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A day in the life of a field ecologist

November 28, 2023

Details from my summer experience conducting fieldwork in the Colorado Rockies

Photo caption: View of the Rocky Mountain Biological Laboratory, with Gothic Mountain towering above.

4:30 AM: Good morning! To arrive at my research sites in time to start my bird species surveys by 6 AM, I often have to wake up long before the sun rises. I get dressed, refill my water bottles, collect the snacks and PB&J sandwiches I prepared the night before, and grab my pack. Inside my pack I have my binoculars, data collection sheets, a handheld GPS unit, and of course a first aid kit and extra warm layers. Field research requires that you always be prepared for the unexpected!

This summer I am investigating how birds in the southern Rocky Mountains are responding to climate change. To do this, I am conducting a resurvey of bird species along an elevational gradient. I have six research sites ranging from 8,100-11,200ft in elevation, and I will visit each site three times throughout the season to conduct a point count survey of bird species. A point count survey involves standing in one spot for a specific amount of time and recording all bird species I observe by sight or sound. Another student conducted these surveys in 2011, so I am repeating their methods to be able to compare the data from 2011 and 2023 and examine how bird communities have changed over the 12-year period.

6:00 AM: After biking 3 miles along a gravel road to the base of Snodgrass Mountain and hiking another 3 miles up the slope (including a break halfway to enjoy my PB&J for breakfast), I am ready to begin the point counts. I use the GPS unit to locate the coordinates of each survey point, and record all of the species that I hear and see during a five-minute period. These surveys require me to be very familiar with the songs and calls of bird species in the region, as the majority of my detections are auditory. I audio record all of my point counts so that I can go back and verify observations later if I am not certain about my real-time identification in the field. 

8:00 AM: After I finish the point counts, I sit down to eat a snack and enjoy the beautiful views around me. I begin my bike ride down the mountain, whizzing through fields full of blooming wildflowers.

Photo caption: Views of the sunrise and wildflowers from my trek up Snodgrass Mountain.

9:30 AM: I make it back to my home for the summer: the Rocky Mountain Biological Laboratory (RMBL), a thriving community of almost 200 field biologists, each studying one component within the greater ecosystem of the southern Rocky Mountains, united with the goal of recording long-term ecosystem responses to climate change. I head to the dining hall to grab more snacks and refill my water—field work can be exhausting and it’s especially important to stay hydrated.

10:00 AM: I head back to my cabin to enter my point count data from the morning. This involves transcribing my handwritten field notes on paper datasheets into a spreadsheet on my laptop. This is important for multiple reasons: it serves as a backup in case my paper datasheets are lost or damaged, and formatting an electronic file is the first step to performing statistical analyses to uncover trends and relationships in one’s data.

11:00 AM: Now it’s naptime! With a wake-up call before sunrise, I almost always need a nap to recharge after morning fieldwork. 

12:00 PM: I nap until lunchtime, and then I head down to the dining hall. RMBL has a great kitchen staff that makes sure the researchers are well fueled for their fieldwork, and the dining hall is a perfect place to meet other scientists and learn about all of the cool research taking place.

1:00 PM: After lunch, I take some time to look over my research report draft and make some edits to the methods section because a couple of aspects have changed since I began my surveys out in the field. Keeping good records on the methods used for surveys and experiments is very important because it ensures future researchers will be able to reproduce the experiments.

2:00 PM: Most afternoons, I work as part of the golden-mantled ground squirrel (GMGS) team on a long-term behavioral and spatial ecology project. This involves making rounds around the RMBL townsite looking for squirrels and recording their behavior in 10-minute periods. GMGS resemble chipmunks, but they are larger and can run faster. Another big component of the squirrel project is live trapping, where we bait small wire traps with peanut butter and place them around known squirrel territories. When we capture a squirrel, we put them into a special canvas bag for safe handling and we record morphological measurements such as weight and take hair samples for genetic analysis. The most exciting time is mid-summer when the squirrel pups emerge from their burrows. We try to trap pups every couple of weeks to monitor their growth.

From left to right: A GMGS inside a trap, data collection during GMGS trapping.

5:00 PM: After my shift with the squirrel team, I head back to my cabin to rest and relax until dinner. I also must pack up my camping gear, because the site I am surveying tomorrow morning is several miles up the valley from the field station, and it requires an overnight backpacking trip to get there. I pack a sleeping bag, sleeping pad, small 2-person tent, extra layers, water filter, and my survey equipment in a backpacking pack. At the dining hall during dinner, I will grab extra snacks and make a couple of PB&Js to carry with me. 

7:00PM: After dinner, myself and two of my fellow student researchers borrow a vehicle to drive to the beginning of the hiking trail that will take us to the research site. We park next to a beautiful alpine lake and begin the 3-mile hike. We will gain 1500 ft in elevation over the course of the hike, so the majority of the trail is unrelentlessly steep. The gorgeous views of the sunset over the mountains around us serve as a good distraction from the strenuous hike. Two miles into the hike, we reach a river crossing. We take off our hiking boots and socks and roll up our pants to wade through the ice-cold water. Once across the river, we know there’s only a mile left to climb, and the sunlight is fading fast so we push onwards to try and make it to the site before dark.

Once we get to the site, which consists of a series of ponds located along a rocky mountain slope, we quickly set up our tents and get ready for bed. The stars are incredible here—we can even see the outline of the milky way.

Left: View of Emerald Lake from trail above. Right: Sunset over the valley after the river crossing.

Photo caption: View down into the river valley at twilight.

9:30PM: Time for bed! After the sun sets, it gets very cold at this high elevation, and the most comfortable place to be is inside my warm sleeping bag. I set my alarm for 5:30AM and make sure all of my survey equipment is ready for the morning.

This summer I learned a lot about how to effectively design and conduct a field ecology research study—especially how to be flexible and creative when problems arise. Field research is challenging but it is so refreshing to work outside in such beautiful places, and it’s especially rewarding to explore the trends in one’s data after a season of hard work. That’s how I’ll be spending the winter months, as do many field ecologists outside of field season—crunching the numbers in order to reveal the mechanisms of change in the ecosystems around us.

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About Author

Lily is an undergraduate student at Georgia Tech studying biology. She is pursuing a career in evolutionary ecology research.

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.