Introduction

Seals are scientifically referred to as the Pinnipedia.There are about 33 species of seals in the world. Seals are generally located along the coasts as well as cold waters. Most seals are characterised by having a thick fat (blubber) which keeps them warm as well as dense fur. Despite the differences that exist between various seal species, they all have feet that are fin-shaped. Further, all the seals’ species are semi-aquatic marine mammals implying that a part of their lives is spent on land as well as the sea ice. Since the years that have passed, seals have encountered various threats such as the hunting of seals by humans which has led to the extinction of some seal species (National Geographic Society, 2021). Since past years seals have faced threats from human beings who hunt them down for meat, pelts and blubber. Further, fishermen blame the seals for decreased fish and thus hunt them down (World Wildlife Fund, 2021).

The scientific name for a harbour seal is PhocaVitulina while that of a grey seal is Halichoerus grypus (IFAW, 2021). The United Kingdom has more grey seals as compared to the harbour seal. The harbours seals exist in groups of about 100, 000 and keep far from one another to avoid any fights. The harbour seals are generally not able to drink the seawater and thus obtain much of their water from their prey. The seals mainly feed on herring, shellfish as well as crustaceans. The harbour seals generally prefer the rocky shores as well as the estuaries that are filled with sand. The seals in the United Kingdom are preyed upon by killer sharks and whales. They face threats such as pollution of the waters, getting stacked in abandoned fishing gears. There are unknown causes of decreased seals around Britain (PTES, 2019).

NatureScot (2021) posited that the northern hemisphere is home to a bigger population of harbour seals. About 100, 000 harbour seals are found in Europe. The United Kingdom is home to 30% of the world’s seal population out of which 80% are found in the waters of Scotland. One of the rare species of seals in the world is the grey seal. Grey seals are estimated to be about 400,000 globally. The United Kingdom contains approximately 40% of the grey seals out of which 90% breed at colonies in Scotland.  The harbour seals have a life span of between 20-30 years. Harbours greatly occupy the west coast of Scotland, the Northern Isles and the Hebrides. The harbour female normally gives birth during June and July. On the other hand, grey seals have a life span of about 20 to 30 years. To feed, grey seals can travel more than 100km (NatureScot, 2021).

According to Industry Nature Conservation Association [INCA] (2021) Tees forms one of the North Sea’s main estuaries. Tees which is found in the United Kingdom has endowed a large population, a port as well as several industries. It is of key importance to the economy of the United Kingdom. Tees are characterized for their wildlife habitats as well as animals such as the seals. The Tees Valley encounters a challenge of the need to work towards economic development and at the same time conserve nature.

In the 1800s, the seal population had decreased at a fast rate primarily due to pollution. Further, around the 1900s the seals had vanished from the estuary. Due to the recovery of the lower estuary, people were denied accessing the river and this reduced the disturbance to the seals. Further, the industries and regulators took a collective initiative of reducing the pollution of the estuary in the 1970s. As a result of these initiatives, the seals began to be seen in the estuary which consisted of a small population. Since then, the harbour and grey seals are said to have increased in the estuary (INCA, 2021).

The Tees is home to two types of seals that is the harbour seal and the grey seal. During the industrial period of the mid-20th century, seals had completed disappeared in the area. However, the last part of the century has been characterised by initiatives of improving the environment. Thus, the number of seals is growing as the years’ pass (BaldHiker, 2017).

Literature Review

Silva et al. (2021) investigated some of the risks that are related to overexploitation of a relatively stable seal population. The study employed a trend analysis on aerial survey data as well as somatic growth curves. Based on the real-life history parameters, the study conducted a population viability analysis. The study findings revealed since the seal population is capable of regaining 11% of its internal increase yearly, the population can support an extra mortality rate that is caused by moderate hunting and rare break out of diseases. However, a slight shift towards a decline in the growth potential makes even limited hunting risky for the population.

Similarly, Brasseur et al. (2018) examined the regional population variations in the recovery of harbour seals. The study employed data of moulting harbour seals and the pups of the period of 1974-2014. The study further employed models of population growth to determine whether there were population variations among regions. The use of these models incorporated the occurrence of two Phocine Distemper Virus (PDV) epizootics of 1988 and 2002. The study findings revealed that there was a higher recovery of the population in the Netherlands during the first epizootic. This finding was based on the proposition that due to the migration of seals from other areas, the Netherland population increased. Since other areas’ population growth rate was below or remained at the expected internal rate of about 13%. Further, loyalty to the breeding sites fostered the growth of the number of pulps in areas that were less affected.

Similarly, Sundqvist, Harkonen, Svensson and Harding (2012) conducted a study on the linking of climate change with population complexities in the Baltic ringed seals. A climatological model was employed in the study. The study established that the forecasted 30, 730 seals at end of the 21^st-century form only 16% of the past population size. Thus, reduction of the ice cover only will lead to a decline in the seal population growth rate.

Further, Thomas et al. (2019) examined the population of the British grey seals. An age-structured dynamic model was employed in the study. A framework of Bayesian state-space modelling was used as the study’s framework which enables the model to be connected to the data available and past distributions of the population parameters. The model was fitted onto data from 1984 to 2010 and independently selected statistics of an adult population that were from aerial surveys of hauled-out seals of 2008.  The study established that there was a gradual constant increase in the North Sea seal population while other regions experienced a gradual growth of seal population during the mid-1990s. In 2010, the total population of a 1-year-old seal was 116,100 which was an increase as opposed to the year 2009.

In the same vein, a study by Kiełpińska and Kowalski (2021) numerically modelled the grey seals population. The researcher employed expert data for the study. The study findings indicated that the population of the grey seals would be kept in check based on factors such as hunting, birth rate procedures as well as herd size. The researchers provided a tool for the government to employ and initiate the expected programs that limit the seals’ predator population size.

On the other hand, Russell et al. (2019) used data from 1987-2010 aerially surveyed colonies to determine the pup production. At least 45,000 pulps were born in the United Kingdom in 2010. The study established that the production of pulp attained asymptote stage at the Outer and Inner Hebrides and Orkney areas.  The North Sea experienced an exponential increase in the population. The study established that the observed temporal and changing geography in the growth patterns of the colony revealed increased risks of employing such sites in formulating expansive management policies.

Similarly, Jones et al. (2015) conducted a study on a 2-decade sea movement data and terrestrial count data of grey and harbour seals to reveal the extensive map of the distribution of these species.  The study results established that grey seals utilize the offshore areas that are near or linked with their haul-out area. On the other hand, the harbour seals were found 50 km off the coastline. Therefore, the results indicated that the harbour seals had different conservation needs to that of the grey seals. The study suggested that spatial prioritization was not a relevant geographical strategy even with species of the same category.

          Aims

The existing body of knowledge has conducted investigations of other parameters that influence the seal population such as climate change, human exploitation and pollution. There are limited studies that have explored factors such as the species and sites as causes of seal population variation. Thus, the current study will examine the following objectives:

1. To determine how sites affect the average count of seals in Tees
2. To examine the effect of species on the average count of seals in Tees

Methods

Data collection

The study employed a seal data set collected from 2007 to 2010 from the Industry Nature Conservation Association [INCA]. Data were mainly collected in mid-June, Late June, early July and mid-September which are the birthing period for the seals. The seal count was also collected in August and early September which form the moulting period. The sites where the data was collected comprised of the following: Sites A, B, C, Spit, Wall, D and E.

Data analysis

The independent variables include the sites and the species of the seals. The site is a categorical variable that comprises seven levels that include: sites A, B, C, Spit, Wall, D and E. The species is also a categorical variable and has two levels that include harbour and grey seals. The average count (outcome variable) is a scale variable that shows the average number of seals found in each site or location. The study will use Statistical Packages for Social Sciences (SPSS) software to carry out descriptive and inferential statistics.

The study will first test for assumptions of normality using SPSS by first generating residuals through the following steps (analyse>General Linear Model>univariate>Transfer average count into dependent variable box>transfer sites and species into fixed factor(s) box>save>check unstandardized>continue>okay. To check for normality the study will undertake the following steps (analyse>descriptive statistics>explore>transfer the generated residual variable into the dependent box>plots>uncheck stem and leaf>check histogram>check normality plots with test>continue>okay.

In the case of two-way ANOVA data, we check whether its residual is normally distributed. If the residuals are not normally distributed, two-way ANOVA is still conducted on the data as it is considered as being robust towards the violation of the assumption of normality (Wijker, 2021).

Descriptive statistics will also be carried out in SPSS through the following steps (analyse-General Linear Model>univariate>Transfer average count into dependent variable box>transfer sites and species into fixed factor(s) box>options>transfer all three variables into the display means for box>check descriptive statistics>continue>okay. The study will also use excel to generate various figures.

The study will use a two-way ANOVA as the statistical test. This is because two-way ANOVA is used to analyse two categorical variables affecting one continuous outcome variable. Kim (2014) concurs with the current study argument by highlighting that a two-way ANOVA test for testing relationships is used in the presence of two categorical variables and one continuous outcome variable.

Results

Normality

Table 1

Test for Normality

	Kolmogorov-Smirnova	Shapiro-Wilk
Statistic	df	Sig.	Statistic	df	Sig.
Residual for average.count	.190	210	.000	.897	210	.000
a. Lilliefors Significance Correction

A non-significant test (>0.05) implies that the assumptions of normality have not been violated. Since the Shapiro-Wilk test [(210) =0.190, <0.001] is significant, the assumption for normality has been violated. The residual for average count is not normally distributed.

Descriptive Statistics

Table 2

Descriptive statistics

Descriptive Statistics
Species	Site	Mean	Std. Deviation	N
GREY	A	.793333	.4636296	15
B	.093333	.1099784	15
C	1.020000	1.3444064	15
D	1.162000	1.0792603	15
E	.340000	.5803940	15
Spit	5.560000	3.3640537	15
Wall	1.436667	.8255878	15
Total	1.486476	2.2517177	105
HARBOUR	A	19.386667	4.2011676	15
B	1.273333	1.7657320	15
C	3.720000	4.2631645	15
D	.126667	.2282438	15
E	2.686667	3.0189560	15
Spit	8.206667	6.6996233	15
Wall	10.880000	7.7109570	15
Total	6.611429	7.7884198	105
Total	A	10.090000	9.9011441	30
B	.683333	1.3678786	30
C	2.370000	3.3958545	30
D	.644333	.9298857	30
E	1.513333	2.4467760	30
Spit	6.883333	5.3799169	30
Wall	6.158333	7.2177712	30
Total	4.048952	6.2693949	210

As shown in Table 1, site A has 15 grey seals (M=0.7933, SD=0.4636), site B contains 15 grey seals (M=.0933, SD=.10998), site C has 15 grey seals (M=1.0200, SD=1.3444) and site D has 15 grey seals (M=1.1620, SD=1.0792). On the other hand, site E has 15 greys (M=.3400, SD=.5803) and site Spit has 15 greys (M=5.5600, SD=3.3641). Lastly, the site wall has 15 greys (M=1.4367, SD=.82559).

As shown in Table 2, site A has15 harbour seals (M=19.3867, SD=4.20117) and site B 15 harbour M=1.2733, SD=1.76573). While site C has 15 harbours (M=3.7200, SD=4.26316), site D has 15 harbours (M=.1267, SD=.2282). Site E has 15 harbours (M=2.6867, SD=3.0190) and site Spit has 15 harbours (M=8.2067, SD=7.71096). Lastly, the site wall has 15 harbours (M=10.8800, SD=7.78842).

The total number of sites had 105 grey seals (M=1.4865, SD=2.25172) and 105 harbour seals (M=6.6114, SD=7.78842)

GGplot Graph

Figure 1 Distribution of the average counts by site and species

Inferential Statistics

Two-Way ANOVA Test

Levene test

The Levene test is used for testing the homogeneity of variance. A non-significant test indicates that the data assumes equal variances.

Levene’s Test of Equality of Error Variances
Dependent Variable: average.count
F	df1	df2	Sig.
23.153	13	196	.000

Table 3

As shown in Table 3, the data does not assume equal variances since the Levene test statistic is 23.153 (13, 196) degrees of freedom with a p-value less than 0.05 (<0.001).

1. To Determine How Sites Affect the Average Count of Seals in Tees

Table 4

To Determine How Sites Affect the Average Count of Seals in Tees

Dependent Variable: average.count
	Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Contrast	2434.336	6	405.723	33.813	.000	.509
Error	2351.815	196	11.999

As shown in Table 4, the site has a significant effect on the average count of seals [F (6, 196) =33.813, <0.001]. The partial Eta squares indicate that 50.9% of total variations in the average counts are explained by sites

1. To Examine the Effect of Species on the Average Count of Seals in Tees

Table 5

To Examine the Effect of Species on the Average Count of Seals in Tees

Dependent Variable: average.count
	Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Contrast	1378.920	1	1378.920	114.919	.000	.370
Error	2351.815	196	11.999

Table 5 shows that species have a statistically significant effect on the average count of seals [F (1, 196) =114.919, <0.001]. The partial eta squared shows that 37% of the total variations in the average count of seals is explained by species.

Table 6

Homogenous Subsets

Ryan-Einot-Gabriel-Welsch Range
Site	N	Subset
1	2	3
D	30	.6443
B	30	.6833
E	30	1.5133
C	30	2.3700
Wall	30		6.1583
Spit	30		6.8833
A	30			10.0900
Sig.		.351	.850	1.000

Table 6 shows that Wall and Spit are not statistically different as they are in the same subset. Similarly, sites D, B, E and C are not statistically different. However, Site A is statistically different from site spit and wall as well as sites D, B, E and C. In addition, sites wall and spit are statistically different from sites D, B, E and C.

 

Discussion

Main findings

The study findings indicate that sites have a statistically significant effect on the average count of seals. The current study findings similarly affirm the current studies. Brasseur et al. (2018) examined the regional population variations in the recovery of harbour seals. The study findings revealed that there was a higher recovery of the population in the Netherlands during the first epizootic. This finding was based on the proposition that due to the migration of seals from other areas, the Netherland population increased. Since other areas’ population growth rate was below or remained at the expected internal rate of about 13%. Further, loyalty to the breeding sites fostered the growth of the number of pulps in areas that were less affected.

The current study also found that species had a significant effect on the average count of sales. The existing body of knowledge concurs with current study findings. For instance, Kiełpińska and Kowalski (2021) numerically modelled the grey seals population. The study results indicated that the population of the grey seals would be kept in check based on factors such as hunting, birth rate procedures as well as herd size. Thus, the characteristics of a species also determined its population size such as the birth rate procedures.

Limitations

Putting into account the characteristics of the harbour and grey seals (sampling units) and their aquatic environment, miscounting was a limitation to the study. The counts may not be accurate due to the seals’ movement in the waters.

Future recommendations 

1. The study recommends that researchers who intend to explore studies that are in line with the current study should also include other factors that affect the average count of seals aside from those employed in the current study. This is because the factors selected in the study did not entirely explain the total variations of the average count of the seals.
2. Future studies should focus on investigating seals from other areas as opposed to one area to allow the generalization of study findings. Since the current study focused on one area (Tees).
3. Researchers should also employ a larger sample size to generalize the findings since the current used a dataset of 2007-2010.

Summary

The study aimed was guided by the following study objectives: to determine how sites affect the average count of seals in Tees and to examine the effect of species on the average count of seals in Tees. The study used a dataset of 2007-2010 and used Statistical software for Social Sciences to analyse the data. The descriptive statistics used in the study included means, standard deviations. Two-Way ANOVA was used as the statistical test for inferential analysis. The study results revealed that species, as well as sites, have a statistically significant effect on the average count of seals in Tees. The study recommended that future studies should employ a larger sample size to allow the generalization of study findings.

Implications for management?

The conservation management should put into place conservation strategies that incorporate characteristics of different species of seals and their various habitats to be relevant and effective.

Conclusions

The study made the following conclusions based on the study findings:

Species have a significant effect on the number of seal counts in the aquatic ecosystem. There are variations in the population of seals based on the species.

The seals’ counts differ across various sites in which they are located. Some sites may form habitats of a greater number of seals as opposed to others due to the migration of seals.

References

Brasseur, S. M., Reijnders, P. J., Cremer, J., Meesters, E., Kirkwood, R., Jensen, L. F., … & Aarts, G. (2018). Echoes from the past: regional variations in recovery within a harbour seal population. PLoS One, 13(1), e0189674.

Common (or harbour) seal. (2019, September 6). People’s Trust for Endangered Species. https://ptes.org/get-informed/facts-figures/common-harbour-seal/

IFAW (2021). Seals. https://www.ifaw.org/uk/animals/seals

INCA (2021). Wildlife Where You Least Expect it. http://www.inca.uk.com/

Kiełpińska, J., & Kowalski, P. A. (2021). Numerical modelling of the population of grey seal (Halichoerus grypus) from the Baltic Sea in the context of reduction of damage to the fishing economy. Ecological Indicators, 124, 107423. https://doi.org/10.1016/j.ecolind.2021.107423

Seals, facts and photos. (2021). National Geographic. https://www.nationalgeographic.com/animals/mammals/facts/seals-pinnipeds-walruses-sea-lions

Seals. (2021). Seals. World Wildlife Fund. https://www.worldwildlife.org/species/seals

Seals. (2021, April 5). NatureScot. https://www.nature.scot/plants-animals-and-fungi/mammals/marine-mammals/seals

Silva, W. T., Bottagisio, E., Härkönen, T., Galatius, A., Olsen, M. T., & Harding, K. C. (2021). Risk for overexploiting a seemingly stable seal population: influence of multiple stressors and hunting. Ecosphere, 12(1), e03343.

Sundqvist, L., Harkonen, T., Svensson, C. J., & Harding, K. C. (2012). Linking climate trends to population dynamics in the Baltic ringed seal: Impacts of historical and future winter temperatures. AMBIO, 41(8), 865-872. https://doi.org/10.1007/s13280-012-0334-x

The seals of seal sands, Teesside. (2017, July 23). BaldHiker. https://www.baldhiker.com/2017/07/23/the-seals-of-seal-sands-teesside/

Thomas, L., Russell, D. J., Duck, C. D., Morris, C. D., Lonergan, M., Empacher, F., … & Harwood, J. (2019). Modelling the population size and dynamics of the British grey seal. Aquatic Conservation: Marine and Freshwater Ecosystems, 29, 6-23.

Appendix

Figure1: A Seal in Tess Valley

Source: The Nothern Echo (2017)