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Infographic 9 — The Canadian Arctic: What do we need to understand change

Infographic 9: What do we need to understand change
Long description

A graph based infographic describing what is needed to better understand change in the Canadian Arctic. Two graphs are shown at the top to describe normal variability and change. The normal variability graph have values that fluctuate up and down (blue line representing year-to-year variability) and a yellow line smoothly increases and decreases over a period of 10-years indicating decade-to decade variability. Overall, a white line through the data points is horizontal indicating no overall change over a period of 20 years. To the right is a second graph that shows variability with change. In this graph the line through the data points increases and an overall change is obvious despite fluctuations in the data points. The bottom panel shows two more graphs. On the left, a graph shows blue circles for data collected consistently over 20 years. With all the data present, a white line through the data shows an increasing trend. On the right, the same data is presented however some of the circles are grey, representing missing data. The white line is drawn through the blue available data and this time the line is horizontal and shows no increasing trend due to the missing data points. Text at the bottom of the figure describes the importance of long-term monitoring.

  • What is variability? What is change?
  • Year-to-year variability
  • Decade-to-decade variability
  • Overall change
  • Normal variability
  • Ecosystems naturally differ over time
  • 20 years
  • Variability with change
  • Long-term data can show real change
  • Consistent data collection is needed to detect change
  • Complete data
  • Change observed with complete data
  • Incomplete data
  • Appears unchanged over time because data is missing. Normal variability can hide changes when data is incomplete.
  • Long-term monitoring is needed to identify and explain changes in Arctic marine ecosystems
  • Missing data
  • False conclusion

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