases or lower survival of eggs with a change in overall historical spawning date. The downside of this is to have serendipitous association. An example of this is high mortality of early eggs being correlated to new boots each spawning year. A correlation exists, but they are not linked by cause.