This week has been full of data: entering data, proofing data, analyzing data, graphing data and writing about data. Sitting and staring at numbers for hours at a time, I’ve found, can take a toll on the body and mind. However, it definitely has its own special silver lining.

Ever since the first time I collected my own data, I remember being fascinated by the jumbled collection of numbers that I had created over time. Finally, after all the numbers had been transferred to my screen, seeing each number side-by-side created a deceiving sense that they had lost the complexity of their original meaning. When all of the physical work was finished, and everything was neatly packed away- every leaf that I had counted from each of the plants that I had grown, or the temperatures changes of the soil over time, or the weight of the dried biomass of the plants that I harvested- it all just became a number on a screen. I knew what each number meant at face value, but something truly clicked when I saw them laid out in front of me. I realized how little I really knew about what I had previously thought I had a good amount of knowledge about.

Although was clear to me how much information the numbers represented, I came to understand that I would always be limited to the depth and quality of the tools that would allow me to understand all that the numbers had to say. I would never be able to access all the information that the numbers held; the data’s secrets became apparent as I realized what I was really looking at were the stories of the countless interconnections of the factors I had been studying. It was now my job to let the numbers teach me what information I could pull from them without the bias of my human eyes. Suddenly, data became more than just numbers to me, data became a symbol of a human attempt to record the complexity of the interconnections of the natural world.

Here’s a snapshot of what I found from the shorebird survey data:

Abundance Graph (2013-2019)

In a previous blog post, I discussed how typically, the influx of shorebirds exhibited two clear migration abundance peaks. The graph above shows the abundance of shorebirds counted at each day of the survey (May 1st to May 16th) from years 2013 to 2019 and the distinct year’s abundance peaks. Every year is vastly different. Seeing all the peaks in one graph holds evidence of what makes each year similar in nature and also, so complexly different in timing and quantitatively. It amazes me how much perspective a graph can offer us through time and space.

Graphs allow me to see trends in the data while simultaneously seeing through my own human bias. For example, because this year’s shorebird abundance was obviously less than it had been in previous years, it was a natural concern to ask myself why this may be. I found myself assuming it would have something to do with climate change affecting- shifting weather, temperature patterns- the migration timing and abundance and I looked at the data with this lens. I was searching for evidence in the graph to confirm my own bias; I was looking for peaks of fewer numbers and earlier timing compared to earlier years. However, the data does not represent such a clean and clear story, no matter how much my human brain craves the safety and security of confirmation bias. Instead, this graph shows that although years 2014 and 2015 do show higher abundance peaks than the other years, 2013 does not neatly fit into my hypothesis box because of its dramatically small abundance peak. In fact, it’s the smallest abundance peak of all of the compiled years of data.

This could mean several things. One being that I am completely wrong in my hypothesis, perhaps the changing climate has nothing to do with it. Another much more realistic conclusion is that I have not yet accessed the right tools and information to do the analysis necessary to ask the data this kind of question yet. Or perhaps, I do not have the yearly range of data in order to make this kind of assumption at all. I still haven’t run any statistical tests on the data. I have decided to pull temperature data for each day of the survey for each of the years to see if it may allow me to gain more insight and clarity. In an upcoming post, I will address the questions that I’ve asked the data and discuss what I find.

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