Saturday, May 23, 2020

Plant-pollinator interactions - Free Essay Example

Sample details Pages: 27 Words: 8060 Downloads: 9 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Abstract Very little work has been done on the evolution of floral colour diversity, outside of Europe and the Middle East. In particular, we know almost nothing about the evolution of the Australian flora in the context of hymenopteran visual systems. Such a study is likely to be important due to the geologically long isolation of the Australian flora and the high proportion of endemic plant species. Don’t waste time! Our writers will create an original "Plant-pollinator interactions" essay for you Create order The aims of this study were to investigate the colour of Australian native flowers in the context of hymenopteran visual systems, the innate colour preferences of Australian native bees (Trigona carbonaria), and the interactions between native bees and a food deceptive orchid (Caladenia carnea). Firstly, I found that the discrimination thresholds of hymenopterans match up with floral colour diversity and that hymenopterans appear to have been a major contributor to flower colour evolution in Australia. Secondly, I found that Trigona carbonaria has innate preferences for wavelengths of 422, 437 and 530 nm. Thirdly, I found that bees were able to habituate to orchid flowers based on colour, thus potentially explaining the colour polymorphism of Caladenia carnea. Together, my study suggests that the evolution of the Australian flora has been influenced by hymenopterans. 1. Introduction Plant-pollinator interactions The mutual interactions between pollinators and plants have been suspected in driving angiosperm radiation and diversification in the past (Regal 1977; Crepet 1984; McPeek 1996). The obvious mutual benefit is that pollinators depend on the pollen and/or nectar of flowering plants for food and, in return, partake in the incidental transfer of pollen necessary for plant reproduction (Faegri and van der Pijl 1978; Harder, Williams et al. 2001). Worldwide, it is estimated that more than 67% of angiosperm plants rely on pollination by insects (Tepedino 1979). Hence, pollinators play a critical role in the persistence and survival of flowering plants, which are of high value to the human food chain (Kearns and Inouye 1997; Klein, Vaissiere et al. 2007). Flower colour signals and sensory exploitation Colour is the result of the visible light being absorbed or reflected off objects and then processed by the eye and brain of an animal (Le Grand 1968). Light is part of the electromagnetic spectrum, and can be quantified by the wavelength of different photons of energy (Bueche 1986). The wavelengths reflected off the object are perceived by a visual system as the objects colour. For example, light that appears blue to a human observer can be described by a dominant wavelength of 400nm, whilst light that appears red is 700nm. Ultraviolet light falls between 300-400nm and can be seen by bees, but not humans. Flower colours have been influenced by the sensory receptors of insects, including their colour vision, which is different to human vision. Humans have a red, blue and green receptor (Chittka and Wells 2004). In contrast insects have a UV, green and blue receptor (Chittka and Wells 2004). As human vision is very different to a hymenopterans colour visual system, one cannot discuss a bees colour perception according to human colour terms such as red or blue. Therefore, this thesis will discuss colours according to wavelength. Colour is one of the most important floral signals plants use to communicate information to insect pollinators (Giurfa, Vorobyev et al. 1996; Dyer, Spaethe et al. 2008). Although it is known that pollinators select flowers based on morphology, nectar availability, size, and odour (Giurfa, Nez et al. 1994; Kunze and Gumbert 2001; Spaethe, Tautz et al. 2001; Whitney and Glover 2007), colour is known to play a critical role in enabling pollinators to detect and discriminate target flowers from a biologically important distance of up to 50 cm (Giurfa, Vorobyev et al. 1996; Dyer, Spaethe et al. 2008). Our understanding of the evolution of colour vision in insects has advanced considerably in recent years. In the past, studies of colour perception were limited due to little information on the colour visual system of insects (Frisch 1914; Daumer 1956). It is now possible to evaluate how flower visual signals appear to the visual system of hymenopteran pollinators, using spectrophotometer and colorimetry techniques, which allows quantitative evaluations of how complex colour information is perceived by insect pollinators (Chittka 1992) (fig. 1). Previous research has revealed that colour discrimination in hymenopterans is phylogenetically ancient, with different hymenopterans sharing similar colour perception (Helversen 1972; Chittka and Menzel 1992). Importantly, colour discrimination in the hymenoptera is known to predate the evolution of floral colour diversity (Chittka 1996). Here, recent research has revealed remarkable convergence in the evolution and distribution of floral colours in different parts of the world. Specifically, in a seminal paper, Chittka (1996) showed that flowering plants in both Europe and the Middle East have adapted their colour signals to the visual systems of bees, with flower colours in these regions closely matched to the visual receptors of hymenopterans (Chittka 1996). However, outside of Europe and the Middle East, very little work has been done on the evolution of floral colour diversity. In particular, we know almost nothing about the evolution of the Australian flora in the context of hy menopteran visual systems. This is an important question to investigate due to the long isolation of the Australian flora and the high proportion of endemic plant species. I hypothesise that the Australian floral coloration will closely match the discrimination thresholds of hymenopterans as recent evidence suggests that insect pollinators supported the early spread of flowering plants (Hu, Dilcher et al. 2008). Innate colour preferences of bees Charles Darwin was the first to state that innate preferences could allow an inexperienced pollinator to find a food source (Darwin 1877). Pollinators may use certain traits of flowers such as morphology, scent, temperature and colour to locate food (Heinrich 1979; Menzel 1985; Dyer, Whitney et al. 2006; Raine, Ings et al. 2006). Previous studies evaluating innate colour preferences have tended to focus on two species: the European honey bee (Apis mellifera) and bumblebee (Bombus terrestris). By contrast, no studies have looked at the innate colour preferences of Australian bees and how this affects their choices for flowers. We know that European bumblebees and honeybees show strong preferences for violet and blue (400-420nm) throughout their geographic range (Chittka, Ings et al. 2004) ,which interestingly correlates with the most profitable food sources (Lunau and Maier 1995; Chittka and Raine 2006). These preferences are likely to have had an impact on the relative success of dif ferent flower colours in regions where these bees are dominant pollinators (Chittka and Wells 2004). Consequently, information on the innate preferences of Australian bees will be important to understand hymenopteran plant interactions in the Australian context. Pollinator learning and food deceptive orchids Most plants reward their pollinators with nectar or pollen. However, some species do not offer floral rewards and, instead, employ a range of deceptive techniques to trick insects into performing the task of pollination. Deceptive pollination strategies are particularly well known and widespread among orchids (Jerskov, Johnson et al. 2006). For instance, approximately 400 orchid species are known to achieve pollination through sexual deceit, luring unsuspecting male insects to the flower through olfactory, visual and tactile mimicry of potential mates. More common are food deceptive orchids which are believed to number as many as 6,000 species (one-third of orchids) (Jerskov, Johnson et al. 2009). Food mimicking orchids employ bright colours to falsely advertise the presence of a reward to attract naive pollinators (Ackerman 1986; Nilsson 1992; Jerskov, Johnson et al. 2006). The common occurrence of food deception in orchids suggests that this form of pollination by deception is an e xtremely successful evolutionary strategy (Cozzolino and Widmer 2005). Visits by pollinators to deceptive plants are influenced by pollinator learning. In the case of sexual deception, previous research shows that insects quickly learn unrewarding flower decoys and avoid them. For example, male insects learn to avoid areas containing sexually deceptive orchids (Peakall 1990; Wong and Schiestl 2002). However, whether insects can learn to avoid food deceptive orchids remains to be investigated. In addition, high levels of variability in floral traits, particularly flower colour and floral scent, may interrupt the associative learning of insects by preventing their ability to become familiar with deceptive flowers (Schiestl 2005). Indeed, variation in colour, shape and fragrance is evident in non-model food-deceptive orchids (Moya and Ackerman 1993; Aragn and Ackerman 2004; Salzmann, Nardella et al. 2007). However, previous studies have only looked at pollinator preference for colour morphs (Koivisto, Vallius et al. 2002), rather than assessing if variable flower colour slows down the ability of naive pollinators to learn unrewarding flower decoys. Furthermore, there is a need to incorporate a combination of colour vision science and behavioural ecology to understand how a bee perceives the orchid flowers, as bees have a different visual system to humans. Although humans cannot see ultra-violet light, UV sensitivity is common in some animals (Tove 1995). UV sensitivity has been found in insects, birds, fish and reptiles (Marshall, Jones et al. 1996; Neumeyer and Kitschmann 1998; Cuthill, Partridge et al. 2000; Briscoe and Chittka 2001). Studies on UV vision in an ecological context have mainly focused on species specific signalling and mate choice (Bennett, Cuthill et al. 1996; Bennett, Cuthill et al. 1997; Pearn 2001; Cummings, Garc et al. 2006). However, few studies have looked at the role of UV signals in attracting bees to orchids. Previous studies have shown that the presence of UV reflecting crab spiders attracts honeybees to daisies (Heiling, Herberstein et al. 2003). In a similar study, Australian native bees (Austroplebia australis) were attracted but did not land on flowers with UV reflecting crab spiders (Heiling and Herberstein 2004). However, the role of UV signals in orchids is not well studied. In particular, it is not known if the UV signal is important in attracting naive bees to food deceptive orchids. Thus, it will be useful to know if UV signals might also serve to lure naive pollinators to deceptive flowers to understand deceptive pollination. Aims This project will investigate Australian flower colour diversity in the context of hymenopteran visual systems, the innate colour preferences of Australian native bees (Trigona carbonaria) and their interactions with a food deceptive orchid (Caladenia carnea). This study aims to address the following questions: 1. Is there a link between hymenopteran vision and Australian floral coloration? 2. Does an Australian native bee (Trigona carbonaria) have innate colour preferences? 3. Does a food deceptive orchid (Caladenia carnea) exploit the innate colour preferences of Trigona carbonar 2. Methods Part 1. Is there a link between hymenopteran vision and Australian floral coloration? Flower collection and spectral reflectance functions of Australian native plant flowers Australian native flowers were collected from Maranoa Gardens, Balwyn (melway ref 46 F7). Maranoa Gardens was chosen due to the diverse collection of species from all over Australia. Flowers were collected once a month, from May to January. A colour photograph was taken of the flower for identification. I also took a UV photograph for all flowers, using a digital UV camera [Fuji Finepix Pro S3 UVIR modified CCD for UV imaging] with calibrated UV-vis grey scales (Dyer, Muir et al. 2004). As UV rays are invisible to the human eye (Menzel and Blakers 1976; Dyer 2001), this photo enabled any UV reflectance areas of the flower to be measured by the spectrophotometer (Indsto, Weston et al. 2006). The spectral reflection functions of flowers were calculated from 300 to 700 nm using a spectrophotometer(S2000) with a PX-2 pulsed xenon light source attached to a PC running SpectraSuite software (Ocean Optics Inc., Dunedin, FL, USA). The spectrophotometer was used to quantify the colour of the flower as wavelength. The white standard was a freshly pressed pellet of dry BaSO4, used to calibrate the spectrophotometer. A minimum of three flowers from each plant were used for each spectral analysis. I evaluated a sample of 111 spectral measurements from Australian flowering plants, encompassing a representative variety of plant families (fig. 2). Correlations between spectral reflectance functions of different plant flowers and trichomatic vision of the honeybees To understand if there is a link between hymenopteran vision and Australian native flowers, I used the methodology used by Chittka and Menzel (1992). In that study, Chittka and Menzel looked for correlations between flower spectra sharp steps of different plant flowers and trichomatic vision of the honeybees. Sharp steps are a rapid change in the spectra wavelength (Chittka and Menzel 1992) (see fig. 3 for an example of a sharp step). These steps cross over different receptors, thereby producing vivid colours that stand out from the background. Furthermore, a colour signal will be more distinguishable to a pollinator if the sharp steps match up with the overlap of receptors in a visual system. Thus, the main feature of a flower wavelength is a sharp step. For this study, I defined a sharp step as a change of greater than 20 % reflectance in less than 50 nm of the bee visual spectrum. The midpoint of the slope was determined by eyesight as described by Chittka and Menzel (1992), as th e nature of curves varied with each flower. The absolute numbers of sharp steps within each flower spectra were counted. The frequencies are shown in fig. 4b. As hybrid plants are artificially selected by humans, hybrid flowers were not included in the analyses. Generating a Hexagon colour space To evaluate how flower colours are seen by bees, I plotted the flower colour positions in a colour hexagon space. A colour space is a numerical representation of an insects colour perception that is suitable for a wide range of hymenopteran species (Chittka 1992). In a colour space, the distances between locations of a two colour objects link with the insects capacity to differentiate those colours. To make the colour space, the spectral reflectance of the colour objects were required, as well as the receptor sensitivities of the insect. For Trigona carbonaria, the exact photoreceptors are currently unknown, but hymenopteran trichromatic vision is very similar between species as the colour photoreceptors are phylogenetically ancient (Chittka 1996). Thus, it is possible to model hymenopteran vision with a vitamin A1 visual template (Stavenga, Smits et al. 1993) as described by Dyer (1999). I then predicted how the brain processed these colour signals by using the average reflectance f rom each flower, and calculating the photoreceptor excitation (E) values, according to the UV, blue and green receptor sensitivities (Briscoe and Chittka 2001) using the methods explained by Chittka (1992). The UV, blue and green E-values of flower spectra were used as coordinates and plotted in a colour space (Chittka 1992). The colour difference as perceived by a bee was calculated by the Euclidean distance between two objects locations in the colour hexagon space (Chittka 1992). Modelling the distributions of Australian flower colours according to bees perception I analysed the most frequent flower colour according to a bees colour perception using the methods of Chittka, Shmida et al. (1994). I plotted the Australian flower colours in a colour space (Fig 5a). A colour space is a graphical representation of a bees colour perception. A radial grid of 10 degree sectors was placed over the distribution of colour loci and the number of floral colour loci within each sector was counted(fig. 5b). Part 2. Does an Australian native bee (Trigona carbonaria) have innate colour preferences? Insect model and housing Trigona carbonaria is an Australian native stingless bee that lives in colonies of 4000-10000 individuals (Heard 1988). In the wild, stingless bees live in hollows inside trees (Dollin, Dollin et al. 1997). Trigona carbonaria has a similar social structure to the honeybee (Wille 1983). They are common to North Eastern Australia and are a potentially important pollinator for several major commercial crops (Heard 1999). A research colony (ca. 4000 adults and 800 foraging individuals) of T. carbonaria was propagated for the experiments by Dr Tim Heard (CSIRO Entomology, 120 Meiers Rd, Indooroopilly 4068, Australia) as described in the paper by Heard (1988). Bees were maintained in laboratory conditions so that no previous contact with flowers had been made. For this study, a colony was placed in a pine nest box (27.5 x 20 x 31 cm; LWH) and connected to the foraging arena by a 16 cm plexiglass tube, containing individual shutters to control bee movements. All laboratory experiments were conducted in a Controlled Temperature Laboratory (CTL) at Monash University, Clayton, School of Biological Sciences (CTL room G12C dimensions 3 x 5m), during the months of July 2009- January 2010. Relative humidity (RH) was set to 30%, and the temperature was set to 27 C (SPER-Scientific Hygrometer, Arizona, USA), as this set up approximately matches conditions in Queensland for insect pollinators (Heard and Hendrikz 1993). Illumination (10/14 hr day/night) was provided by four Phillips Master TLS HE slimline 28W/865 UV+ daylight fluorescent tubes (Holland) with specially fitted high frequency (1200Hz) ATEC Jupiter EGF PMD2x14-35 electronic dimmable ballasts which closely matches daylight conditions for trichromatic hymenoptera (Dyer and Chittka 2004). The flight arena (1.2 x 0.6 x 0.5m; LWH) was made of a coated steel frame with laminated white wooden side panels. The arena floor was painted foliage green, and the arena lid was covered with UV transparent plexiglass. Experiments we re conducted from 1pm-3pm to control for time of day, as this is when bees are most active (Heard and Hendrikz 1993). Pre-training Bees were habituated to the flight arena for seven days. Naive foragers (i.e. bees that had never encountered real or artificial flowers) were initially pre-trained to forage in the flight arena on three rewarding aluminium sanded disks (25 mm in diameter), with a 10-l droplet of 15% (w/w) sucrose solution placed in the centre. The disks were placed on vertical plastic cylinders (diameter = 25 mm, height = 100 mm), to raise them above the floor of the flight arena so that bees learnt to fly to the disks. Pre-training allows bees to become habituated to visiting artificial flowers for further experiments. The aluminium sanded disks were chosen as neutral stimuli because they have an even spectral reflectance curve in the spectral visual range of the bees, fig. 6. The sucrose solution reward on these training disks was refilled using a pipette after it was consumed by foraging bees. The spatial positions of these training disks were pseudo randomised, so that bees would not learn to as sociate particular locations with reward. Bees were allowed a minimum of two hours to forage on the pre-training disks before data collection Innate colour preference testing To test the innate colour preferences of naive bees, I performed simultaneous choice experiments with flower-naive bees using artificial flowers that simulated the floral colours of natural flowers. The aluminum rewarding disks were replaced by the ten unrewarding, coloured artificial disks in the original flight arena. Artificial flower stimuli were cut in a circle (70 mm diameter) from standardized colour papers of the HKS-N-series (Hostmann-Steinberg K+E Druckfarben, H. Schmincke Co., Germany). In each experiment the same set of ten test colours (1N pale yellow, 3N saturated yellow, 21N light pink, 32N pink, 33N purple, 50N blue, 68N green, 82N brown, 92N grey, back of 92N white) were used. These colours were chosen as they have been used in innate colour experiments with other hymenopterans (Giurfa, Nez et al. 1995; Kelber 1997; Gumbert 2000), and the colours are also widely used in other bee colour experiments (Giurfa, Vorobyev et al. 1996). The coloured paper disks w ere placed on vertical plastic cylinders (diameter = 15 mm; height = 50 mm), to raise them above the floor of the flight arena. The gate was shut in the arena to ensure the bees used in each trial were separated from the next trial. The number of landings and approaches to the stimuli were recorded for one hour. Approximately 200 bees were used for each trial. The spatial positions of the artificial flowers were pseudo randomised in a counter balance fashion every 15 minutes. After each trial, the colour disks were aired and wiped with a paper tissue to remove possible scent marks, which are known to affect experiments with honeybees (Schmitt and Bertsch 1990; Giurfa and Nez 1992). I conducted each subsequent trial after removing the used bees from the system, to ensure that the bees in the next trial were replaced with naive foragers. It is known that perception of colour can be influenced by background colour (Lunau, Wacht et al. 1996). Therefore, I also tested colour choices on other background colours of grey and black. The results are qualitatively similar (fig. 8b), so only data from the biologically relevant green background was used for subsequent analysis. Analysis of colour stimuli As bees see colours differently to humans, I quantified stimuli according to five parameters: wavelength, brightness, purity (saturation), chromatic contrast to the background and green receptor contrast. Dominant wavelength was calculated by tracing a line from the centre of the colour hexagon through the stimulus location to the corresponding spectrum locus wavelength (Wyszecki and Stiles 1982). Brightness was measured as the sum of excitation values of the UV, blue and green receptors (Spaethe, Tautz et al. 2001). Spectral purity of the stimulus was calculated by the percentage distance of the stimulus in relation to the end of the spectrum locus (Chittka and Wells 2004). Chromatic contrast was calculated as the distance of a colour stimulus from the centre of the colour hexagon relative to the background. Chromatic contrast is important as perception can be affected by background colour (Lunau, Wacht et al. 1996). Green receptor contrast was measured as the green receptor excitat ion from a stimulus relative to the background (Giurfa, Nez et al. 1995). This contrast is relevant as green receptors and green contrast are known to affect motion in bees (Srinivasan, Lehrer et al. 1987). Statistical analyses The impact of wavelength on number of landings by Trigona carbonaria was investigated using a single factor analysis of variance (ANOVA) and a post hoc Tukeys HSD test (=0.05) (Quinn and Keough 2002) using the number of landings as the dependent variable and wavelength of stimuli as the independent variable. Brightness, purity (saturation), chromatic contrast to the background and green receptor contrast of stimuli were analysed using the Spearmans rank correlation test against choices. Statistical analyses were conducted using R statistical and graphical environment (R Development Core Team, 2007). Statistical significance was set to P0.05. Part 3. Does a food deceptive orchid (Caladenia carnea) exploit the innate colour preferences of Trigona carbonaria? Plant model Caladenia carnea is a widespread species, common to eastern Australia. The orchid is highly variable in colour, ranging from pink to white. It is pollinated by Australian native bees of the Trigona species (Adams and Lawson 1993).With bright colours and fragrance, this orchid achieves pollination by food mimicry (Adams and Lawson 1993). Thus, due to the colour variation of the orchid, C. carnea is an excellent model with which to examine floral exploitation of potential pollinators. Caladenia carnea flowers were supplied by private growers from the Australasian Native Orchid Society. Can Trigona carbonaria perceive a difference between pink and white flowers of Caladenia carnea? Colorimetric analysis of the pink and white Caladenia carnea flowers were used to investigate whether different colours of the orchid would be perceived as similar or different to a bees visual system. A spectrophotometer was used to take four measurements of each flower colour (pink versus white). The actual measurements used in the analysis were an average of each colour (Dyer, Whitney et al. 2007). To predict the probability with which insect pollinators would discriminate between different flowers, these spectra were plotted as loci in a hexagon colour space (Chittka 1992) (see hexagon colour space methods). Choice experiments I conducted trials testing the preferences of bees when offered a dichotomous choice between a white versus pink Caladenia carnea flower. Each trial took place inside a flight arena. Each white and pink flower used in a trial were matched for size, placed into indiviual plastic containers (diameter= 5 cm, height=5 cm) and placed in the arena with a distance of 10 cm between flower centres. Each container was covered with Glad WrapTM (The Clorox Company, Oaklands, CA, USA) to remove olfactory cues as they are known to inuence the choice behaviour of honeybees (e.g. Pelz, Gerber et al. 1997; Laska, Galizia et al. 1999). Approximately 50 bees were let into the arena for each trial. The rst contact made by a bee with the Glad WrapTM within a distance of 4 cm, was recorded as a choice of that ower (Dyer, Whitney et al. 2007). The number of landings were recorded to the flowers for five minutes. After each trial, the Glad WrapTM was changed to prevent scent marks. In addition, individual f lowers and spatial positions were randomised. Individual bees were sacrificed after each trial to avoid pseudo replication. Does the UV signal affect the attraction of bees to orchid flowers? To investigate whether the UV reectance of the dorsal sepal affected the response of bees, I offered bees the choice between two white orchids, one with a UV signal and the other without (N=16). The UV signal was removed by applying a thin layer of sunscreen (Hamilton SPF 30+, Adelaide, SA, Australia) over the dorsal sepal. Spectral reflectance measurements were taken to ensure that the sunscreen prevented any reflection of UV light (below 395 nm) from the sepals and did not change the reflectance properties of the orchid. In addition, spectral measurements of orchid sepals under Glad WrapTM confirmed that the foil was permeable to all wavelengths of light above 300 nm and did not obscure the reflectance of flowers. Do bees display preferences when choosing between pink versus white orchid flowers? To assess whether bees show a preference for pink or white variants of the orchid Caladenia carnea, I offered bees a simultaneous choice between a pink or white flower (N=16). See procedures for choice testing. Do bees habituate to non-rewarding orchids based on differences in floral coloration? I conducted a two stage experiment to investigate if bees could learn to habituate to a non-rewarding flower colour over time and whether bees adjusted their subsequent flower choice depending on the flower colour encountered previously. At stage 1 of the experiment, native bees were presented with one flower, either white or pink. Flowers were placed in a container with Glad WrapTM. Landings to the flower were recorded at the start and again at the 30 min mark. At stage 2, the flower from stage 1 was swapped with a new flower colour and the number of landings were scored for 5 minutes. Flowers were randomised and Glad WrapTM changed to prevent scent marks after each trial. Once again, bees were used only once per experiment. Statistical analyses For experiments 2, 3 4, numbers of landings by naive bees to flower pairs were compared using two tailed paired t-tests. A two factor ANOVA was used to analyse whether bees habituate to non-rewarding orchids based on differences in floral coloration. The dependent variable was the number of landings and the two independent variables were previous flower colour and new flower colour. 3. Results Part 1. Is there a link between hymenopteran vision and Australian floral coloration? Correlations between the inflection curves of different plant flowers and trichomatic vision of hymenopterans The analysis of 111 spectral reflection curves of Australian flowers reveals that sharp steps occur at those wavelengths where hymenoterans are most sensitive to spectral differences (fig. 4b). There are three clear peaks in sharp steps (fig. 4b). It is known that hymenopteran trichomats are all sensitive to spectral differences at approximately 400 and 500 nm (Menzel and Backhaus 1991; Peitsch, Fietz et al. 1992). Hence, the peaks at 400 and 500 nm can be discriminated well by hymenopteran trichomats, as illustrated by the inverse / function (solid curve shown in fig. 4a) of the honeybee (Helversen 1972), which is an empirically determined threshold function which shows the region of the electromagnetic function that a bees visual system discriminates colours best. In summary, the spectral position of receptors of trichomatic hymenopterans are correlates with steps in the floral spectra of Australian flowers. The distributions of Australian flower colours according to bees perception The floral colour loci are strongly clustered in the colour hexagon (fig. 5a). Blue-green flowers are the most common in the perception of bees, while pure UV flowers were the rarest (fig. 5b). Part 2. Does an Australian native bee (Trigona carbonaria) have innate colour preferences? Effect of brightness, spectral purity, chromatic contrast and green receptor contrast on colour choices There was no significant effect of stimulus brightness on choice frequency (rs= 0.333, n=10, p= 0.347; fig. 7a). There was no significant effect of spectral purity on choice frequency (rs = 0.224, n=10, p= 0.533; figure 7b). There was no significant correlation effect of chromatic contrast on choice frequency (rs = 0.042, n=10, p= 0.907; figure 7c). There was no significant effect of green receptor contrast on choice frequency (rs = 0. 0.552, n=10, p= 0.098; figure 7d). Effect of wavelength on colour choices Stimuli colours are plotted in figure 8a, as they appear to a human viewer to enable readers to understand the correlation between colour choices. However, all statistical analyses were conducted with stimuli plotted as wavelength due to the different visual perception of bees and humans (Kevan, Chittka et al. 2001). There is a significant effect of wavelength on the number of landings by Trigona carbonaria (Single factor ANOVA, F9,110 = 5.60, P 0.001), figure 8a. Tukeys post hoc test revealed that the wavelength of 437 nm (a white colour to a human viewer, but strongly coloured to a bees visual system as this stimulus does not reflect UV radiation) had significantly higher landings than other wavelengths of 528 nm (brown) (P0.01), 432 nm (grey) (P 0.01), 431 nm (light pink) (P0.01), 420 nm (purple) (P0.01), 455 nm (blue) (P=0.0196) and 535 nm (green) (P=0.0266). In addition, the number of landings to wavelengths of 530 nm (pale yellow) (P=0.0321) and 422 nm (pink) (P=0.0318) disks w ere significantly higher than that of 432 nm (grey) (figure 8a). Part 3. Does a food deceptive orchid (Caladenia carnea) exploit the innate colour preferences of Trigona carbonaria? Can Trigona carbonaria perceive a difference between pink and white flowers of Caladenia carnea? Ultraviolet photographs and reflectance measurements revealed that lateral sepals were different from the dorsal sepals (fig. 9). The spectra of the pink and white lateral sepals indicated no UV reflection. In contrast, the spectra of the dorsal sepals show reflection in the UV region (320-400 nm) (fig. 9b). Figure 10 shows the loci of the respective flower spectra in a hexagon colour space. Dyer and Chittka (2004) showed that with increasing colour distance between flowers and distractor flowers, less errors were made by foraging bees (fig. 11). Colour distance between the white and pink flowers is measured in hexagon units (Euclidean colour metric); Table 1. The lateral sepals (UV-) of pink and white flowers are separated by only 0.082 colour hexagon units, while pink and white dorsal sepals (UV+) are separated by 0.039 hexagon units. Thus, pink and white lateral sepals are distinguishable to a bee. In contrast, pink and white dorsal sepals (UV+) are perceptually similar to a bee. Therefore, the white vs. pink flowers of Caladenia carnea can thus be discriminated with between 70-90% accuracy (fig. 11). This means that visits to white/pink flower colours may results in occasional pollinator perceptual errors (1-3 errors/10 visits). Does the UV signal affect the attraction of bees to orchid flowers? When bees were presented with a choice between two white orchid flowers, one with a UV signal and one without, there was a significant preference for the flower with the UV reflectance (paired t-test: t= 6.949, df= 15, p0.001, n=16; figure 12). Do bees display preferences when choosing between pink versus white orchid flowers? When test subjects were presented with a choice between two flower colours, pink and white, there was a significant preference for the white flower (paired t-test: t= -3.484, df= 15, p= 0.003, n=16; figure 13). Do bees habituate to non-rewarding orchids based on differences in floral coloration? Bees were found to habituate to non-rewarding flowers, as the mean number of landings by Trigona carbonaria to the flower at the first time stage (T1) were found to be significantly different from the second time stage (T2) for white (paired t-test: t= 8.34, df= 15, p0.001) and pink flowers (paired t-test: t= 8.11, df= 15, p0.001) (fig. 14). Habituation rates were found to differ with different flower colours, as the mean number of landings by Trigona carbonaria to the white flower were found to be significantly higher from that of the pink flower (paired t-test: t=3.59, df=15, p=0.003, figure 14). I also looked at delta, which is calculated as the rate of change between landings at the first and second time stage for pink and white flowers separately. Hence, bees were found to habituate faster to pink flowers, as the rate of change was found to be significantly different (paired t-test: t=3.94, df=15, p=0.001). The number of landings to a flower were found to be significantly affect ed by the interaction between the previous flower colour and new flower colour, (two factor ANOVA, F3,28=6.846, p=0.001, figure 15). When the second flower colour presented was the same colour as the previous flower, landings were not significantly different to the second flower (F1,14=4.332 p=0.056). In contrast, when the second flower colour was different to the previous colour, landings were found to be significantly different to the second flower (F1,14=9.168 p=0.009) (fig. 15). In addition, preferences depended on the colour that bees were exposed to previously. When the previous flower was white, landings to the second pink or white flower were not found to be significantly different (F1,14=5.332,p=0.230). In contrast, when the previous flower colour was pink, landings were found to be significantly higher to the second white flower than to new pink flower (F1,14=8.395, p=0.012, figure 15). Bees, in this regard, were adjusting their choices to the second flower depending on their previous flower experience. 4. Discussion Hymenopteran vision and Australian floral coloration. Part 1 of this project aimed to investigate a possible link between hymenopteran vision and Australian floral coloration floral colour diversity My results suggest that the discrimination thresholds of hymenopterans match up with the Australian floral colours. These results are consistent with the study of Chittka and Menzel (1992), who found a correlation between flower spectra of different flowers and trichomatic vision of hymenopterans for flowers collected in Europe and parts of the Middle East. I have found a similar pattern in Australia, so this data is highly suggestive that hymenopterans appear to have been a major contributor to flower evolution in Australia. As bee vision predates the evolution of flower colours (Chittka 1996), one possibility is that Australian native flowers may initially have evolved to exploit the vision of hymenopteran species. Another alternative is that the existence of the current floral colours is due to phylogenetic constraints on the pigments in flower colours (Menzel and Shmida 1993). The distribution of flower colours that has evolved has a remarkably similar distribution to other parts of the world, such as Europe and the Middle East, where honeybees are the dominant pollinators (fig. 4a b, 5b c). Blue-green flowers were the most common as flower colours appear to a bee, while pure UV flowers were the rarest in the flowers sampled (fig. 5b). This result is similar to previous studies that found a similar cluster of blue-green flowers in Europe and Middle East (Chittka, Shmida et al. 1994). In that study, it was suggested that this cluster may be explained by the innate colour preferences of insects for certain colours (Chittka, Shmida et al. 1994). However, other studies contradict this because naive and experienced honeybees prefer UV-blue and blue colours over blue-green colours (Menzel 1967; Giurfa, Nez et al. 1995). However, the distribution of blue-green flowers is larger than that of UV-blue and blue flowers. The refore, Chittka (1997) suggested that the distribution could be caused by evolutionary constraints on the pigments of flower colours. Another theory for why flower colours are not evenly distributed in the colour space could be due to colour constancy (where bees only visit one flower type) in complex environments (Dyer 1999; Dyer and Chittka 2004). Hence, as there is no equal spacing of colours in the Australian floral coloration and there is a higher proportion of blue-green flowers, this may correspond to either pigment constraints in flowers or selective pressures by important pollinators like hymenopterans. There are two likely scenarios as to whether floral colours in Australia have evolved independently to those of Europe and the Middle East. First, angiosperms evolved after Australia separated from Gondwana. Hence, parallel evolution may have occurred where similar flower colours were being selected by hymenopteran trichomatic vision. The second possible scenario is that ang iosperms evolved before Australia separated from Gondwana and radiated out to all continents. Thus, flowering plants drifted with the moving land masses and evolved in a similar way to European and Middle Eastern flowers. Scenario 1, in this regard, seems more likely as the evolution of flowers in Australia is likely to be independent, based on work by Kevan and Backhaus (1998) who estimate that early angiosperms were most likely to be a pale yellow pollen colour and later evolved highly coloured signals to lure important pollinator vectors. It is estimated that the earliest angiosperm fossil dates at 132 million years ago (mya), around the early Cretaceous (Crane, Donoghue et al. 1989; Crane, Friis et al. 1995). Towards the end of the Cretaceous, Australia separated from Gondwana (Rich and Rich 1993). However, the time scales are too imprecise to conclusively resolve this question. Additional data is needed on biogeographical relationships and how this relates to floral reflectance data for other continents such as Africa, South America, Asia and North America to understand this question. The foraging success of a bee is dependent on the colour vision receptors being able to relialy distinguish flower species from each other (Chittka and Menzel 1992). There is a mutual benefit here as the pollinators foraging efficiency is increased if it can distinguish flowers from the surrounding background; and the plant is more likely to be pollinated if it appears distinct from its surroundings (Chittka and Menzel 1992). It is known that bees can discriminate colour stimuli best at 400 and 500 nm (Helversen 1972). So why, then, do we see a third peak at 600 nm (fig. 4b)? One reason could be that biological material (including leaves) reflect infrared radiation above 600 nm (Chittka, Shmida et al. 1994). There is also the possibility that insects with red receptors such as butterflies and beetles (Menzel and Backhaus 1991; Peitsch, Fietz et al. 1992) might also be important pollination vectors influencing the evolution of some Australian flower colours. Currently, there is very l ittle information within Australia about the vision of insects with long wavelength sensitive receptors, but this would provide an interesting avenue for future research. It was really important to not bias my data set by specifically picking species that are pollinated by only hymenopterans. Thus, I took a broad approach of including every flowering plant species available at my sampling site to best represent the colour distribution of Australian flowering plants that have evolved. This enabled me to test whether hymenopteran colour vision has been a major driving force shaping the evolution of floral colours. In spite of the fact that the dataset included a broad sample of plants (some of which would likely not even be pollinated by hymenopterans), strong patterns were detected, suggesting that hymenopterans may have been major players shaping the evolution of floral colours. Innate colour preferences of an Australian native bee In part 2 of the study, the simultaneous choices of naive bees (Trigona carbonaria) were tested for 10 different colours using articial owers. After each test, bees were sacrificed so all the data was independent, avoiding the risk of pseudo replication. In addition, the bees were not exposed to real flowers and reared on colour neutral disks prior to colour testing (fig. 6). Thus, their behaviour can be classified as innate (Giurfa, Nez et al. 1995). It was necessary to pre-train bees to land on aluminium disks because it was not possible to get bees to land on colour stimuli without previous training (Giurfa, Nez et al. 1995). I also tested whether bees preferred stimuli on the basis of brightness, spectral purity, contrast and green receptor contrast. My results showed that bees preferred stimuli irrespective of brightness, spectral purity, contrast and green receptor contrast (fig. 7). This was found to be consistent with the study by (Giurfa, Nez et al. 1995). Thus, the only sig nificant factor affecting bees choices was wavelength. The results revealed that Trigona carbonaria has innate preferences for wavelengths of 422, 437 and 530 nm (fig. 8b). These results are remarkably similar to the innate preferences of flower naive honeybees and bumblebees in Europe (Menzel 1967; Lunau 1990; Giurfa, Nez et al. 1995) that have innate preference for blue and violet. In those studies, it was suggested that the innate preference for blue correlates with blue and violet flowers having a slightly higher nectar reward than other flower colours in Europe (Giurfa, Nez et al. 1995; Chittka, Ings et al. 2004). In the same way, I hypothesise that these the innate preferences of Trigona carbonaria might correspond to Australian flowers colours that are more profitable to bees. Thus, future studies may want to look for correlations between the amounts of nectar in Australian native flowers versus different colour categories to see if nectar content may have fine-tuned the colour preferences of Australian stingless bees. Interactions between Australian stingless bees and a food deceptive orchid In part 3, the results illustrated that bees preferred flowers with a UV signal than those without (fig. 12). The results are in agreement with the study by Peter and Johnson (2008) who removed the UV component of the flower by using sunscreen, which reduced the number of pollinator visits. In a similar way, the UV signal of C. carnea is likely to be important in attracting naive bees to the flower. The UV-signal aside, I found that bees also significantly preferred the white flower colour over the pink flower colour (fig. 13). This result is consistent with part 2 of my study where I found that Trigona carbonaria showed innate preferences for certain colours over others. This could potentially result in fitness differences for the orchid depending on the colour of its flower. Here, it is possible that negative frequency-dependent selection may be important, with pollinators visiting the rarer morph and, in so doing, help retain floral colour variation (Smithson and Macnair 1997). Fo r example, negative frequency-dependent selection was found to influence flower colour variation in Dactylorhiza sambucina (Gigord, Macnair et al. 2001), where the rarer morph was visited more often. In a similar way, it is possible that negative frequency-dependent selection might be occurring in my system, but more information would be needed on the frequency of the two colours under natural field conditions. My results also reveal that bees were able to habituate to flowers on the basis of colour (fig. 14). This result in similar to the study by Simonds and Plowright (2004), who found that bumblebees habituated to colour paper disks and patterns, with a reduction in the number of landings over time. In that particular study, it was suggested that fatigue may have been responsible for bees habituating to colour disks. Another possibility is that bees were learning to habituate to the presence of unrewarding flower decoys through associative learning. Such a possibility is consistent with work carried out on the response of wasps that are exploited as pollinators by sexually deceptive orchids (Wong and Schiestl 2002; Wong, Salzmann et al. 2004). In those studies, it was found that males quickly learn the presence of unrewarding flowers and avoided flowers and locations where they had previously been deceived. Intriguingly, I found an increase in the number of landings to a newly introduced flower if it was a colour that the bee innately preferred, thus countering the habituation effect towards unrewarding orchids. It seems reasonable, therefore, that the existence of multiple flower colours in C. carnea could have fitness consequences for the orchid by making it more difficult for their pollinators to associate a particular colour with non-rewarding flowers. In nature, the number of visits a reward less orchid receives by naive pollinators also depends on ecological factors such as flowering time along with availability of other rewarding plants. Further studies might therefore like to take such factors into account. It is also important to point out that this study only examined visual cues. In nature, pollinators may obtain and assess information about their environment from a variety of visual and olfactory cues (Kunze and Gumbert 2001). The question of which cue has the greater influe nce on pollinator decisions warrants further investigation, and provides interesting avenues for future research with food-deceptive orchids. It is possible that group learning behaviour may have occurred in the habituation experiments. For example, previous studies have shown that insects can learn through transfer of social information (Worden and Papaj 2005; Leadbeater and Chittka 2007). It has been shown that bumblebee workers, for instance, can learn by observing others (Worden and Papaj 2005). However, it was not possible to control for group learning behaviour, as bees tested in isolation did not respond at all in pilot studies. To try and minimise the effects of group learning, however, bees were used only once and were removed after each trial so that each replicate was independent. Furthermore, although I controlled for floral scent in my study by using glad wrap, it was not possible to control for floral shape. Bees, in this regard, can also have preferences based on shape (Dafni, Lehrer et al. 1997; Kunze and Gumbert 2001; Galizia, Kunze et al. 2005). The orchid flower sepals in my experiments varied subtlety in shape (e.g. width). However, to control for this, flowers were completely randomised with respect to shape. In addition, evidence suggest that subtle differences in shape may not actually be perceived by bees due to their low acuity spatial vision (Land 1997; Land 1999). However, it would be interesting to test for shape preferences in the future. Conclusion and Future Directions In part 1, I found that the discrimination thresholds of hymenopterans match up with the with Australian floral coloration and that bees appear to have been a major contributor to flower evolution in Australia. In part 2, I found that Trigona carbonaria has innate preferences for wavelengths of 422, 437 and 530 nm, which might correspond to Australian flowers colours that are more profitable to bees. In part 3, I found that bees were able to habituate orchids based on colours (consistent with the data obtained in part 2). However, evidence also suggest that variation in flower colour could be an important strategy by C. carnea orchids to counter the bees capacity to learn and avoid unrewarding flower decoys. This study has highlighted a number of areas in which future research can advance our understanding of the exploitation of bee colour vision by flowers. Most work, to date, has focused on bees and flowers from Europe and it is surprising that very few studies have looked at the i nteraction between Australia bees and flowers. My study underscores the importance of further work in the Australian context for what it might reveal about general ecological, biogeographic and evolutionary patterns of plant-pollinator relationships. Acknowledgements My supervisors, Adrian Dyer and Bob Wong. Adrian thank you for the endless support, patience, sharing your knowledge about the exciting field of colour vision science and expanding my thinking beyond intellectual boundaries. Bob thank you for your exceptional levels of guidance, feedback, time and encouragement. You both inspired me to explore this topic with great enthusiasm. The culmination of two experts in their field with the right skills enabled me to do so much in this year. I hope that you both team up to supervise many honours students as this project has opened so many doors to explore. Vera Simonov, for being my field and research assistant and talking to me about things other than research. Andreas Svensson, for help with the stats. Melanie Norgate, for all the advice and encouragement. The behavioural ecology lab group, all the ideas and suggestions were invaluable. James, Ken, Lenny, Nikki, Wendy and Marianne for reading my various thesis drafts. Mani Shrestha and Dick Thomson, for putting me touch with the orchid society. The Caladenia carnea flowers were kindly supplied by Richard Austin and Russell Mawson of the Australasian Native Orchid Society (Victorian Group). Tim Heard and the CSIRO, for supplying the bees. Paul Birch and Andrea Dennis, the gardeners at Maranoa gardens, for letting me take flower samples and providing verbal information about the flora of Maranoa gardens. My fellow honours students for the sharing of ideas, stress and all the laughs, especially Emma Jensson and Kat Rajchl. I hope we stay great friends. Alanna, Kirsten, Mez, Wendy and Vera, thank you for the motivation, support and organising outings to take my mind away from research. And finally to Mum, Dad, Lenny, Harry and Lucy- for taking care of me and understanding that honours is really a hermit year, but its been a great one! Once again, thank you to you all for making the year a great success!

Tuesday, May 12, 2020

Becoming A Social Worker, Working With The Veteran Population

Everybody does not know at an early age, what they would like to be in life. Some of us need time to discover our gift, passion or destiny. I’m one of them. After two careers and struggling the idea of want to do my remaining time on earth, the decision was made to start a third career, that had substance and geared toward building a better place to rear my children. I want to become a social worker, working with the veteran population. With this career I would be able to reach back as did many of my mentors. Become a resource. Utilizing my resources is a tool I used as a road map to success. The title of Social Worker is a legal classification reserved for those who have received specialized training through an authorized university and have completed the requirements for Bachelors, Masters or Doctoral degree and are registered with a professional regulatory body. The foundation of Social work is rooted in its 5 core values of service: social justice, dignity and worth of the person, importance of human relationships, integrity, and competence. The area of social work I would like to pursuit is Adult and Healthy aging with a specialization in military veterans, this population is important to me because I stand in the same boots and sleep in the same conditions. The sense of strong wills I developed as a child is the skill used to crawl through the battlefield of life, to get to the point where I could ask for help. Getting old is a difficult to grasp, I struggled with aShow MoreRelatedThe Social Worker s Profession Essay1591 Words   |  7 Pagesbasic purpose of the social worker’s profession is to help individuals, families, and communities to enhance their individual and collective well-being. 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Wednesday, May 6, 2020

Road use charges should be introduced in the UK both for motorways and for urban areas Free Essays

string(80) " on the fact that roads have an elasticity that is either inelastic or elastic\." Whenever we hear today about the problems facing this country and how the government intends to solve them, arguably the three most commonly occurring points for debate on the government’s agenda are education, the health system and transport. Transport often comes into focus when high profile incidences involving sub-standard public transport (most notably in recent times involving the railway network) are brought to light. And all this does is reinforce the reasons why many of us choose to use a car to travel in, rather than public transport. We will write a custom essay sample on Road use charges should be introduced in the UK both for motorways and for urban areas or any similar topic only for you Order Now Due to the increasing need to use cars, our roads are becoming more and more congested. Now the government, in its position of trying to improve the situation, has to find a solution, which will at least ease the problem. Congestion arises when the volume of traffic exceeds road capacity. This reduces the speed of all vehicles and so increases the average time it takes to complete a particular journey. The congestion mainly occurs at peak times where the demand for the road is at its highest. Particularly when queuing in traffic jams, more people are using the roads, which increases the (marginal) cost of time to other people. Congestion occurs due to the fact that roads are a â€Å"nonexcludable† public good, i.e. no one is excluded from using the roads (based on the assumption that everyone can afford a vehicle, can drive a vehicle, and can afford the additional costs to run a vehicle.) By its definition, when a nonexcludable public good is provided, it affects the welfare of every person in the society. A public good is one where another individual using it will have no effect on the benefits received by others using it (i.e. the marginal cost of someone else consuming it is zero) and theref ore there is no competition for the service). Figure 1;The speed flow curve (Inverse relationship between the number of vehicles on a road and the average speed of vehicles) There are many causes of congestion, which all lead to some economic costs and therefore affect businesses and users of the roads. For a business the consequences of congestion are most likely to be incurred when transporting the goods and raw materials to and from factories and retail outlets. Congestion increases firms’ costs, resulting in a lower comparable profit (to the value of profit without congestion) for the firm. Providing the price is inelastic these extra costs could be passed on to consumers in the form of higher market prices (whereby demand for the good is not too sensitive to a change in price). These extra costs can be incurred by either the opportunity cost of time (delays) or direct costs of extra fuel being burned travelling at lower speed. Figure 2: Supply and Demand during congestion for a price elastic good (Congestion costs shift the supply curve to the left, resulting in higher selling price and therefore lower quantity demanded) Supply curve with congestion charges Supply curve without congestion charges Demand curve Congestion may cause delays in delivery, which in turn may lead to various negative impacts on the business itself. For instance this may adversely effect the reputation of the business. Furthermore suppliers to the business may be delayed causing the firm to be less productive incurring unnecessary costs from staff becoming idle. These consequences are an adverse affect on the welfare of the other motorists, (i.e. people most likely become more irritated). Since there is a direct effect here of the actions of one person on the welfare of another person or persons in a way that is not transmitted by market prices, we have the definition of an effect that is called an externality. In economics, public goods and externalities are closely related and are often associated with efficiency problems. Thus this is part of the reason why our roads are â€Å"inefficient†. Congestion also causes the road network to become an impure public good. A public good is defined as one where another individual using it will have no effect on the benefits received by others using it (i.e. the marginal cost of someone else consuming it is zero) and therefore there is no competition for the service. An impure good is one where the consumption of the commodity is to some extent rival. When congestion occurs the use of the road network becomes competitive (especially during the rush hour) and therefore the good becomes impure. This allows the public good to be given a price and so allows schemes such as congestion charging and toll roads to be introduced. Governments across the world are introducing road charges with the goal of reducing road use and minimising the negative effects of road usage. An important decision that the government will take into consideration will be whether introducing road charges would have a negative impact upon the environment. In the capital the Mayor of London, Ken Livingston, is currently asking for the views of 300 groups likely to be affected by road charges to enter London. Despite a London report suggesting a 12% reduction in traffic, opposing parties believe it would cause chaos and adversely effect families and small businesses. Transport is a derived demand as it comes from the needs of the people (whether they are travelling to work, going shopping or meeting socially) and needs of businesses (transportation of goods and industrial materials). However the increasing demand for road use over the years has added to the number of vehicles on the road, and in turn, increases the damaging effects upon the environment. The obvious aim of introducing road charges for using motorways or entering urban areas in private vehicles are that fewer people will choose to do so, thus decreasing the harmful gas engine emissions as a result. Noise and sight pollution, along with air pollution, should also be seen as a result of reduced traffic and congestion. This would rely on the fact that roads have an elasticity that is either inelastic or elastic. You read "Road use charges should be introduced in the UK both for motorways and for urban areas" in category "Papers" The greater the elasticity (value) of these road charges would indicate higher price sensitivity in using the road in question. Establishing the elasticity will enable us to identify the extent to which the environment will benefit from a reduction in pollution (air, gas and noise). From a report by Button (1993) the ‘trip type’ significantly affects the price elasticity. For instance travelling to urban areas for shopping purposes has a high elasticity, so if road charges become applicable it is likely that fewer people will decide to make the journey using private vehicles. From an environmental perspective fewer cars entering these urban areas will reduce congestion and vehicle pollution levels. When looking into the decrease of CO emissions for particular areas, such as Hereford as shown above, this illustrates the dramatic reduction in air pollution levels when a charge is applied. This does however depend on price elasticity of demand for the road. This is in comparison to the minimum elasticity for urban commuting (travelling to work) which is much lower. Any road charges imposed on these people will have a minimal effect, as it is more essential for them to travel into the urban area. An additional advantage of road charges is the extra revenue that would be generated through payment of those people who continue to use the particular (charged) road. This has seen positive results in Norway where the funds collected are being used to support the successful public transport areas of Oslo and Trondheim. The biggest environmental concern that road charges are associated with, specifically with motorways, is the likelihood that car drivers would take alternate routes (not charged) such as country roads. These roads that were previously quiet would then be subjected to noise and gas emissions as well as congestion problems. This is an example of the substitution effect as both the main roads and the rural roads could satisfy the needs of the public. The introduction of payment for using the main roads would result in an increase in the quantity demanded of the substitute, that is the rural roads where no charges are being imposed. How elasticity effects the demand for motorways with/without alternative routes (Motorways without alternative routes) (Motorways with alternative routes) From an environmental perspective road charges should be introduced for the vehicles entering the urban areas. This is because there is significant evidence that motor vehicle usage will drop significantly (as shown in the Button 1993 table). The additional revenue raised can be used to fund the much-needed public transport services. Therefore those who will no longer use their own private vehicles will potentially benefit from an improved transport service. Evidence showed that those who continue to travel into the urban areas using private vehicles are also more satisfied if the money they are charged goes into improving public transport (as shown in the MORI diagram). For motorways the decision from an environmental perspective is split between those motorways that have alternative routes and those that do not. On those motorways with possible alternative routes the disadvantages (such as the possible movement of traffic as opposed to reduction) outweighs the potential benefit of the additional revenue. Where alternative routes are unavailable road charges are appropriate as the disadvantages stated above and in the report are much less of a problem. Revenue generated from these charges can then help urban areas with the possible improvement of public transport (like the Norwegian example). If roads were a â€Å"typical† competitive commodity, supply and demand would determine its price and an organization or business would own it. However there isn’t a market for roads, and (in places where there are no toll systems in place) people can use the roads for free (i.e. its price is treated as if it was zero). Added to the fact that no one actually owns the roads (the government only has a requirement to maintain them), we have a demand for the usage of the roads by the public, but a failure of a market to emerge. Therefore there is no mechanism to ensure that the resource of roads is used efficiently. Therefore if someone owned the roads and could charge a price for their use, a market would emerge leading to an efficient use of the commodity, because the price reflects the value for alternative uses. Thus through the understanding of how a road is economically defined and the fact that at the moment, it is economically â€Å"inefficient†, a measure has to be taken. Therefore we will now explain using the defining economic principles why road use charges should be introduced for motorways and for urban areas. Arguably, though, the notion of congestion charges is more readily available to be implemented than road tolls, but the economic principles that should guide the design of this policy towards taxing and charging road users also applies to road tolls. The aim of the policy is to find an equilibrium position, where the marginal cost of using the resource is equal to the marginal cost of providing the resource. Figure 4: The equilibrium position and the effect of road charges The graph is labelled with â€Å"level of traffic† on the x-axis and â€Å"generalised cost of driving† (this means that the generalised costs are time and money spent on making the journey) on the y-axis. The first line drawn in was the demand curve (D), which is also the marginal benefit (MB) curve, (which is a straight line). The easiest way to describe it’s negative gradient is as the number of people using the road increases, the marginal driver will have a lower benefit from the road than the previous one. There are two costs curves to be added to the diagram. The private marginal costs (PMC) are the direct costs to the driver. The PMC curve is also the supply curve (S). It is an upward sloping curve because with low traffic densities, the only cost to the driver is petrol. However as levels of traffic increase, congestion increases, and thus the driver incurs time costs as well. The social marginal costs (SMC) has the same base as the PMC (i.e. petrol costs) but as traffic increases, not only does it represent the time cost to that one particular driver, there is also the time costs that each driver imposes on every other driver. As is to be expected, at the moment, drivers act according to their PMC curve and the level of traffic on the road increases to the point (Xo), where the PMC is equal to the MB. Thus as the economic theory states, we are at a level of traffic above the social optimum, because the social optimum occurs when the SMC is equal to the MB. This is shown at the point X*, which is the social optimum and the optimal level of traffic. To get the level of traffic reduced from Xo to X*, we have to impose an extra charge or tax â€Å"P† (= P*-Po). This is referred to as the Pigouvian tax amount. This amount (P) forces the road user to take into account the costs of the externality that they are generating, and thus induces them to operate at the efficient level. Since this â€Å"tax† is a congestion charge, it should only be imposed where congestion arises, and the level of charge should be directly related to the amount of congestion. Therefore the inner cities (i.e. urban areas) and motorways, having high levels of traffic, should have higher charges than areas with low levels of traffic. Traffic imposes a cost on society over and above the PMC. To achieve the socially optimal solution, these costs should be passed on to the driver. In doing this, only those drivers who were willing to pay society for the externalities they cause would use the road network. Thus levels of traffic would fall to the optimal output level, reducing congestion and pollution in the desired areas of urban areas and the motorways. It could be argued that if drivers were charged for the delays and road congestion they impose on one another, some of them would arrange to travel at different times, by different means e.g. rail and bus, or arrange to use different routes where road use charges can be avoided completely. There are various technological methods of charging for road uses. The simplest method is buying a license to enter a zone at certain times. The license, like a tax disc, would be prominently displayed with traffic wardens policing the system. However this would mean that only people parked in these zones would get checked; it would not include people just driving through them. To do this, traffic wardens would somehow have to physically stop cars and charge them for entrance into the area. To set this up in the UK would be very difficult with our complicated network of motorways, urban and rural roads and actually stopping cars would cause more congestion rather than reducing it. Another method that uses the new technology of electronic tolls/beacons, no longer requires motorists to halt at tollbooths. As motorists drive past these tollbooths, the toll registers the electronic number plate and sends a signal to a recording computer. This is a very direct way to charge the amount specific to the road and to the time of day with the amount due being deducted from their bank account. However, this would infringe on privacy rights, as it would enable people to derive individuals’ locations. Hence the use of smartcards would be more preferable like the method used in Hong Kong. The driver inserts a prepaid card, like a telephone card, into the electronic number plate and payments are debited from it when a tollbooth is passed. Only in a case where the card runs out of credit do the central computer monitors start charging directly for road use. The installation of electronic technology would have the drawbacks of being time consuming and very costly. Not only is there the construction of tollbooths, the installation of smartcards on every single car, but maintenance as well. A type of security system would also be needed to prevent free riders1, for example they could remove the smartcard or tamper with it. A method of overcoming this would be the use of cameras on each booth to capture an image of the license plate if a smart card was not detected. The most technical method, and therefore probably the most expensive, would be satellite car tracking technology. This uses existing Global Positioning System (GPS) satellites to track vehicles via electronic black boxes fixed to the dashboard of all vehicles. The problems associated with using satellites are similar to that of the above; that is the infringement on privacy rights and costs of setup and maintenance. However one possibility with this method is that it can also be used in conjunction with locating stolen cars. From a technical point of view smartcards seem to be the most sensible option. Although it would be more costly than just buying licenses, this method would lead to less congestion, as there is no stopping to enter the areas being charged. It is less expensive than using satellites and would not infringe on privacy rights. However there are difficulties and limitations with implementing any of these road use charging methods; the general public will have to be educated in the use of them, which will be quite complicated due to different regions and the specific times of the day having varying charges. The aim of this report was to analyse the argument that as elsewhere in the world, road charges should be introduced in the UK, both for motorways and for urban areas. On addressing the argument the environmental and the consequential factors of congestion needed to be considered. On considering the environmental factors the conclusion is that the reduction in pollution levels would only occur if the road charges reduced private road use and increased the use for public transport (i.e. there were no alternative routes that people had switched to avoid the charges). However a reduction in congestion on motorways and urban areas could certainly improve a firm’s economic profit as the investigation into consequences of congestion revealed. Having decided the obvious need for charges with the economic principles supporting this, whether the application of schemes is technically possible became important. For this factor the smartcard road-charging scheme emerged as the best option. On bringing all these points together road charges are both necessary and feasible for UK motorways and urban areas. Except by environmental perspectives where additionally there have to be few alternative routes, thus preventing people congesting other roads and avoid payment. How to cite Road use charges should be introduced in the UK both for motorways and for urban areas, Papers

Sunday, May 3, 2020

Stressful Jobs free essay sample

For many workers stress is part of the job description. Some types of jobs trigger more stress than others, and would drive most people out of their mind. I think two of the most stressful jobs are air traffic controller and commercial airlines pilot. These jobs can be notoriously stressful because of the communication, responsibility and working hours. Communication is a vital part of these jobs. Air traffic controllers are trained to focus on the exact words pilots and other controllers speak. Commercial airlines pilots need to ensure all information on the route, passengers and controllers received, because a single misunderstanding can have tragic consequences. Next, the responsibilities of both jobs are similar. Air traffic controllers are responsible for the safety of aircraft; they analyze factors such as weather and fuel requirements, they alert the airport to emergencies and inspect and control radio equipment and airport lights. Similarly, commercial airline pilots are responsible for the passengers and coworker safety. We will write a custom essay sample on Stressful Jobs or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page They also analyze the weather, plan the route to be taken and ensure they have the correct amount of fuel required. They have to react quickly and appropriately to changes and emergencies. Finally, the working hours are variable in these jobs. Air traffic controllers work rotating shifts, including nights, weekends, and holidays because is a twenty four hour, three hundred sixty five day days a year job. Commercial airlines pilots spend a considerable amount of time away from home, because some flights are overnight, and as the same as air traffic controllers they work on weekends and holidays. In conclusion, these jobs are really stressful because they are mentally challenging and they have a great responsibility taking care of the lives of thousands of people.