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Hidden secrets of Magalies Mountains: Guess what I discovered this past weekend!!

Few months ago I met this lady called Karen. She is an environmentalist at heart, loves the mountains and nature. In her travels exploring different mountains, she got an inspiration and started an organization called SOAPKidz, soap meaning Sun Rise On Africa’s Peak. Her believe is that it’s pure magic when nature and kids meet.

Anywhere, back to my weekend. So Karen invited me to what she called “de-weeding” weekend at the Magaliesberg mountains. As she explained, basically the idea was to go out to the mountains and get rid of the invasive plants. I got a few friends and thought hey let us pretend for just two days that we have no assignments, meetings, exams, Facebook, and all that…and just pack a sleeping bag enroute Magaliesberg.

Saturday morning we were out in the bushes pulling invasive plants by their roots, getting scratched, falling into unseen streams…and I whole lot of fun stuff like this!

In class, we always speak about all types of solutions towards such problems like invasive plants. However, I have never physically seen it in action, let alone contribute towards executing the solution itself!!! I never knew of “Friends of the Mountains” and the hard work that goes into just keeping the Magaliesberg streams flowing. If it weren’t for the group of these old professors (botanist, environmentalist, engineers..) and 7 year old explores! this fountain, the stream and the Magalies wouldn’t be what it is today…for all we know, we wouldn’t be able to appreciate such view close by. And that is the hidden secret of the Magaliesberg mountains, peaceful, healing and just too awesome a place to not mention. I bet you didn’t know that existed:p

And that was my weekend with Mountain Club South Africa. More other cool things we did: *skin deeping in the bush!

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Variables affecting vigilance behaviour in cape sparrows (Passer melanurus) populations around the University of Pretoria, South Africa

Abstract

Vigilance is the ability to maintain alertness and attention over a long period of time. Variables influencing the vigilance behaviour of Passer melanurus, cape sparrows occurring around the University of Pretoria main campus were examined. The study found that vigilance decreases with increase in the number of individuals in a flock, number of females, number of males, ratio of females and males, cover distance, and the flock distance from the observers. A positive correlation was found between traffic (number of disturbances) and vigilance percentage occurrence. Furthermore, the study also found that there was no significant difference between vigilance in females and males, as well as vigilance between different times of the day (morning, afternoon, and evening). Different variables affect this behaviour in different ways. Our results supported hypothesis one (vigilance decreased with increasing number of individuals in a group) and hypothesis three (vigilance increased with increasing number of disturbances). However, hypothesis two (males were not more vigilant than females).

 

Keywords: vigilance, behaviour, disturbance, cape sparrow

 

Introduction

Animals spend much time feeding but they often stop foraging to scan surroundings, and such behaviour is referred to as vigilance (Wang et al. 2010). Parasuraman (2009) define vigilance as the ability to maintain alertness and attention over a long period of time. The function of vigilance is one topic still under much debate. Karuse and Ruxton (2002) propose that vigilance is an anti-predatory adaptation that evolved to allow individuals to detect and flee from predators before attack. Vigilance may also be used as a way to detect food and companions (Beauchamp 2001). Many studies relating to vigilance have come up with different hypothesis as a means to explain this behaviour. In animals that live in groups, the amount of time allocated to vigilance decreases with growing group size given that many eyes and ears are available to detect predators, referred to as “many-eyes hypothesis” (Pulliam 1973). In such groups, more time is then allocated to other fitness-enhancing activities such as foraging (Caro 2005). Foster and Treherne (1981) furthermore proposed that individuals in a group dilute individual risk of attack by predators, and referred it as the risk-dilution hypothesis.

An alternative hypothesis to the predation hypothesis is the “competition hypothesis”, which states that vigilance group size effect reflects scramble competition for limited resources (Clark and Mangel 1986). In simple terms, in an area where food is limited and group size increases, an animal will increase its feeding rate (i.e. reduces time allocate to vigilance) to gain maximum amount of food supply (Clark and Mangel 1986). In their study on finches, Beauchamp and Livoreil (1997) suggested that such competitive effects may be the primary driving force behind vigilance group size effect. However, “predation hypothesis” still provides the capital explanation of the pervasive group size effect (Roberts 1996). It is also important to remember that searching for food and vigilance are mutually exclusive as most animals need to look up to detect predators or competitors and need to lower their heads to detect food and forage (Proctor et al. 2006). The trade-offs have been studied to a vast extent in many bird species (McNamara & Houston 1992, Proctor et al. 2006), making birds ideal study species of such topic.

While predation is viewed as activity of natural enemies, other disturbances (not necessarily natural) may play a role influencing vigilance (Frid and Dill 2002). Take a group of birds for example, apart from the obvious predators; the time allocated to vigilance may be affected by human presence, traffic, and weather. To address these issues, we looked at vigilance behaviour in Cape sparrows, Passer melanurus (Passeridae: Müller, 1776). These birds are common species in Southern Africa, occurring in arid savannah, woodlands, farmlands and human habitations (Kopi 2013). The aim of the current study is to describe the effects of different disturbances to vigilance in P. melanurus populations occurring around University of Pretoria main campus.         Our hypotheses are that; (1) vigilance will decrease with increasing number of individuals in a group, (2) males should be more vigilant than females (Burger and Gochfeld 1994), and (3) vigilance will increase with increasing number of disturbances.

Materials and methods

Study species

Cape sparrow, Passer melanurus was the study species (Passeriformes; passeridae). The Cape Sparrow has a height of 16 cm and weighs around 29 grams. Male and female differs slightly. The male has a black head and a black with broad white semi-circles on either side, from behind the eye to the side of the throat. The mantle is grey and the under-parts white. The eyes are brown and the bill black. The female is almost similar, but duller with the head and breast dark grey. It is a social bird usually seen in small family groups or in large flocks. The Cape Sparrow is primarily a seed-eater, though it also consumes small insects such as butterflies, bees, wasps, locusts and ants. It feeds mostly on the ground.

Study site

The experiment was carried out at the University of Pretoria (25.7536° S, 28.2297° E), South Africa. The area is composed of different trees with the jacaranda species (Jacaranda mimosifolia, Lamiales: Bignoniacea) being the dominant, shrub species, and a lot of high buildings. Six sites were chosen to represent low, medium and highly disturbed areas (two sites per each criterion) (Table 1). Sampling was done at three replicates for each study site.

Table 1: Different study sites that were chosen for the study that looked at different variables affecting vigilance of cape sparrows, Passer melanurus, at the University of Pretoria Hatfield campus, South Africa.

Criterion High disturbance Intermediate disturbance Low disturbance
Study site Grass area in-front of Client Service Centre (CSC) Grass area around Theology building Grass area around Drama building
Student centre Grass area in-front of Chemistry building Grass area around Admin building

Experimental protocol

Surveyors were divided into different groups ranging from one to six, with three to six members in a group. Each group was assigned study areas to sample during the day. Sampling was conducted in the morning (between 7:00-8:00), afternoon (between 12:00-13:00), and evening (between 17:00-18:00). The survey was divided into three categories namely, scan, focal and approach sampling. Before the sampling began, the observers had to record distance (m) from flock, distance (m) from cover, the numbers in the flock and the number of males and females in the flock. Following that, observers had to wait for at least two minutes to allow the birds to settle and get familiar with observers’ presence.

Scan and focal sampling were done simultaneously by different observers of the same group. The objectives of scanning were to record the number of females and males of the flock, as well as how many of those individuals were vigilant and how many were not vigilant. Vigilance in this study is defined as rising of the head and moving it sideways (left and right). Focal sampling focused on one individual of the flock and recording the time spend vigilant, time spent non-vigilant, total time of observation, and reasons for flying away (if it happens that the bird flees away before observation time (two minutes) is over). In flocks where it was possible, focal sampling was done for one male and one female of the flock.

Approach sampling is where observers approached the bird and recorded the distance at which the bird flees away. In addition to that the number of disturbances (traffic, number of people, other animals such as cats, etc.) was also recorded. Any other variables that observers felt influenced the birds’ vigilance and reasons for flying away were also recorded.

 

Data analysis

All data from all groups was captured in Microsoft Excel (Microsoft 2010). In addition, amount of time spent being vigilant and being non vigilant were calculated as a percentage, and the ratio of males and females was also calculated. Using Statistica ( StaSoft, Statistica 12), a correlation analysis was performed between vigilance percentage and the following variables: flock number, number of females, number of males, ratio of females and males, cover distance, flock distance, and traffic. All correlations were under case-wise deletion of missing data. Furthermore, an ANOVA two-way test was performed to analyse the amount of traffic for each time period across the five day sampling period. Using data generated by the ANOVA test, a bar graph was constructed in Microsoft Excel. A T-test independent by groups was performed to analyse time spent vigilant between females and males. Another ANOVA two-way test was performed to see how vigilance differs during different times of the day.

 

Results

Normality tests showed that all data used in analysis were normally distributed and hence basic statistics (not non-parametric tests) were used further to analyse the data. The strength of association between vigilance percentage and the following variables, flock number (Correlation test: N=184, p=-0.088586), number of females (Correlation test: N=184, p=-0.0759), number of males (Correlation test: N=184, p=-0.066004), ratio of females and males (Correlation test: N=184, p=-0.012488), cover distance (Correlation test: N=184, p=-0.104420), flock distance (Correlation test: N=184, p=-0.116075) was negative. In other words, each variable was inversely proportional to percentage of time spent being vigilant (Fig 1-6). However, correlation between percentage vigilance and traffic showed a positive relationship (Correlation test: N=184, p=-0.020517) (Fig. 7).

The study also found that more traffic was observed during the afternoon, followed by evening, and morning with the least amount of disturbances (Fig 8.)

Fig.8: The number of disturbances observed during the study of vigilance in cape sparrows, Passer melanurus, and populations occurring around the University of Pretoria main campus. Observations were done in the morning (07:00-08:00), afternoon (12:00-13:00), and evening (17:00-18:00) for a period of five days across six different sites with three replicates for each site.

Vigilance between females (T-test: (N=20) T-value= 0.172452, df= 16, P>0.05) and males (T-test: (N=20) T-value= 0.390345, df= 16, P>0.05) showed no significant difference (Fig. 9). Vigilance showed no significant difference across different times of the day (Fig. 10)

Discussion

The study revealed interesting results regarding the influence of different variables on vigilance in cape sparrows. An inverse relationship was observed between different variables and percentage of vigilance observed. These results indicate that vigilance decreases with increase in the number of individuals in a flock, number of females, number of males, ratio of females and males, cover distance, and the flock distance from the observers. The “many-eyes” hypothesis stipulated by Pulliam (1973) is being supported in this case. In a flock where there are a lot of individuals, prey detection is easier and becomes a shared duty. And thus individuals will resort more time into fitness-enhancing activities such as foraging (Caro 2005).

Furthermore the study found that there was no difference in vigilance between females and males. According to Betram (1980), males are expected to be more vigilant than females. This is explained by Artiss and Martin (1995) where bird species showcase mate guarding or protection of paternity. Correlation between percentage vigilance and traffic (number of disturbances) showed a positive relationship. The results indicate that as number of disturbances increase so does vigilance. This was expected because of the number of people present on campus and the amount of other disturbances (cars, small mammals, sounds, etc.) have been shown to disturb birds and trigger vigilance behaviour (Bekoff 1995). However, we do not consider human disturbance as a major role in this study because cape sparrows are considered to be well adapted to human habitations (Hockey et al. 2005).

There was no significant difference in vigilance behaviour across different times of the day. At all times, birds need to maintain alertness because it is not known when predators might attack.

Errors that might have affected the results of the study include consistency in approaching the birds. Different observers used different methods, some walked to the birds slowly, whereas others literally tried to scare the birds away, and in many events than most, succeeded. Furthermore, the some of the observers of the study were not very familiar with the overall appearance of the bird species under study, and therefore might have confused it with other sparrows occurring around the study area. And lastly, vigilance might have been confused with the behaviour where a bird is trying to swallow bolus of food, to detect the feeding rates of its companions, to look for nearby food, or to look for conspecifics (Betram 1980). And due to these errors, the data was not easy to analyse and interpret.

In conclusion, vigilance is not an easy behaviour to detect. Different variables affect this behaviour in different ways. Our results supported hypothesis one (vigilance decreased with increasing number of individuals in a group) and hypothesis three (vigilance increased with increasing number of disturbances). However, hypothesis two (males were not more vigilant than females).

References

Artrtiss T, Martin K (1995) Male vigilance in white-tailed ptarmigan, Lagopus leucurus: mate guarding or predator detection? Animal behaviour 49: 1259-1258.

Beauchamp G (2001) Should vigilance always decrease with group size? Behavioral Ecology and Sociobiology 51:47-52.

Bekoff M (1995) Vigilance, flock size, and flock geometry: Information gathering by Western evening grosbeaks (Aves, Fringillidae). Ethology 99(1-2): 150-161

Bertram BCR (1980) Vigilance and group size in ostriches. Animal behaviour 28: 278-286.

Burger J, Gochfeld M (1994) Vigilance in African mammals: differences among mothers, other females and males. Behaviour 131:153–164.

Caro TM (2005) Antipredator defenses in birds and mammals. University of Chicago Press, Chicago.

Clark C, Mangel M (1986) The evolutionary advantages of group foraging. Theoretical population biology 30:45–75.

Foster WA, Treherne JE (1981) Evidence for the dilution effect in the selfish herd from fish individuals on a marine insect. Nature 293: 466-467

Frid A, Dill L (2002) Human-caused disturbance stimuli as a form of predation risk. Conservation Ecology 6.

Hockey PAR, Dean WRJ, Ryan PJ (2005) Roberts – birds of southern Africa, viithed. The trustees of the john voelcker bird book fund, Cape Town.

Kopi G (2013) Nesting sites of cape sparrows Passer melanurus in Maloti/ Drankensberg, Southern Africa. Intern. Stud. Sparrows 37: 28-31

Krause J, Ruxton GD (2002) Living in Groups. Oxford University Press, New York.

Mcnamara JM, Houston AI (1992) Evolutionarily stable levels of vigilance as a function of group-size. Animal Behaviour 43: 641–658.

Parasuraman R, de Visser E, Clarke E, McGarry R, Hussey E, Shaw T, Thompson JC(2009) Detecting threat-related intentional actions of others: Effects of image quality, response mode, and target cueing on vigilance. Journal of Experimental Psychology: Applied 15(4): 275-290

Proctor CJ,Broom M, Ruxton GD (2006) Antipredator vigilance in birds: modelling the ‘edge ‘effect.Math. Biosci.199: 79-96

Pullian HR (1973) On the advantages of flocking. Theoretical biology 38: 419–422.

Roberts G (1996) Why individual vigilance declines as group sizeincreases. Anim Behav 51:1077–108

Wang Z, Zhongqiu L, Beauchamp G, Jiang Z (2010) Flock size and human disturbance affect vigilance of endangered red-crowned cranes (Grus japonensis). Biological Conservation doi:10.1016/j.biocon.2010.06.025

Appendix

Fig. 9: Summary ANOVA two-way test for vigilance of cape sparrow (Passer melanurus) populations around the University of Pretoria main campus grounds. The study lasted for five days with observation done in the morning (07:00-08:00), afternoon (12:00-13:00), and evening (17:00-18:00).
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Phylogenetic analysis of Spheniscidae (Penguins) based on cytochrome-b

¹Department of Zoology & Entomology, University of Pretoria, Pretoria, 0002, South |Africa

Abstract

The phylogenetic of order Spheniscidae, extant penguins, was studied. MEGA was used to analysed all collected data from different studies through GenBank. Nucleotide sequences were used to infer phylogenetic relationships between the different species of the six extant genera of order Spheniscidae. The gene under study was from mitochondrial genome, cytochrome b which is a coding gene that is highly conserved. Tamura Nei model (TN93+G) was the model of selection used for phonetic and cladistics analysis. Evolutionary trees including Neighbour-Joining, Minimum Evolution, Maximum Parsimony, Maximum Likelihood and Unweighted-Pair Group Method with Arithmetic means tree were constructed. Different genera showed similarity and some degree of homoplasy was observed, of which can lead to genetic variation. This study provides valuable information that can be further applied to conservation and solving of evolutionary relationships of penguins, of which still remains unresolved to this date.

Keywords: Penguins, phylogeny, phonetics, cladistics

Introduction

Biology

Spheniscidae is a family of all extant species of penguins. In this family there are six genera namely, Aptenodytes (Great penguins, Eudyptes (Crested penguins), Eudyptula (Little penguins), Megadyptes (Yellow-eyed penguins), Pygoscelis (Brush-tailed penguins), and Spheniscus (Banded penguins). These birds are inhabitants of Southern Hemisphere, including Antarctica, Argentina, Australia, Chile, New Zealand, and South Africa (Jadwiszczak 2009), and are classified as flightless birds. Large bodied populations live at higher altitudes than smaller bodied populations (Piper et al. 2007). Morphologically, they have black and white plumage, referred to as counter-shaded, and are fusiform in shape. Their wings have evolved into flippers, providing thrust during swimming. Their feathers are designed to keep water off the skin of the animal, thus providing insulation against cold. Webbed toes are present and function as a steering mechanism. Damage to surface can be corrected for by yearly moult. These birds also spend some time on land, and their feet are positioned underneath body thus allowing for bi-pedal walking locomotion on land achieved through small steps and hops. Their diet is mostly from krill, fish and squid. Most penguins breed in large colonies, with a few exceptions ().

Taxonomy

Kingdom Animalia
Phylum Chordata
Subphylum Vertebrata
Class Aves
Subclass Impennes
Infraclass Neoaves
Order Sphenisciformes
Family Spheniscidae
Genus Aptenodytes

  1. fosteri (Emperor penguin)
  2. patagonicus (King penguin)

Eudyptes

  1. chrysocome (Rockhopper penguin)
  2. chrysolophus (Macaroni penguin)
  3. pachyrhynchus (Fiordland penguin)
  4. robustus (Snares penguin)
  5. schlegeli (Royal penguin)
  6. sclateri (Erect-crested penguin)

Eudytula

  1. minor (Little penguin)

Megadyptes

  1. antipodes (Yellow-eyed penguin)

Spheniscus

  1. demersus (African penguin)
  2. humboldti (Humboldt penguin)
  3. magellanicus (Magellanic penguin)
  4. mendiculus (Galapagos penguin)

Pygoscelis

  1. adeliae (Adelie penguin)
  2. papua (Gentoo penguin)
  3. antarcticus (Chinstrap penguin)

(Meyers et al. 2012)

 

 

Evolutionary relationships of penguins to this date still remain unresolved to completion. This study aims to contribute to the understanding of the phylogeny of different species of Spheniscidae family of penguins. At least fifty penguin species are represented in the fossil record of the Southern Hemisphere and date back 60 million years to the Early Paleocene epoch. A common ancestry for penguins and tubenoses (Procellariiformes) has been suggested by the presence of tubular nostrils in fossil penguins and in the extant Little Penguin (Eudyptula minor). Penguins play a significant role in both aquatic and terrestrial ecosystems. Twelve penguin species are included in the IUCN Red List of Threatened Species; three of these (Eudyptes sclateri, Megadytpes antipodes, Spheniscus mendiculus) are listed as Endangered. Major threats to wild populations include: destruction of breeding habitat, human disturbance / hunting pressures, egg and guano collection, predation by introduced mammals, commercial fishing, oil spills and even hybridization with other penguin species. Understanding the phylogenetic history of these species can help aid conservation methods with tangible results. The inter-specific taxonomy will be inferred (by constructing phylogenetic trees) of the family using molecular analysis methods.

Methods and materials

Data collection

Cytochrome-b is a coding gene of fixed length that has no gaps in the alignment sequence. The gene offers the most sequence information available for different mammalian species and cab be useful in comparing sequence variability among species of same genus and same family (Castresana 2001). In this study we used cytochrome-b to resolve phylogenetic relationships between 16 species of different genera of family Spheniscidae. Oougroup. The programs MEGA 6 and MEGA 5.2 (Molecular Evolutionary Genetic Analyser) was used to analyse and collect data. The data for the analysis was retrieved from GenBank accessed through MEGA to generate the first sequence under nucleotides and using cytochrome-b as the gene of study. The BLAST (Basic Local Alignment Search Tool) function was then used in order to ensure homologous data was retrieved. The out-group taxa was also obtained through use of BLAST function. The GenBank accession number, species name, geographic distribution, and an article of reference were obtained in each case (Table 1).

Data analysis

Final dataset was obtained through alignment function and trimming at each end in MEGA that resulted in all species with same length. The number of viable sites, number of parsimony informative sites, the nucleotide frequencies of A, G, T, and C, as well as transition: transversion ratio (R) was obtained in MEGA. A hierarchical likelihood model selection test was performed to find the model best suited for the data, of which was later used in the phenetic and likelihood analyses. The above information served sufficient to construct a neighbour-joining (NJ) tree and a minimum-evolution tree. The similarities were compared and sum of branch lengths (SBL) for ME tree was recorded.

Maximum parsimony (MP) tree was obtained through parsimony analysis, and the consistency index (CI), retention index (RI), and rescaled consistency index (RCI) were noted for the different homoplasy indexes. Parsimony informative sites were also recorded and the tree with least evolutionary changes was taken as the optimal tree as this is criterion for parsimony. A maximum likelihood (ML) tree was constructed through likelihood analysis where the log likelihood (LogL) score and the final number of trees obtained by this score were recorded. All trees were constructed under bootstrap analysis with 1000 replications. Using a p-distance NJ, a molecular clock was constructed, which was later converted to unweighted-pair group method with arithmetic means (UPGMA) tree. And lastly, the likelihood molecular clock test was performed to test the rate of heterogeneity. In all molecular clock tests an evolutionary rate of 0.02 was assumed as this is the evolutionary rate of mammalian cytochrome-b.

Species name GenBank accession number Reference Geographic origin
Aptenodytes forsteri DQ137225.1 Proc. Biol. Sci. 273 (1582), 11-17 (2006) Antarctica coast
Aptenodytes patagonicus AF076044.1 Mol. Biol. Evol. 15 (10), 1360-1371 (1998) Falkalnads island, Argentina, Chile and South Georgia
Eudyptula minor KJ456273.1 Nature 509 (7499), 222-225 (2014) Australia and New Zealand
Eudyptes chrysocome AF076051.1 Mol. Biol. Evol. 15 (10), 1360-1371 (1998) Argentina, Chile, Falkland islands, New Zealand and South Africa
Eudyptes chrysolophus AF338610.1 Unpublished Antarctica
Eudyptes pachyrhynchus DQ137210.1 Proc. Biol. Sci. 273 (1582), 11-17 (2006) New Zealand
Eudyptes robustus DQ137216.1 Proc. Biol. Sci. 273 (1582), 11-17 (2006 New Zealand
Eudyptes schlegeli DQ137215.1 Proc. Biol. Sci. 273 (1582), 11-17 (2006) Australia
Eudyptes sclateri DQ137209.1 Proc. Biol. Sci. 273 (1582), 11-17 (2006) New Zealand
Pygoscelis adeliae KC875855.1 Mol. Phyl. Evol. 68 (2), 229-238 (2013) Antarctica, South Georgia and the South Sandwich Islands
Pygoscelis papua AB776018.1 Unpublished Antarctica
Pygoscelis antarcticus KF020634.1 Unpublished Antarctica
Spheniscus demerus KC914350.1 Gene 534 (1), 113-118 (2014) Chile and Peru
Spheniscus humboldti AB776012.1 Unpublished Chile and Peru
Spheniscus magellanicus AB776015.1 Unpublished Argentina; Brazil; Chile; Falkland Islands (Malvinas); Uruguay
Spheniscus mendiculus AF338602.1 Unpublished Ecuador (Galápagos)
Grus antigone U43621.1 Auk 113 (1996) In press Australia; Cambodia; China; India; Lao People’s Democratic Republic; Myanmar; Nepal; Pakistan; Vietnam
Grus rubicunda FJ769853.1 Auk 127 (2), 440-452 (2010) Australia; Indonesia; Papua New Guinea

Results

Each sequence of the final dataset was 771 base pairs in length. Of the 274 variable nucleotide sites, there were 179 parsimony informative sites. The nucleotide frequencies were 23.9%, 37.6%, 26.2%, and 12.3% for Thymine, Cytosine, Adenine and Guanine respectively. The transition: transversion ratio (R) was 17.00. The results of the overall mean distance (d statistic) showed a distance of 0.099. The evolutionary model that best fitted data under BIC was the TN93+G. Tamura-Nei was selected as the model of sequence that best fitted the dataset; with gamma distribution (G) was 0.27. The NJ tree and the ME tree gave same results and because the trees were exactly the same, only the NJ tree was presented in this study (Figure 1). The SBL recorded for ME tree was 0.84246412, and this was the single best tree obtained for this dataset.

The MP tree finds the tree that has the least amount of changes to explain the data that is being observed (Figure 2). There were two trees obtained for parsimony analysis and we present the best tree in this report which had a tree length of 505(Figure 2). The different homoplasy indices recorded for MP were CI was 0.621782, RI was 0.661348, and RCI was 0.411214 for all sites. The ML tree uses multiple models of evolution and allows statistical tests of evolutionary hypotheses to be conducted. The ML tree presented in this study had a log-likelihood of -3486.94 (Figure 3). The first clock test using Tajima’s relative test was not rejected and had a P value of 0.9055, which allowed us to impose a molecular clock, the UPGMA tree (Figure 4). The evolutionary rate was assumed at 0.02 (2% sequence divergence per million years), because of the species under study being a mammal. The tree split into different genera about 2.5 million years ago (Figure 4). The likelihood test for rate of heterogeneity had a P value of 0.6491, and thus allowed us to impose a molecular clock (Figure 5). Once again, an evolutionary rate of 0.02 was set.

Discussion

Grievink et al. (2010) suggest that tree constructions can become less accurate as the proportion of variable sites increases. In our study we had 274/771 variable sites. The percentage of variable sites was at 35.5%. Parsiomony informative site were greater than the number of taxa, and thus confirmed that our data was good for further analysis. The NJ tree 9Gigure 1) provides the topology and the; lengths of the branches of the final tree (Saitou & Nei 1987). Species that are neighbours reflecte close relation of their phylogeny. Where bootstrap levels are high for the branches show confidence level (Saitou & Nei 1987). Homoplasy exists as the CI values is less than 1, and increases as the CI value decreases 9lipscomb 1998). Stearns & Hoestra (2005) explained that homoplasy is caused by similarity as a result of similar events.

Penguins are considered monophyletic (Bertelli et al. 2005). In our experiement, our results also support the idea that penguins are monophyletic. The relationship within orders, based on Bertelli et al. (2005), suggest that penguins evolved from flying, tube-nosed seabirds (Procellariiformes). In addition, DNA comparisons link penguins and tube-nosed seabirds as sister groups (Bertelli et al. 2005). However, relationships amongst families still remain an unsolved mystery.

In other studies conducted with the same principles, it is suggested that the higher the number of replicates (bootsrap value) the more reliable the results. Perhaps in our case we could have taken a bootstrap of 10000 we would have inferred different results.

References

Baker, A. J., S. L. Pereira, O. P. Haddrath, and K.-A. Edge. 2006. Multiple gene evidence for expansion of extant penguins out of Antarctica due to global cooling. Proceedings of the Royal Society B: Biological Sciences 273(1582):11-17.

Bertelli, S., and N. P. Giannini. 2005. A phylogeny of extant penguins (Aves: Sphenisciformes) combining morphology and mitochondrial sequences. Cladistics 21: 209-239.

Bininda-Emonds, O.R.P., Gittleman, J.L. Purvis, A. 1999. Building large trees by combining phylogenetic information: A complete phylogeny of Mammalia. Biological Review 74: 143-175.

Castresana, J. 2001. Cytochrome b and the taxonomy of great apes and mammals. Molecular Biology and Evolution 18 (4): 465-471.

Felsenstein, J. 1985. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39: 783-791.

Giannini, N. P., and S. Bertelli. 2004. Phylogeny of extant penguins based on integumentary and breeding characters. The Auk 121: 422-434.O’Hara, R. J. 1989. An estimate of the phylogeny of the living penguins (Aves: Spheniscidae). Am. Zool. 29, 11A.

Jadwiszczak, P. 2009. Penguin past: The current state of knowledge. Polish Polar Research 30: 3-28.

Saitou, N & Nei, M. 1987. The neighbour-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4: 406-425.

Saundry, P., & Life, E. (2012). Penguins. Retrieved from Retrieved http://www.eoearth.org/view/article/155172

Stearns, S.C. & Hoekstra, R.F. 2005. Evolution: An introduction. Second Ed. Oxford University Press, New York.

Tamura, K. & Nei, M. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution 10: 512-526.

Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M., & Kumar, R, S. 2011. MEGA 5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Molecular Biology and Evolution 28: 2731-2739.

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