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CAMERA
Related lesson plan
I See What You Did There
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.
Species behaviour
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.
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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.
Competition
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.
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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?
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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.
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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.
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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.
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