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What is this sensor?
Perhaps one of the most familiar tools in science, cameras allow us a literal glimpse into places that are hard to visit. Observations are key to the processes of science, and cameras allow us to make visual observations in environments that are difficult to access or study on a regular basis.
How does this sensor work?
Deep-sea cameras are essentially identical to any other camera, the only additions being a waterproof housing, remote recording, and remote control via the internet. Like any other digital camera, ocean cameras record imagery in the presence of light. Sensors within the camera translate this light into a digital code which is either recorded on a storage device within the camera (which is then collected by the researcher at a later date), or sent to a remote storage device via a cable or the internet.
Like our eyes, cameras need light to function, so data are only recorded when there is light for the camera to "see". Humans then review the imagery and extract biological information such as the presence of species, their size, and their activities (locomotion, predation, burrowing). In locations with sufficient natural light (e.g. shallow sites), cameras may record continuously. In deep locations, cameras require artificial light to record imagery. In many deep-sea environments, lights are used only sparingly to mitigate stress to organisms in these environments through light pollution. Furthermore, in order for data from different camera deployments to be comparable, imagery needs to be recorded at the same intervals and for the same periods of time each day.
What data are recorded?
Cameras can be used for different purposes, depending on the needs of researchers. For example, some cameras are used to record the habitat conditions, while others record the progress of experiments. Regardless, cameras allow researchers to monitor these places or processes without being present. Data from all Ocean Networks Canada cameras are archived in SeaTube Pro. In shallow locations, the camera may stream video continually during daylight hours.
What variables influence camera data?
What the camera "sees" will be largely dependent on outside variables, but the camera data are not affected. For example, if an area is experiencing low oxygen, as indicated by the oxygen sensor, the camera may record a landscape devoid of animals that depend on high levels of oxygen. The camera data are not being impacted by the low oxygen, but the environment in which it sits is. Equally, if the camera is in a shallow location and there is evidence of a phytoplankton bloom, the camera will not be affected by this, but it may end up recording a cloudy green soup through which other images are indistinguishable.
Cameras can also be largely affected by biofouling, especially in shallow locations. The camera is housed in a glass dome, which protects it from the water and pressure around it. Many marine organisms, such as barnacles and algae, settle on any available surface, which can cause the lens to cloud or even become completely dark. Several different measures can be taken to help mitigate biofouling, including coating the lens in special grease, setting a longer focal length (so the camera can "see" past growth on the lens) and placing the camera in deep locations where algae and barnacles can’t grow.
Application of Principles and Variables
Cameras allow us to make observations of the environment directly around their immediate vicinity. The imagery collected provides many insights into the specific environment and the organisms that live there. Below are some ways in which you can extract biological and ecological information from camera imagery. These are only examples and may not all be applicable to every situation.
Many different species may be seen on a camera at one time. You may choose to observe one particular species and make inferences about that species’ behaviour, or think about how all of those species co-exist together (e.g. predator-prey relationships). You may need to support your hypothesis with other data, such as published works and anecdotal evidence.
Species richness and diversity
You may observe several different species in the imagery, which would give you a measure of what biologists refer to as "species richness": the number of species present in an area of interest. Equally, you may also be able to determine how many individuals of each species are present. This will allow you to calculate an index of species diversity. By analyzing camera imagery, you may also be able to infer why animals live (and at what density) within the environment viewed by the camera.
Reaction to stimuli/environmental changes
Camera imagery may help you directly observe or infer animal behaviour based on the influences of other stimuli. For example, you may notice that animals face in different directions, depending on the current. You may also notice changes in behaviour due to abiotic factors such as weather, light, or biotic factors such as food.
Feeding and Predation
The camera may allow us to see evidence (or direct observations) of predation between predator and prey. Equally, the camera may allow us to see unique relationships between species, such as symbiosis, or parasitism, For example, shrimp may seek protection in the holes left by clams or you may view sea cucumbers and urchins feeding on chunks of seaweed or smaller bits of detritus.
You may be able to observe or infer competition between members of the same species (intraspecific competition) or different species (interspecific competition). This may be indirect competition, such as when two or more animals are feeding on the same resource or attempting to settle on the same space. The two animals are competing as one attempts to get the better share of resources from the other, but they are not directly prohibiting the other from succeeding. Direct competition, where an animal attempts to prevent another from using a resource (such as chasing the competition out of an area) may also be observed.
Additionally, you may notice that competition relationships change depending on available resources. For example, you may find that animals are not directly competitive when oxygen levels are high, but are extremely competitive as resources become depleted.
Ideas for classroom explorations
This section is intended to inspire you and your students to explore different ways of accessing, recording, and interpreting data. These suggestions can be used "as is", or can be freely modified to suit your needs. They can also be used to generate discussion and ideas, or as potential staring points for projects.
- Review video from several days or weeks. Are there observable changes over your time frame? What other data evidence would suggest a reason for this change?
- Monitor oxygen levels, chlorophyll, and atmospheric conditions: how do they contribute to what is viewed on the camera? Can your data help you make further predictions about when and where changes can be observed on the camera?
- Compare camera imagery before and after specific atmospheric events. For example, does the environment change during or after a heavy rain or periods of intense sunlight?
- Compare local recordings to those in other locations. Will one predict or precede the trend of another? What other factors may account for the change in what you see?
Ideas for projects
This section contains suggestions for long-term projects you and your students may be interested in investigating using the data. These projects may require support from multiple data sources, experts in the field, or additional experimentation.
- Compare the data and inquire about relationship among other seasonal trends. For example, during fish migrations is there a trend in the data? In successful spawning or fishing years, are there differences observed from unsuccessful spawning or fishing years?
- Compare data from several years. Can a seasonal or annual trend be determined? Can this trend be attributed to something using additional data, anecdotal evidence, or both?
- Compare multiple stations using the same variables.
- Investigate man-made corrections to changes or developments in the environment.
- Monitor behaviour and infer if/when an animal has specific response to stimuli. What could you do to illicit this response?
Common misconceptions or difficult concept elements
This section is intended to help you anticipate where students may struggle with difficult concept elements or ideas. We’ve noted content that may require additional support for students to fully understand, or content that may lead to misconceptions.
- Cameras do not self-regulate—i.e. the camera does not focus on things as they go in front of it, instead it has a set focal range. However, remotely-operated cameras can be controlled from shore via the internet; the scientist can pan and tilt the camera and even zoom in if needed.
- The camera is not directly affected by environmental variables (e.g. changes in temperature) where the camera sits. Changes in the environment that are noticed in other data sets may positively or negatively affect the species found there and camera imagery may help to confirm the impact to the environment when changes occur.
- Camera data often depend on other evidence to "explain" what is happening. For example, a researcher may assume no animals are present due to hypoxia. The researcher will need oxygen data to confirm this hypothesis.
- Camera data are not necessarily "event" based. For example, the camera may allow us to observe otherwise imperceptible changes over time, or to observe species presence or absence.