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Observation of a red fox, Vulpes vulpes, captured by a camera trap in a forested habitat in Pickens County, SC.
Above image and activity below is based on
Overview
Overview
Understanding the natural world requires not only observation, but also well-designed experiments in order to draw accurate conclusions. In this lab, you will use camera trap data to design and carry out a "data experiment" about mammal activity and behavior.
Objectives
Objectives
Students should be able to
Describe the key elements of a well-designed experiment
Identify independent, dependent, and controlled variables
Develop novel hypotheses using pre-recorded data
Test their hypotheses by aggregating and arranging data as needed
Produce visualizations that match their hypothesis
Interpret results and propose future research
Introduction
Introduction
Although scientists often collect data in the field or lab, they can also make use of existing datasets. NEON, the NSF's LTER program, and community science projects are just examples of large-scale datasets that provide monitoring or occurrence data that scientists can use. These datasets may be larger than any single scientist can collect and offer unique insight on ecological questions. Today we will use an existing dataset from camera traps to ask questions about patterns in mammal activity or diversity. This lab will allow you to design your own set of questions using the scientific method.
First, you'll explore a camera trap data set and review an example analysis of data for the red fox (Vulpes vulpes). Then, you'll plan your own data experiment by developing a research question, identifying relevant variables, and stating a hypothesis. Finally, you'll analyze the data and write up your results in a lab report.
Mammals can be extremely difficult to observe directly in the wild because they may inhabit dangerous or inaccessible terrain, be active primarily at night, or actively avoid researchers. Camera traps, remote motion-activated cameras with infrared sensors, can help researchers document mammal activity in a relatively inexpensive and non-intrusive way (Burton et al. 2015). Camera traps are used extensively in ecological research and have been used to answer questions about the distribution, abundance, and behavior of populations as well as to address questions about biodiversity and community structure of mammals (Trolliet et al. 2014).
A variety of factors influence patterns of mammal abundance and activity. Habitat choice is species-specific. Some species are more common in forested areas, while others select open areas. For example, in a study of mesocarnivores in Illinois, Lesmeister et al. (2015) found that bobcats avoided agricultural areas, coyotes were common in forested areas, and red and gray foxes preferred spatially complex habitats. Within a species, habitat choice depends upon a variety of factors, including foraging or hunting access as well the ability to avoid predators (e.g., Fattebert et al. 2019).
Human disturbance also influences the spatial distribution of mammals. As a consequence of habitat fragmentation and direct removal, large predators are usually less common in areas with high human footprints (Lesmeister et al. 2015; Nickel et al. 2020; Ritchie & Johnson 2009). Other mammals, like raccoons, are less affected by urban environments (e.g., Mims et al. 2022). In fact, raccoons may benefit from the removal of predators and from using human food waste as a resource (Bozek et al. 2007). The presence of both red foxes and striped skunks has been reported to be positively associated with human disturbance in landscapes as well (Lesmeister et al. 2015).
Different species of mammals have different diel activity patterns, meaning that they are more active at some times of day than others. Some, like raccoons, are nocturnal, meaning they are mostly active at night. Others, like squirrels, are diurnal, meaning they are primarily active during the day (Ikeda et al. 2016). Bobcats are crepuscular, or active during dawn and dusk (Lesmeister et al. 2015). Red foxes have no clear diel pattern; instead, they tend to be cathemeral, or sporadically active during the day and night (Ikeda et al. 2016). However, even in mammals with clear diel patterns, temporal activity may shift depending on specific conditions. For example, red deer are active during the day in some habitats but during the night in others, presumably in an effort to avoid different predators (Fattebert et al. 2019).
Human disturbance also alters diel activity patterns. Gaynor et al. (2018) reported a worldwide shift of wildlife towards nocturnality in response to human disturbance. Specifically, red foxes (Lovell et al. 2022) and nine-banded armadillos (DeGregorio et al. 2021) are more nocturnal in urban than rural environments. Even when human infrastructure does not alter the landscape, the temporary presence of humans in the environment has been linked to increased nocturnal activity in predators such as bobcats, coyotes, and pumas (Nickle et al. 2020). This effect is not universal because some mammals, such as the European badger, may not exhibit plasticity in diel activity (Lovell et al. 2022).
Methods
Methods
Data collection
Data collection
Beginning in late spring 2018, the members of Lander University’s Mammal Ecology Lab set up camera traps in Upstate South Carolina. Each camera is motion activated and records the date and time when each photograph is taken. We began with five camera stations, but we established additional stations over the next few years. Occasionally, we stop monitoring at particular camera stations because cameras that are frequently vandalized or stolen must be relocated. Currently, we have data from 26 cameras in Greenwood, Laurens, and Pickens Counties in South Carolina. The cameras are in six different sites: Grace Street Park in Greenwood County, on the campus of the Greenwood Genetics Center in Greenwood County, at Fellowship Camp and Conference Center in Laurens County, at Lake Greenwood State Park in Greenwood County, in undeveloped land near Table Rock State Park in Pickens County, and in the Jocassee Gorges Wilderness Area in Pickens County (Figure 1).
Figure 1: Map of camera trap locations. Top: Location of Upstate South Carolina within the Eastern United States. Bottom: Camera trap site locations in Greenwood, Laurens, and Pickens counties.
Figure 2 below shows examples of photographs captured by the camera traps. The locations of the cameras encompass several different types of habitats. Some are in forested areas with substantial tree canopy overhead. Some are in open fields. Other cameras are along the edge of habitats and are positioned in locations where forests transition to fields or to open water. We use the area directly surrounding the camera to classify each habitat type as either forest, edge, or open.
Figure 2: Example camera trap images. Note the differences in species, habitat type, and time of day. Top row: white-tailed deer, raccoon, red fox. Lower left: black bear. Remaining pictures (clockwise from upper left): gray squirrel, striped skunk, bobcat, armadillo, and coyote. Pictures have been cropped to fit.
The cameras also differ in the amount of human disturbance that occurs near them. Some sites are in urban areas with extensive landscape-level disturbance in the form of human infrastructure such as roads and bridges. Other sites are in relatively undeveloped land. The total length of roads and area of buildings within 1 km of the site were used to classify the lasting human disturbance (LHD) as high or low.
Because urban areas often have lots of artificial light, even at night, each station was also classified as having high or low artificial light at night (ALAN).
The stations also differ in the intensity of temporary human activity captured by each camera. Some cameras capture no human activity. Others record the occasional hiker or dog-walker. However, some cameras record frequent human disturbance, which may include people, dogs, farm animals, bicycles, cars, lawn mowers, and industrial vehicles. The frequency of images capturing disturbance, weighted by the intensity and duration of the disturbance, was used to classify each station as having either high, low, or no temporary human disturbance (THD).
You have access to a data file with the records of each photograph taken from May 2018 until August 2022. The file contains four sheets (tabs located at the bottom):
Camera Images contains observations of mammal activity. Each row represents a single image, and includes the camera site, station, habitat, THD level, LHD level, and ALAN level. The row also includes which species was identified, that species’ trophic level, the number of individuals in the image, and whether the animals were part of a group. Finally, each row includes information about when the image was taken, including the date, the season, the time, and whether the picture was taken during the day or at night. To characterize cathemeral activity, each row also includes information about the time the image was taken divided up into four categories; day, night, dawn, and dusk.
Metadata provided information on what each column in the "Camera Images" tab measures, including variable levels
Station Information provides information about the site, habitat type, deployment time, and human disturbance at each camera station.
Fox example gives an example of a research questions and analysis (more information below)
Part 1: Data exploration
Part 1: Data exploration
First, review the data file. Notice it's big!
How many rows are included in the Camera Images datasheet?
How many unique species wer
Classify each of the following variables as either quantitative or qualitative
month
species
habitat
number_indiv
Classify each of the following variables as either ordinal or discrete
day_night
ALAN
THD
trophic_level
How many levels (categories) does each of the following variables have?
habitat
day_night
trophic_level
season
Now take a look at the Fox example. What is the research question addressed by this analysis?
What are the independent (explanatory) variables in the fox example?
What is the dependent (response) variables in the fox example?
The fox example filters the dataset to include only observations of single species (the Red fox, Vulpes vulpes). Species is best characterized as what type of variable in this analysis?
The fox example reports each cell as a percent of its row, rather than raw counts. Why?
Consider a different research question: Do deer form groups more often in areas with high vs. low lasting human disturbance (LHD)?
Create a pivot table to address this question
Create and upload a bar graph (based on your pivot table) to address the question
Part 2: Planning your experiment
Part 2: Planning your experiment
What is your research question?
What will be your independent (explanatory) variable(s)? Make sure you list specific columns in the data sheet.
What are your dependent (response) variable(s)? Make sure you list columns in the datasheet.
What is your hypothesis? State it and justify your prediction using relevant background information.
Sketch or describe how your pivot table needs to be set up. What goes in the rows? The columns? What are the cell values (counts)? Do you need to filter the data?
Part 3: Lab report
Part 3: Lab report
After completing this exercise, you will write up the results of your proposed data experiment as submit a formal lab report. Your report should be brief (1000-1500 words) and be organized in the style of a scientific paper, with the following sections:
Introduction — context and rationale for your study, including references to past research. Use the background reading to motivate your specific question and hypothesis.
Methods — describe the dataset you were given, how you filtered and aggregated the data (your pivot table), and how you summarized the results (counts converted to proportions, etc.). Someone should be able to reproduce your analysis from this section.
Results — present your findings in words, a table (your pivot table), and at least one figure. Describe the patterns and the size of any differences without interpreting them here.
Discussion — explain and interpret your results and place them in context. Consider: Was your hypothesis supported by the patterns you observed? What biological explanations might account for the pattern (or lack of one)? What confounding factors (e.g., season, habitat, sampling effort, site differences) could influence your result, and why might they be hard to disentangle? What are the limitations of camera-trap data, and what would you do next?
References — at least three citations to peer-reviewed articles, formatted consistently (APA formatting is recommended). Several relevant sources appear in the background information above.
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