Shark Control
Project 2
500383779
University of Sydney | Math1005 | Lab Class 01 : (Wed 9am Carslaw Learning Studio 353)
Project 2 - Individual
1 Executive Summary
This report’s objective is to examine the Queensland Shark Control Program’s 2016 data set by posing two research topics.
Does the location influence the occurrence of sharks?
How does water temperature influence the frequency of shark appearances and shark species?
The primary findings indicated that tiger sharks were the most common species and that most of the species were caught in warmer water temperatures, between 23°C and 27°C. Further more reveal that different shark species exhibit distinct habitat preferences, with their distribution and movements linked to a multitude of environmental variables.
2 Report
2.1 Initial Data Analysis (IDA)
sharks = read.csv("sharks.csv")
#The first 10 rows of data
head(sharks,n=10)## Species.Name Date Area Location Latitude
## 1 AUSTRALIAN BLACKTIP 16/11/2016 Cairns Holloways Beach -16°49.82
## 2 BLACKTIP REEF WHALER 2/01/2016 Cairns Buchans Point Beach -16°43.56
## 3 BLACKTIP REEF WHALER 2/01/2016 Cairns Ellis Beach -16°43.3
## 4 BLACKTIP REEF WHALER 5/01/2016 Mackay Harbour Beach -21°7.08
## 5 BLACKTIP REEF WHALER 7/01/2016 Mackay Harbour Beach -21°7.1
## 6 BLACKTIP REEF WHALER 13/01/2016 Cairns Holloways Beach -16°49.89
## 7 BLACKTIP REEF WHALER 20/01/2016 Cairns Palm Cove -16°44.74
## 8 BLACKTIP REEF WHALER 15/02/2016 Townsville Alma Bay -19°9.06
## 9 BLACKTIP REEF WHALER 22/03/2016 Cairns Clifton Beach -16°45.58
## 10 BLACKTIP REEF WHALER 2/04/2016 Cairns Trinity Beach -16°46.5
## Longitude Length..m. Water.Temp..C. Month Day.of.Week
## 1 145°44.85 1.00 27 November Wednesday
## 2 145°39.78 0.70 27 January Saturday
## 3 145°39.01 1.50 27 January Saturday
## 4 149°13.62 2.20 26 January Tuesday
## 5 149°13.68 1.70 26 January Thursday
## 6 145°44.91 1.20 29 January Wednesday
## 7 145°40.53 0.75 30 January Wednesday
## 8 146°52.45 1.20 31 February Monday
## 9 145°40.93 0.80 29 March Tuesday
## 10 145°41.97 1.30 29 April Saturday#Overview of data frame
summary(sharks)## Species.Name Date Area Location
## Length:532 Length:532 Length:532 Length:532
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Latitude Longitude Length..m. Water.Temp..C.
## Length:532 Length:532 Min. :0.520 Min. :17.00
## Class :character Class :character 1st Qu.:1.200 1st Qu.:23.00
## Mode :character Mode :character Median :1.830 Median :25.00
## Mean :1.920 Mean :24.83
## 3rd Qu.:2.455 3rd Qu.:27.00
## Max. :4.100 Max. :32.00
## Month Day.of.Week
## Length:532 Length:532
## Class :character Class :character
## Mode :character Mode :character
##
##
## dim(sharks)## [1] 532 102.2 IDA : Background & Source
This data set is sourced from the Queensland government. It contains 532 rows and 10 categories (seen above).This data explores shark captures within Queensland in 2016 under the Queensland Shark Control Program. It investigates through species, date of capture, location, water temperature, size and sex.
Although this data is reliable because it comes from the Department of Agriculture and Fisheries, Queensland Government, it is out of date as it only looks at activities that occurred in 2016. As a result, it could not be as helpful in 2023 because resources have changed and capture rates may have gone up or down as a result of technological advancements. An additional disadvantage is that, because it concentrates on Queensland’s coastal regions, it wouldn’t be as beneficial globally.Additionally, this collection contains a few unidentified species that could lead to incorrect categorisation of sharks or a simple lack of identification, which could undermine the source.
Stakeholders such as Marine researchers and scientists may find use of this data for trend analysis. Travel and diving companies could benefit for trip scheduling. The study’s may also interest the general public, those concerned about shark populations and their impact on marine ecosystems.
## read in data
class(sharks)## [1] "data.frame"## show classification of variables
str(sharks)## 'data.frame': 532 obs. of 10 variables:
## $ Species.Name : chr "AUSTRALIAN BLACKTIP" "BLACKTIP REEF WHALER" "BLACKTIP REEF WHALER" "BLACKTIP REEF WHALER" ...
## $ Date : chr "16/11/2016" "2/01/2016" "2/01/2016" "5/01/2016" ...
## $ Area : chr "Cairns" "Cairns" "Cairns" "Mackay" ...
## $ Location : chr "Holloways Beach" "Buchans Point Beach" "Ellis Beach" "Harbour Beach" ...
## $ Latitude : chr "-16°49.82" "-16°43.56" "-16°43.3" "-21°7.08" ...
## $ Longitude : chr "145°44.85" "145°39.78" "145°39.01" "149°13.62" ...
## $ Length..m. : num 1 0.7 1.5 2.2 1.7 1.2 0.75 1.2 0.8 1.3 ...
## $ Water.Temp..C.: int 27 27 27 26 26 29 30 31 29 29 ...
## $ Month : chr "November" "January" "January" "January" ...
## $ Day.of.Week : chr "Wednesday" "Saturday" "Saturday" "Tuesday" ...2.3 IDA : Variables
The dataset provides a wide range of information by including variables that are quantitative as well as qualitative.
Key variables are:
Species (Character): Each species that has been caught, categorical and is therefore nominal (qualitative)
Water Temperature (Integer): Numerical field.
Length (Numerical): Is a numerical field (quantitative). Sharks measured in meters.
2.4 Research Question 1: Does the location influence the occurence of sharks?
location = table (sharks$Location)
location = sort (location,decreasing = TRUE)
location = location[1:10]
par(mar = c(12, 4, 2, 2))
barplot (location, xlab = "Numbers", ylab = "Location", main = "Frequent Location Captures", las = 2, col = sapply(10:24/24, hsv, 0.3, 0.9), las= 1, horiz = TRUE, cex.names = 0.5)
Shark distribution and movement vary depending on life stage, sex, time of day, and season, and these factors can also influence a shark’s vulnerability to harvest As seen above Tannum Sands has the most sharks captures. Tannum Sands is indeed a coastal located in the centre of the QLD region. This location’s proximity to both the North and South Pacific Oceans may be the reason why it draws more sharks than other areas. All year round, the typical water temperature is roughly 25°C, which is comfortable for sharks. Studies show that several species have a habitat preference. Shark distribution and movements have also been related to depth, temperature, tidal patterns, ocean currents, salinity levels, benthic characteristics (seabed structures), and dissolved oxygen concentrations (Speed et al., 2010). A study done by Monteforte et al showed that White and greynurse shark sightings appear to be significantly influenced by the distance to the closest estuary; the likelihood of seeing them increases with decreasing distance to the estuary. However, this factor does not account for a large variance in sightings for whaler species and bull sharks.
2.5 Research Question 2: How does water temperature influence the frequency of shark appearances and shark species?
## write code here
watertemp = sharks$Water.Temp..C.
range(watertemp)## [1] 17 32barplot(table(watertemp), xlab = "Temperature (°C)", ylab = "Frequency", sub ="Fig. 1: Frequency of Shark Captures in Different Water Temperatures")
month = (sharks$Month)
month <- factor(month, levels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"))
boxplot(watertemp ~ month, xlab= "Month", ylab= "Temperature (Celsius)" , main = "Water Temperatures vs Month ", las = 2, notch = FALSE,sub = "Figure 2", col=c("purple","blue","green","yellow","orange","red","cyan","skyblue","turquoise", "pink", "violet", "brown", cex.axis = 0.5 ))
# setting up for only top 15 species to be displayed
species = table (sharks$Species.Name)
species = sort (species,decreasing = TRUE)
species = species[1:15]
par(mar = c(12, 4, 2, 2))
barplot (species, xlab = "Numbers", ylab = "Species", main = "Species ", sub = "Figure 3: Top 15 most occured species in 2016" ,las = 2, col = sapply(10:24/24, hsv, 0.3, 0.9),las = 1, horiz = TRUE, cex.names = 0.5)
The temperature of the water, as seen above, varies from 17°C to 32°C. Queensland experiences a wide range of temperatures, however it often stays in the warmer temperatures. Queensland’s seas are warmer than those in places like Sydney since it is closer to the equator, making it more tropical. The distribution and physiology of marine organisms have been significantly impacted by the rise in water temperature over time. The ideal living conditions for marine animals vary depending on factors like pH and temperature (Hobday & Hartog). Sharks adjust their vertical motion in reaction to temperature changes at the sea surface. A significant amount of captures range between 23°C and 27°C. Figure 2 demonstrates that the months of December and February have the highest number of captures, indicating a correlation between higher water temperatures and a higher shark population. Sharks must sustain a specific body temperature in order to maximise their abilities and forage efficiently. Figure 3 indicates that the tiger sharks are the most often occuring species. Tiger sharks are more known to be seen in tropical and warm temperatures. This species is known to have bad interactions with humans (i.e. bites). Research done by Payne et al, concluded that when the water temperature rises above 22°C, tropical sharks—like tiger sharks—are most prevalent and exhibit their best swimming abilities. This is consistent with the statistical data in this report, as shark activity peaks at temperatures over 22°C.
2.6 Hypothesis testing
“Produces a hypothesis test with a careful assessment of assumptions and limitations. Interprets result in context.”
average water temp of tiger shark data is 25 degrees
Hypothesis is:
H0 :70% of species are tiger sharks H1 : more that 70% of species are tiger sharks
H0:p=0.7 vs H1:p>0.7
The observations are independent of each other
The sample size is large enough for the Central Limit Theorem. The sample size of captures is 532. The specific samples that were tiger sharks is 52.
and if the data is not greatly skewed, this assumption is satisfied.
mu = 0.7
sig = sqrt(((1-0.7)^2 * 70 + (0-0.7)^2 * 30)/100)
c(mu,sig)## [1] 0.7000000 0.4582576Sum:
n = 532
EV_sum = mu * n
SE_sum = sig * sqrt(n)
c(EV_sum, SE_sum)## [1] 372.40000 10.56977Mean:
n = 532
EV_mean = mu
SE_mean = sig / sqrt(n)
c(EV_mean, SE_mean)## [1] 0.70000000 0.01986799Test stats (sum) is
OV_sum = 207
test.stat_sum = (OV_sum - EV_sum)/SE_sum
test.stat_sum## [1] -15.6484Test stats (mean)
OV_mean = 207/532
test.stat_mean = (OV_mean - EV_mean)/SE_mean
test.stat_mean## [1] -15.6484P-value
pnorm(test.stat_sum,lower.tail = FALSE)## [1] 1Given the test statistic of -15.6484, it indicates that the observed sample proportion of tiger sharks (207) is significantly below the expected proportion (70%) based on the null hypothesis. The negative value of the test statistic indicates that the sample proportion is more than 30 standard errors below the expected proportion, which strongly supports the alternative hypothesis that more than 70% of the species are tiger sharks.
In summary, the results of this test suggest strong evidence to reject the null hypothesis in favor of the alternative hypothesis. It indicates that the proportion of tiger sharks in the dataset is significantly higher than 70%, which aligns with the research hypothesis (H1). However the p-value results indicates that the results of this test is not statistically significant.It suggests that there is very little or no evidence to reject the null hypothesis and implies that the observed data is entirely consistent with the null hypothesis.
3 References
Hobday , A., & Hartog, J. (n.d.). National Resources. Sea temperatures and climate change in Queensland. https://www.redmap.org.au/article/sea-temperatures-and-climate-change-in-queensland/
Monteforte, K. I., Butcher, P. A., Morris, S. G., & Kelaher, B. P. (2022). The relative abundance and occurrence of sharks off ocean beaches of New South Wales, Australia. Biology, 11(10), 1456. https://doi.org/10.3390/biology11101456
Payne, N. L., Meyer, C. G., Smith, J. A., Houghton, J. D., Barnett, A., Holmes, B. J., Nakamura, I., Papastamatiou, Y. P., Royer, M. A., Coffey, D. M., Anderson, J. M., Hutchinson, M. R., Sato, K., & Halsey, L. G. (2018). Combining abundance and performance data reveals how temperature regulates coastal occurrences and activity of a roaming Apex Predator. Global Change Biology, 24(5), 1884–1893. https://doi.org/10.1111/gcb.14088
Speed, C., Field, I., Meekan, M., & Bradshaw, C. (2010). Complexities of coastal shark movements and their implications for management. Marine Ecology Progress Series, 408, 275–293. https://doi.org/10.3354/meps08581