Determine if visitor number has an effect on shark movements and behaviours
Determine if decibel level has an effect on shark movements and behaviours
Determine if there are significant individual differences in behaviour
Identify and outline baseline levels of behaviour
Observed 2 species of sharks in 2 separate aquarium environments. Observations were taken in 15 min blocks per individual animal at random times of day from 10am-1pm and 2-5pm for 16 days. Observations were recorded electronically in tge Zoo Monitor application, interval behaviours (interval_channel_value) were recorded once every 1 min. and all occurrence behaviours (or rare behaviours; all_occurrence_value) were recorded anytime they occurred during the 15 min observation session.
session: unique session ID (each session is 15 min long and has one individual/one focal animal)
date_time: date and time of session
weekday: WE: weekend, W: weekday
hour: hour of the day
channel: All Occurrence: measured anytime a behaviour occurred (rare behaviours), Interval: behaviours recorded once every 60 sec (more common behaviours)
focal: Name of focal individual (leopard sharks: Katrina, Little girl, Notches, Old girl)
all_occurrence_value: frequency of rare behaviours measured over the 15 min observation session. Includes: Burst-swim, Face bang, Head-shake, Spy-hop, Surface touch, Turn in water column, Vertical swim, Yawn, Interactions (x8), Routes, Penguin(s) in the water.
- Interactions: Anytime the focal shark interacted with another animal in a positive /negative way (i.e. Pos. Interaction with shark, Neg. Interaction with penguin)
- Routes: No Route: when the shark is not swimming in a pattern, Route 1/2/3: when the focal shark is swimming in a loop pattern repeatedly. (only measured for 1 week out of the full 16 days)
- Penguin(s) in the water: Anytime this all-occurrence value is present in the 15 min. session it means there were penguins in the water. The whole 15-min session should be considered ‘with penguins in the water.’
frame_number: first minute to the 15th min. of observation blocks in the session (1 to 15)
interval_channel_value: Behaviour individual was partaking in at the exact point of the 60 second intervals. Includes: Glide, Other, Out of view, Rest, Sink, Still, Swim, Vertical.
speed: swimming speed, either Swim fast or Swim slow
depth: what depth the shark was at, 0 Floor, 1 Low, 2 Mid, 3 High, 4 Surface
observers: number of observers present, 0, 1_5 (1 to 5), 6_10 (6 to 10), >10
space_use_x: coordinates for space use on x axis
space_use_y: coordinates for space use on y axis
space_use_xy: coordinates for space use on xy axis
feeding_day: whether it was a feeding day or not, Yes (feeding day) or No (not feeding day)
front_tank: yes: at the front of the tank, blank: any other location in the tank
session: unique session ID (each session is 15 min long and has one individual/one focal animal)
date_time: date and time of session
weekday: WE: weekend, W: weekday
hour: hour of the day
channel: All Occurrence: measured anytime a behaviour occurred (rare behaviours), Interval: behaviours recorded once every 60 sec. (more common behaviours)
focal: Name of focal individual (epaulette sharks: Rebel, Nicole)
all_occurrence_value: frequency of rare behaviours measured over the 15 min observation session. Includes: Burst swim, Sand blow, Surface swim, Surface touch, Tail-stand, Yawn, Interactions (x7)
- Interactions: Anytime the focal shark interacted with another animal in a positive /negative way (i.e. Pos. Interaction with shark, Neg. Interaction with shark)
frame_number: first minute to the 15th min of observation blocks in the session (1 to 15)
interval_channel_value: Behaviour individual was partaking in at the exact point of the 60 second intervals. Includes: Glide, Inactive, Other, Out of view, Standing, Stationary active, Swim fast, Swim slow, Tail stand, Walk.
depth: what depth the shark was at, 0 Floor, 1 Low, 2 Mid, 3 High, 4 Surface
observers: number of observers present, 0, 1_5 (1 to 5), 6_10 (6 to 10), >10
space_use_x: coordinates for space use on x axis
space_use_y: coordinates for space use on y axis
space_use_xy: coordinates for space use on xy axis
feeding_day: whether it was a feeding day or not, Yes (feeding day) or No (not feeding day)
vents: number of ventilations per post session measuring minute (should be correlated with activity and repetitive behaviours because higher ventilations means higher activity)
shelter: Yes: shark taking refuge in shelter, blank: shark not taking refuge in shelter
session: session ID number (to be used for matching to lep_v5 and ep_v5 sheets)
la_eq: A-weighted equivalent sound level
max_level: maximum level measured during run time
lc_peak: the C-weighted peak sound pressure level
High activity and repetitive behaviours:
- Epaulette sharks (ep_v5): Burst swim, Surface swim, Surface touch, Tail-stand
- Leopard sharks (lep_v5): Burst swim, Face bang, Head-shake, Spy-hop, Surface touch, Turn in water column, Vertical swim.
Significance: use a statistical significance level of P < 0.05
Eps and Leps (together) Analysis: (ep_v5, lep_v5, and mic_decibels)
- To calculate cumulative visitor numbers during a 15-min observation period: multiply the occurrence (frequency) of each category in the following manner. The first category of visitors, the researcher only (0), by zero, the second category (1-5) by one, the third category (6-10) by two, and the fourth category (>10) by three; this will produce a scale 0– (?) of accumulative visitor numbers.
- Convert behavioural data (interval_channel_value) into percentages for each session
- Convert behavioural data (all_occurrence_value) into frequency counts (or relative frequency) for each session
- visualise with bar graphs
- Is there a significant difference between frequency of number of observers between leps and eps (0, 1-5, 6-10, and >10)
- ggplot bar graph with SD
- Is there evidence that observer numbers alone affect epaulette shark movement (movement defined as high activity and repetitive behaviours: all_occurrence_value)
- Is there a significantly different proportion of time spent doing (interval_channel_value) behaviours when there are 0 vs. more than 0 observers.
- visualise with ggplot and SD
- Establish noise threshold for Leq for eps.
- When the equivalent noise levels are higher than the mean, consider the noise higher than usual and expect a behavioural change. In other words, when the Leq is higher than L50, predict a behavioural response.
- Is there evidence that decibel levels alone (Leq only) significantly affect epaulette shark movement (movement defined as high activity and repetitive behaviours)
- Compare shelter use for sessions with 0 observers and sessions with 1-5, 6-10 and >10 observers. Is there a significant difference?
- Shelter use is occurring when: shelter = yes
- gg plot with SD
- Is number of vents correlated with high activity and repetitive behaviours and what is the relationship?
- Is number of vents correlated with number of visitors and what is the relationship?
- Is number of vents correlated with decibel levels (all 3 types) and what is the relationship?
- ggplot with SD to visualise
- Is there a significant difference between (all_occurrence_value) high activity and repetitive behaviours when it is a feeding day (Yes) vs. when it is not (No)
- Is there a significant difference between time spent inactive vs. all other interval behaviours when it is a feeding day?
- Compare number of positive interactions with number of observers and decibel levels. Is there a significant relationship here between number of positive interactions and observers, or number of positive interactions and decibel level?
- Compare depth (5 types) for sessions with 0 observers vs. sessions with >0 observers. Is there a significant difference with collective time spent at different depth for 0 vs. more than 0 observers.
- Individual variation: did individual sharks spend a significantly different ratio of time at different depth levels?
Separate Analysis (for leps – lep_v5 combined with mic_decibels by session)
- Convert behavioural data (interval_channel_value) into percentages (proportions) for each session
- Convert behavioural data (all_occurrence_value) into frequency counts (or relative frequencies) for each session
- visualise with bar graphs
- (covered above, so no need to repeat) Is there a significant difference between frequency of number of observers between leps and eps (0, 1-5, 6-10, >10)
- ggplot bar graph with SD
- Is there evidence that observer numbers alone affect leopard shark movement (movement defined as high activity and repetitive behaviours)
- Is there a significantly different proportion of time spent in (interval_channel_value) behaviours when there are 0 vs. more than 0 observers.
- Establish noise threshold for Leq for leps.
- When the equivalent noise levels are higher than the mean, consider the noise higher than usual and expect a behavioural change. In other words, when the Leq is higher than L50, predict a behavioural response.
- Is there evidence that decibel level alone (Leq) significantly affects leopard shark movement (movement defined as high activity and repetitive behaviours)?
- Compare space-use for sessions with 0 observers and sessions with 1-5, 6-10 and >10 observers
- Did use of the rest of the tank (not front; front_tank = ‘blank’) increase significantly when there were more than 0 observers?
- Did use of the front of the tank increase significantly when penguins were in the tank? (front_tank = yes)
- Penguins in tank defined as “Penguin(s) in the water” occurring at any time during the session in all_occurrence_value column.
- Compare depth (5 types) for sessions with 0 observers vs. sessions with >0 observers. Is there a significant difference with collective time spent at different depth for 0 vs. more than 0 observers.
- Individual variation: did individual sharks spend a significantly different ratio of time at different depth levels?
- visualise with ggplot and SD
- Compare swim speed (fast or slow) for sessions with 0 observers and sessions with >0 observers, is there a significant difference here?
- visualise with ggplot and SD
- Compare routes taken (no route vs. Route 1/2/3) for sessions with 0 observers and sessions with >0 observers, is there a significant difference here?
- Was there a significant difference between individuals in ratio of time spent in no route vs. in Route 1/2/3?
- Route frequency found in all_occurence_value
CS 340 Milestone One Guidelines and Rubric Overview: For this assignment, you will implement the fundamental operations of create, read, update,
Retail Transaction Programming Project Project Requirements: Develop a program to emulate a purchase transaction at a retail store. This
7COM1028 Secure Systems Programming Referral Coursework: Secure
Create a GUI program that:Accepts the following from a user:Item NameItem QuantityItem PriceAllows the user to create a file to store the sales receip
CS 340 Final Project Guidelines and Rubric Overview The final project will encompass developing a web service using a software stack and impleme
