Our lab et al. published a series of papers in the latest issue of Marine Ecology Progress Series in which we explored linkages between copepod abundance and the migration patters of right whales. Better knowledge of where and when right whales might show up can help prevent ship strikes and gear entanglements. The full articles can be found here: 1 2 3.
One of our results was a strong correlation between the computed abundance of Calanus finmarchicus and the arrival date of right whales in the Great South Channel critical habitat. Researchers have known for awhile that right whales use this habitat every year, but the factors that influence the timing of that usage are harder to pin down. (Details on our computations, like how we calculate arrival date and C.fin. abundance, can be found in the papers.)
This correlation may have use as a forecasting tool. The correlation spans the years 1998-2006. By computing the C.fin. abundance for ensuing years, we can use a linear fit to produce a forecast for the arrival date in the Great South Channel (see figure). Our prediction this year is for an early arrival date--right around now, in fact. We also predicted an early arrival for 2007, and a late arrival for 2008.
Figure. Top: correlation between computed C.fin. abundance and right
whale arrival day in the Great South Channel (R^2=0.7, p=0.01). Red dots
show predicted values for 2009, with the most current prediction indicated
by text. Bottom: our predictions for the 2009 arrival date. As the year
progresses, we assimilate more data, and our prediction changes (see point
2 below). The abrupt drop in late February is due to a modification in our
calculation (see point 4 below).
Caveats
There are a few caveats to this forecast. I'll outline them here.
1) A linear regression is a simplification of the dynamics at play, and there is variability about the line. Therefore, even though we give a specific arrival date, our forecasts should be taken as approximate. It's better to think of them as "early", "average", or "late", rather than as occurring on a specific date.
2) Our models rely on satellite data, which is updated as the year progresses. Therefore, our forecast changes as the year marches on (bottom plot in figure). It's similar to how the weather forecast gets better as next week gets closer. This limits us somewhat, but our previous work has shown that satellite data from January and February generally provide enough data to get a significant correlation.
3) We check our forecasts against a whale arrival date that is calculated from survey data. That is, real people looking for whales from boats and planes. It takes a long time for that information to be processed and passed to us, so we haven't yet been able to check our 2007 and 2008 forecasts. So, unlike the weather forecaster, we don't have the advantage of knowing what "today's weather" is. Even though our 2009 forecasts tell us that right whales are arriving in the Great South Channel right around now, or possibly have arrived already, we may not be able to check that for awhile.
4) The nature of satellite data changes with technology. For example, resolution has improved. We've developed a new interpolation method that helps the satellite data to be consistent over many years. The down side is that we had to re-run our experiment with all of the satellite data in this new format. The good news is that the correlations persisted, though altered a little.
One of our results was a strong correlation between the computed abundance of Calanus finmarchicus and the arrival date of right whales in the Great South Channel critical habitat. Researchers have known for awhile that right whales use this habitat every year, but the factors that influence the timing of that usage are harder to pin down. (Details on our computations, like how we calculate arrival date and C.fin. abundance, can be found in the papers.)
This correlation may have use as a forecasting tool. The correlation spans the years 1998-2006. By computing the C.fin. abundance for ensuing years, we can use a linear fit to produce a forecast for the arrival date in the Great South Channel (see figure). Our prediction this year is for an early arrival date--right around now, in fact. We also predicted an early arrival for 2007, and a late arrival for 2008.
Figure. Top: correlation between computed C.fin. abundance and right
whale arrival day in the Great South Channel (R^2=0.7, p=0.01). Red dots
show predicted values for 2009, with the most current prediction indicated
by text. Bottom: our predictions for the 2009 arrival date. As the year
progresses, we assimilate more data, and our prediction changes (see point
2 below). The abrupt drop in late February is due to a modification in our
calculation (see point 4 below).
Caveats
There are a few caveats to this forecast. I'll outline them here.
1) A linear regression is a simplification of the dynamics at play, and there is variability about the line. Therefore, even though we give a specific arrival date, our forecasts should be taken as approximate. It's better to think of them as "early", "average", or "late", rather than as occurring on a specific date.
2) Our models rely on satellite data, which is updated as the year progresses. Therefore, our forecast changes as the year marches on (bottom plot in figure). It's similar to how the weather forecast gets better as next week gets closer. This limits us somewhat, but our previous work has shown that satellite data from January and February generally provide enough data to get a significant correlation.
3) We check our forecasts against a whale arrival date that is calculated from survey data. That is, real people looking for whales from boats and planes. It takes a long time for that information to be processed and passed to us, so we haven't yet been able to check our 2007 and 2008 forecasts. So, unlike the weather forecaster, we don't have the advantage of knowing what "today's weather" is. Even though our 2009 forecasts tell us that right whales are arriving in the Great South Channel right around now, or possibly have arrived already, we may not be able to check that for awhile.
4) The nature of satellite data changes with technology. For example, resolution has improved. We've developed a new interpolation method that helps the satellite data to be consistent over many years. The down side is that we had to re-run our experiment with all of the satellite data in this new format. The good news is that the correlations persisted, though altered a little.
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