Ch 2 Observational Methods Underestimate The Strength Of Competition Among Plant Species Essay Example

Ch 2: Observational Methods Underestimate The Strength Of Competition Among Plant Species Paper Context In order to understand the biological causes of plant diversity and predict the functional consequences of losing plant diversity, we need to accurately measure the ecological processes that underpin our understanding. Species interactions like competition are the main deterministic forces that structure plant communities. The strength of interactions within and between species determines the types and number of species that stably coexist in diverse communities, and also the relative abundances of each species. Species interactions also drive the effects of diversity on ecosystem functioning by altering the per capita performance of species within the community. The community-level properties that determine ecosystem functions are the product of which species are present in the community and the interaction between those species. Therefore, in order to develop a predictive understanding of how plant diversity influences the functioning of real-world ecosystems, we need a predictive understanding of species interactions in natural plant communities. And in order to have a predictive understanding of species interactions, we need tried and tested methods for measuring species interactions, which consistently and accurately predict how plant communities respond when diversity is lost. Here, I show that existing methods underestimate the impact of diversity loss on the remaining plant community. Chapter Summary We will write a custom essay sample on Ch 2: Observational Methods Underestimate The Strength Of Competition Among Plant Species specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Ch 2: Observational Methods Underestimate The Strength Of Competition Among Plant Species specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Ch 2: Observational Methods Underestimate The Strength Of Competition Among Plant Species specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Competition among neighbouring plants for shared resources is one of the key ecological forces that shapes plant communities. But measuring competition has always been a challenge, leaving controversy over the relative role of competition between species. Observational methods for measuring plant competition have become popular. This approach assumes that we can infer competitive effects from natural variation in the densities of co-occurring species. The effect of competition between species is quantified by predicting how the population size of each species would respond to competitor removal. However, this approach remains untested. We tested the predictive accuracy of this method by combining observational and experimental approaches. We grew four sand-dune annual species in monoculture and mixture. Their local compositions were left to naturally develop, making our data relevant to natural communities. We performed an observational analysis on the mixtures and predicted how each species would perform in the absence of competitors. We compared these predictions with our independent test: the monocultures, where each species was grown in isolation. We predicted that competitive ability increased with seed size, a well-known aspect of competition between these species. We occasionally predicted strong responses to competitor removal. Even so, we consistently underpredicted the effect of interspecific competition, for most species by at least half that observed. The method failed our test so we should infer competition from natural communities with care. We suggest that the method underpredicted the effect of competitor removal because the data lack information on fundamental niches. Species are observed after they have been confined to realised niches by competition, so we lack the information to predict how they will respond to competitive release. Introduction Competition for shared limiting resources is the fundamental assumption that underpins community ecology (Darwin 1859; Tansley 1917; Clements et al. 1929; Gause 1934; Macarthur Levins 1967; Harper 1977; Grime 1979; Tilman 1982, 1988; HilleRisLambers et al. 2004, 2012; Adler et al. 2010; Craine Dybzinski 2013). By understanding the nature of competition between species we can understand how they coexist (Chesson 2000), as coexistence requires that species limit themselves more than they limit others (Adler et al. 2007). This is the basis of the ecological niche concept, in which species are expected to evolve in a direction that will minimise interspecific competition, otherwise competitive exclusion is the inevitable outcome (Roughgarden 1976; Rees et al. 2001; Chase Leibold 2003; Le Gac et al. 2012; Rabosky 2013). Yet, despite this clear theoretical expectation, the nature of competition in real plant communities has always been controversial (Grime 1963, 1973; Connell 1983; Tilman 1987; Goldberg et al. 1999; Connolly et al. 2001; Silvertown 2004; Craine 2005; Levine HilleRisLambers 2009; Rajaniemi et al. 2009; Rees 2013; Trinder et al. 2013). While any farmer or gardener can confirm that competition depresses plant performance, it is less clear how best to measure the extent and nature of this competitive suppression in natural communities. One obvious way is to add or remove plants and measure the response of their neighbours (de Wit 1960; Clatworthy Harper 1962; Silander Antonovics 1982). While seemingly straightforward, removal experiments have been heavily criticised because, among other things, species respond over different timescales (Putwain Harper 1970; Allen Forman 1976). Hence, the immediate response to the removal of a particular species might differ greatly from a longer term outcome. Another common method is to experimentally grow focal plants surrounded by different numbers and types of neighbours. Such experiments often reveal that interspecific competition is strong and asymmetric (Goldberg Barton 1992; Gurevitch et al. 1992; but see Law Watkinson 1989). But it is not clear how to translate these measurements into the field, where densities and conditions could be very different. In short, all direct manipulative methods for measuring the strength of competition among plant species have been criticised, as either methodologically flawed or because they take place under unrealistic ecological conditions (Connell 1983; Connolly 1986; Wilson 1995; Freckleton Watkinson 1997). Partly in response to these criticisms, plant ecologists have turned instead to observational approaches (Rees et al. 1996; Law et al. 1997; Freckleton Watkinson 1999, 2000). Rather than manipulating the system, observational methods exploit natural variation in density and species composition found within real plant communities and can be used to estimate individual-level competitive effects. For example, in neighbourhood modelling, detailed spatial maps of all plants allow focal plant size or fecundity to be modelled as a function of the number and identity of close neighbours. Using this method, estimates of individual-level competition coefficients have been obtained in both artificial and natural settings (Mack Harper 1977; Coomes et al. 2002; Turnbull et al. 2004; Stoll Newbery 2005). Alternatively, counting the numbers of plants in permanent quadrats allow researchers to track changes in population sizes from year to year at small spatial scales. Changes in population size between years can then be modelled as a function of the number of neighbours, again allowing competition coefficients to be estimated (Pacala Silander 1990; Rees et al. 1996). The pattern of interspecific interactions estimated from such observational analyses is mixed (Law et al. 1997; Freckleton et al. 2000; Stoll Newbery 2005; Martorell Freckleton 2014). But many studies have found that interactions between competing species in the field are extremely weak (Rees et al. 1996; Turnbull et al. 2004; Mutshinda et al. 2009; Comita et al. 2010; Martorell Freckleton 2014). However, this result throws up a problem. If competition between species is really so weak as to be negligible, then species would show little or no response to the removal of others. This was best illustrated by Martorell Freckleton (2014) who parameterised a population model for each species in their community separately and then quantified the effect of removing the remaining species. Their results showed that very little response was expected overall—though some species showed positive or negative responses, and rarer species seemed most suppressed by competitors. But this overall result, in turn, suggests that plant communities are not fundamentally competitive, a result clearly at odds with most experimental work. So are the weak interspecific interactions estimated using observational data simply some artefact of the method, or are plant communities in nature truly non-competitive? To resolve this paradox it is essential to combine observational methods for estimating competition coefficients with an independent test of model predictions within the same system. Here we present results from an experiment conducted under semi-natural conditions in which a community of annual plants was established and 2.27 allowed to grow and reproduce for several years. The communities were subdivided into small cells to which we could fit models of population growth using observed cell counts taken from the last two years of the experiment. After fitting models and estimating competition coefficients, we predicted how each species would respond to the removal of the others, and similar to Martorell Freckleton (2014), we predicted very little response to removal. However, we could then compare these predicted population sizes with those of monocultures of each species established at the same time. These comparisons revealed that the effect of interspecific competition had been grossly underestimated. Our work therefore reveals that when observational methods uncover weak interspecific interaction coefficients within natural communities, individual species might still suffer from strong interspecific suppression. Methods Overall approach Our overall approach is summarised in Figure 2.1. To test the accuracy of predictions made using observational approaches, we first needed a suitable dataset with which to fit appropriate models. In this case, we had established semi-natural communities consisting of seven species of sand-dune annuals subdivided into small cells (see Experiment). Population sizes of all species were recorded in two consecutive years. For each of the five common species, we then fitted a population model in which changes in local population size from one year to the next are assumed to be a function of both its own density and the densities of other species. Depending on the details of model structure (see Models) interactions between species can be positive as well as negative. Figure 2.1. The method for estimating the effect of interspecific competition. Observational data on the population sizes of each species in a community is collected over multiple years. For each species, the change in population size from one year to the next is modelled as a function of its own density and the densities of other species. Once models are parameterised, they can be used to predict the population size of each species when: (i) interacting species are present at their observed abundances, and (ii) interacting species are removed. These two predictions are compared with one another to quantify the effect of competition. Once the models are fitted, we can predict the effect of competitors on the focal species by setting population sizes of competitor species to zero and re-calculating the predicted population size of each focal species (Martorell Freckleton 2014). But to test whether these predictions are indeed accurate, we require additional data. In this case, our experiment included monoculture plots, which had been established at the same time. We therefore compared our predictions about the expected effect of the removal of competitors with observations of population sizes from monoculture plots (see Test). Experiment Seven species of sand-dune annuals were grown for four years (2010–13) in a common garden experiment in Zà ¼rich, Switzerland. The study species and their seed sizes were: Saxifraga tridactylites L. (0.006 mg), Arabidopsis thaliana [L.] Heynh. (0.025 mg), Cerastium diffusum Pers. (0.045 mg), Arenaria serpyllifolia L. (0.088 mg), Veronica arvensis L. (0.112 mg), Myosotis discolor Pers. (0.213 mg), and Valerianella locusta [L.] Laterr. (0.851 mg). They germinate in autumn and flower in spring. We analysed data for only five of these species, because Veronica and Valerianella were too rare. The experiment consisted of 80 (1 x 1 m) plots. A concrete lattice was inserted so that each plot consisted of 56 (7 x 7 cm) individual cells filled with a low-nutrient mixture of sand and compost (Figure 2.2). The lattice walls were sufficiently thick (2.5 cm) that plants in adjacent cells never overlapped aboveground. Thus, we assumed that plants within cells competed for resources, while plants in adjacent cells did not. Plants dispersed seeds freely within plots, but barriers to dispersal prevented seed movement between plots. Subdividing the plots into cells provided the fine-grained information necessary for parameterising our models. We grew eight monocultures of each species and 24 mixtures containing all seven species. Figure 2.2. Example of an experimental plot, divided into 56 cells by a concrete grid. This is a mixture plot photographed immediately after seven cells were harvested in 2011. In order to create variation in density, a gradient of disturbance was applied across plots. This facilitated the fitting of nonlinear population models, which is often hampered by a lack of information at low density (Law Watkinson 1987; Rees et al. 1996). Plots were disturbed by removing all plants from a fixed proportion of cells at the end of every growing season, just before the plants set seed. In mixture plots we applied five levels of disturbance: 12.5%, 25%, 50%, 75%, and 87.5%. In the monocultures there were only eight plots per species, so we imposed only three disturbance levels: 12.5%, 50%, and 87.5%. We selected which cells to destroy in a stratified random way, destroying a fixed number of cells from each row in a plot grid. In 2012 and 2013 there was a highly significant negative relationship between disturbance and average density per cell, indicating that the disturbance treatment was successful in creating a density gradient (Supplementary Material SA1). The experiment was established from seed in 2010 using a constant total density of 1000 seeds per plot. The number of individuals in all cells was recorded at the end of the growing season for three years (2011–13), although only occupancy was recorded from the mixture plots in 2011. The transition 2012–13 is therefore the most completely sampled and was used for the model fitting. The end-season biomass of each species was estimated by destructively harvesting and weighing seven cells from each plot during application of the disturbance treatment. Models To ensure that our conclusions were not dependent on model choices, we fitted three models with varying assumptions about the nature of species interactions and the nature of dispersal within plots. We either assumed that: (i) all seeds remain in their natal cells, or (ii) some fraction of seeds (m) remain in the natal cell while the rest (1 – m) join a global seed rain. The general form of model 1 (eqn 2.1) is: where Nt+1,i,c is the population size in year t+1 of focal species i in cell c. The population growth rate of species i in the absence of competition, ri, is modified by density-dependent interactions in the following way: where ÃŽ ±ij is the per capita effect of species j on species i. Thus, the first term in eqn 2.1 describes the expected number of individuals of species i in year t+1 that originated in the natal cell. Similarly, the average value of Fc among the cells within a plot can be calculated using: where p is the total number of cells within a plot. Thus, the second term in eqn 2.1 describes the expected number of immigrants arriving from other cells within the plot. Model 2 (eqn 2.4) contains only the first, within-cell-growth term from eqn 2.1, and thus assumes that no seeds disperse outside their natal cells: Model 3 (eqn 2.5) has a different structure. In this case we assume that within cells population growth is density-dependent, but is only sensitive to the density of conspecifics: where q is an index of cell quality. Other species affect the focal species by modifying the quality of cells: This cell quality index is a logistic function of the densities of other species in year t+1 and their per capita effects on the focal species, ÃŽ ²j. For each focal species we estimate a basal cell quality, ÃŽ ²0, and the quality of each cell can deviate above or below this value depending upon the density of other species present in the same year. Model 3 allows species interactions to be positive as well as negative (eqn 2.6)—in contrast to models 1 and 2 where they are constrained to be negative. Positive interaction coefficients might indicate facilitation. But they might simply indicate that the seedling densities of both the neighbour and focal species tend to be positively correlated, perhaps because they share a preference for the same types of cells. All models were fitted using rjags v3-14 (Plummer 2014) in R v3.1.2 (R Core Team 2014). Each model assumed that Nt+1 was Poisson distributed. To estimate the competition coefficients we specified non-informative priors, assuming they had a normal distribution (ÃŽ ¼ = 0, ÏÆ'2 = 1000). Competition coefficients were constrained to be positive—i.e. competitive—by applying an exponential transformation. A common concern when parameterising these models is that the competition coefficients and the population growth rates in the absence of competition (ri) can be correlated, because they trade off against each other (Rees et al. 1996). This can produce an unstable estimation process, whereby both parameters increase or decrease together and yet give an equally good model fit. To avoid this instability we informed the estimation process on meaningful values of ri by specifying an informative prior for each species that used the best information we had on their maximum capacity for growth. We 2.34 specified these informative priors by assuming a gamma distribution with an expected value equal to the average 2012–13 population growth observed in high-disturbance monocultures (gamma: shape = mean 2012–13 growth; rate = 1; E[X] = shape/rate). When fitting model 3 to Myosotis, we fixed ÃŽ ²0 at zero (basal cell quality = 0.5) to stabilise the estimation process, because there were significant trade-offs between ri and ÃŽ ²0. We ran all models with three sampling chains. We ensured each model had sufficiently converged on the target distribution by running an adaptation period of 40000 samples (plus 10000 burn-in). Following adaptation we monitored 10000 samples from the chains, thinning to every 10th sample to reduce autocorrelation— giving us 1000 samples from each posterior distribution. We checked that the chains had converged by plotting the sampling chains, posterior densities and chain autocorrelation. We used Gelman plots to check that chains had converged on the same target distribution (Brooks Gelman 1998). We also checked models by: (i) testing that they can recover known parameters from simulated data, (ii) examining residual diagnostic plots, (iii) plotting the model fit, and (iv) comparing simulated and observed data to look for systematic differences between models and observations (Gelman Hill 2007). Predicted data qualitatively resembled the observed data, although the observed data often showed a longer tail of right skewness. Finally, we compared model performance using DIC (Plummer 2002). To facilitate comparisons among models, all models were fitted only to cells where Nt was positive. Analysis We examined interaction matrices from the parameterised models to look for patterns in competitive effects. We then used the parameterised models to quantify the effect of competition at the population level, by predicting population sizes in the presence and then in the absence of all other species (by setting populations sizes of other species to zero). We constructed intervals on the predicted population sizes without competitors from posterior samples within each cell. We averaged across these cell-level predictions to get the median predicted population size without competitors and its 95th percentile range. These median and interval values were then expressed relative to the average predicted population size when competitors are present. The predicted effect of competitor removal is thus the ratio between the focal population sizes with and without competitors (Nt+1 without / Nt+1 with). We assessed the observed extent of competitive release by regressing the population sizes in 2013 on the population sizes in 2012 for both mixtures and monocultures. The slope of the regression line through the origin is therefore the population growth rate. We tested whether there was a significant effect of mixture vs monoculture on the growth rate of each species. If the regression slope in monoculture is, on average, steeper than in mixture, then there is a clear positive effect of removing competitors on population growth. In 2012 the range of population sizes in mixtures and monocultures was similar, so it was easy to compare treatments. The observed effect of competitor removal is thus the ratio between monoculture and mixture slopes (monoculture / mixture). Test Finally, to test whether our predictions matched our independent observations, we compared the predicted effect of competitor removal with the observed effect. We described each model’s predictive accuracy by expressing the predicted effect as a 2.36 percentage of the observed effect. We showed the credible range in each model’s predictive accuracy by using the interval for its predicted effect, which captures uncertainty in the estimation process. Results Models Model 1 was the preferred model for three species—although for two species all models performed equally well (Supplementary Material SA1)—hence we focus on results from model 1. The competitive effects estimated by model 1 were asymmetric and structured by seed size (Figure 2.3). If a species had large seeds then it usually had a strong competitive effect on those with smaller seeds. In addition, for most species the strength of intraspecific competition was greater than the strength of competitive suppression by smaller seeded species, but weaker than competitive suppression by larger seeded species (Figure 2.3). Within the context of this seed-size pattern, Myosotis is anomalous. The model estimates that it is strongly affected by several of the smaller seeded species. This seed-size pattern was even more pronounced when the model did not include dispersal (model 2, see Supplementary Material SA1). When we allowed for positive interactions, however, the pattern disappeared: instead all interactions were scattered around zero, as many positive as there were negative (model 3). Broadly speaking, all models estimated the effects of interactions with high precision. Figure 2.3. Competition is asymmetric and related to seed size. The competitive effects of interacting species (columns) on the population size of each focal species (rows). The highlighted diagonal are intraspecific competitive effects. Weak competitive effects are pale and stronger effects are darker (effects are log-scaled, so negative values describe more neutral effects). Species are ordered left to right and bottom to top by increasing seed size. Broadly speaking, competitive effects are linked to seed size, with large-seeded species exerting stronger effects. Model 2 shows even stronger seed-size structure (Supplementary Material SA1). Analysis Using these parameterised models we calculated the predicted effect of competitor removal within each cell (Figure 2.4). The seed-size structure of per capita interactions in Figure 2.3 is also clear in the population-level effects (Figure 2.4). For Saxifraga and Arabidopsis, model 1 predicted at least a doubling in population size in 50% of cells and they are predicted to increase by five-fold or more in 10% of cells. In contrast, larger seeded species are predicted to show a weaker response to competitor removal in most cells (75% of the time their ratio response is close to 1, i.e. no change). Myosotis is predicted to respond more strongly on average than Arenaria or Cerastium, reflecting the relatively strong competitive effects of other species on Myosotis (Figure 2.3). Model 2 predicts smaller population-level responses of Myosotis, better conforming to the trend that large-seeded species respond less strongly to the removal of small-seeded competitors (Supplementary Material S A1). Figure 2.4. Population-level effects of removing competitors. Distributions of the predicted effect of competitor removal at the cell level. An effect size of 2 means that the population size is predicted to double in response to competitor removal. In most cells the predicted effect is small but in some it is large. The x-axis has been truncated for clarity (removing 1% of cases). Species differences reflect the strength of competition shown in Figure 2.3. Predictions from model 2 show an even stronger seed-size structure (Supplementary Material SA1). For the observed effects of competitor removal, there was a significant three-way interaction between species, diversity level and density (F4,140 = 3.4, p = 0.01). Inspection of the slopes revealed that in four out of five cases, the slope in the monoculture is much steeper than that in mixture: for most species, population growth at least doubled when grown in monoculture (Figure 2.5). The only exception was Myosotis, whose population growth was higher in the mixture; although when we used biomass data rather than population sizes, the population growth in monoculture and mixture was the same (Supplementary Material SA1). Myosotis may have responded differently to interspecific competition because it was competitively dominant in mixtures. The hierarchy for average cell biomass in mixture was Myosotis Arenaria Cerastium Saxifraga Arabidopsis—broadly in decreasing order of seed size. It is clear that species densities are reduced by interspecific competition, especially i f they have smaller seeds. Test Comparison of the predicted (Figure 2.4) and observed effects (Figure 2.5) revealed that the models made poor predictions about the population-level response to the removal of competitors (Figure 2.6). Models consistently underpredicted the effect of interspecific competition in four cases, but overpredicted in the case of Myosotis (Figure 2.6). The three different models had similar predictive accuracies, despite differences in the way they describe species interactions (Figure 2.6). Figure 2.5. Population growth rate is higher in monocultures than in mixtures. The plot-level average cell population sizes in 2013 (Nt+1) versus 2012 (Nt) for each species. Blue dots show monocultures, red dots show mixtures. The regression lines, fitted through the origin, show the average population growth rate. Species are ordered by increasing seed size. Population growth rates were higher in monocultures than mixtures for four species. The exception was Myosotis, which was competitively dominant. In terms of population size Myosotis performed much better in the mixtures, but biomass data show no difference between diversity treatments for this species (Supplementary Material SA1). Figure 2.6. The effect of competition is consistently underpredicted. We assessed how accurate our predictions were by comparing the predicted effects shown in Figure 2.4 with the observed effects shown in Figure 2.5. Predictive accuracy is the predicted effect shown as a percentage of the observed effect (log-scaled). All three models behaved similarly. 2.41 Discussion We used tried and tested techniques to fit community models to observationalstyle data garnered in an experimental context. The models appeared to capture the relative competitive abilities of species, as the interaction matrices obtained are consistent with previous work (Rees 1995; Turnbull et al. 1999, 2004). This result, and other diagnostic tests, encourage us to believe that we fitted sensible models to the data. We then used the models to predict what would happen to each species once competitors were removed. Uniquely, we were able to provide an independent test of the model predictions, as the experiment included monoculture plots in which each species was free from interspecific competition. The models made poor predictions about the expected extent of competitive release. For most species the extent of competitive release was severely underpredicted, suggesting that we had underestimated the strength of interspecific interactions in multi-species communities. Why did we underestimate the strength of interspecific competition? The first possibility is that we fitted poor models, but this seems unlikely. The competition coefficients were well-estimated with small standard errors, from independent sampling chains that converged on the same posteriors. Model uncertainty was very low, reflected by the narrow intervals in Figure 2.6. The models were a good fit to the observed data. Distributions of cell population sizes in data simulated from the models closely resembled that of the observed data, further indicating that there was no systematic bias. The three models we fitted described species interactions differently and yet all poorly predicted the effect of interactions in the same way. Our test was fair, as it expressed the same effect as our predictions—the mean change in population size in response to competitor removal—and did so over the same range of data. The wide variation in observed data meant that we avoided potential underestimation due to observing species only at high densities (Law Watkinson 1989). These reasons lead us to believe that our analysis was not at fault for underestimating the strength of interspecific competition, but instead the underlying problem is more profound. The second possibility is that models which estimate the strength of competition in natural communities simply cannot be used to predict the effects of species loss. This might happen because species in natural communities are already confined by competition to realised niches. The fundamental niche represents all conditions in which a species can exist, whilst the realised niche is those conditions in which the species actually exists in the presence of interacting species (Chase Leibold 2003). If species are confined to parts of the habitat where they tend to compete best, then the strength of interspecific competition, as assessed by these methods, will be weak. In contrast, once competitor species are removed the remaining species may be able to expand their niche—assuming that their fundamental niche is wider than their realised niche. But without information on fundamental niches we cannot know the extent that species will respond to competitor removal. Therefore, to predict species responses from natural communities is to predict beyond the range of available information. This explanation for weakly interacting natural communities has previously been called the ghost of competition past (Connell 1980; Law Watkinson 1989). It may explain why interspecific competition measured in natural communities is often weaker (Turnbull et al. 2004; Stoll Newbery 2005; Comita et al. 2010) than 2.43 that measured in experiments (Gurevitch et al. 1992). Our results suggest this is a general problem that is likely to be present in any analysis using similar principles. Further simulation modelling is required to confirm this idea. What are the implications? Ecologists have argued long and hard about the best way to measure competition; in particular, because the results of experimental work conflict with direct measurements of competitive interactions in natural communities. Our study potentially provides a resolution to this debate. In experiments plants are forced to compete, whereas in natural communities plants often display strong spatial aggregation that increases the chance of meeting conspecific neighbours. Species may aggregate in natural communities due to local dispersal. Freckleton Watkinson (2000) recommended that the scale of sampling should reflect the dispersal abilities—and resulting clumping—of the species being monitored, to ensure that multiple species are observed within sampling units. But the larger the scale at which data are sampled, the more the effect of local interactions becomes blurred. There are ways to incorporate dispersal into the modelling (Pacala Silander 1990), as we have done. Aggregation can also be caused by species-specific requirements for particular ecological conditions (Law et al. 1997). This has been suggested before as an explanation for the weak nature of estimated interspecific effects (Rees et al. 1996; Freckleton Watkinson 2000), but the magnitude of this problem has never been quantified. These methods have assumed a homogeneous environment. We believe this assumption was problematic even in our experimental setting—it was also considered by Mack Harper (1977). Perhaps this problem could be addressed by measuring environmental variation, although it is not clear which variables are important to describe species preferences. But ultimately, if species rarely interact due to spatial aggregation then the effect of their interactions will be limited. If species weakly interact in natural communities because they have been confined to realised niches, then we should be wary of interpreting models parameterised using observational data to conclude whether such communities are strongly structured by interactions (Martorell Freckleton 2014). If much of the competition goes unseen we cannot claim whether or not communities are fundamentally competitive from such analyses. In particular, these models may give no information about how the community is likely to respond to the loss or removal of species, which is essential in a world where species are likely to be lost, for example, through new diseases (e.g. Thomas 2016). Future directions To understand more deeply why observational data are lacking, we need to combine experimental and observational approaches. Freckleton et al. (2000) used similar analytical techniques on a long-term dataset where densities were perturbed halfway through the sampling programme. However, they and others have criticised experimental approaches to estimate competition (Freckleton Watkinson 2000, 2001). Removal experiments are still practised (e.g. Olsen et al. 2016), albeit more rarely and with acknowledgement of their caveats. Perhaps experimental manipulations are more in need than recent studies would suggest. There is no method to measure competition that is not flawed. Much progress has been made in critiquing previous methods and developing new ones. We have shown that current observational methods also need refinement. If it is possible to devise a method that accurately predicts the impact of species losses from diverse communities, what extra information would be required? Traitbased approaches may be useful in predicting which species will respond most to the loss of another species. This approach would be most effective if we are able to identify which traits best explain how remaining species will expand their niche in response to competitor removal. This could be combined with experimental approaches that remove one species at a time and measure the response of the remaining community. Experimental communities may also be useful for observing how species become confined to their realised niche if we can measure how species are affected by the gradual shift from homogenised to semi-natural conditions. Alternatively, we might need more detailed data on how species are affected by competition during specific life-history stages, rather than the more common observation of adult–adult transitions. Current observational data and methods are valuable tools, but we will need a greater combination of approaches to fully understand the role of competition between species in natural plant communities. 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Personal, Professional, and Ethical Belief System Essay Essays

Personal, Professional, and Ethical Belief System Essay Essays Personal, Professional, and Ethical Belief System Essay Paper Personal, Professional, and Ethical Belief System Essay Paper In the human services field. personal ethical belief systems combined with professional moralss work in partnership to steer human service professionals in unknoting ethical quandary. An increasing figure of professionals and clients seek out to specify the cardinal policies of the human services field. Humans develop an integrating of values. criterions. and beliefs from birth throughout life. The values. criterions. and beliefs developed through life aid in qualifying personal ethical belief systems. Our personal ethical belief system unites with our professional moralss to determine the ethical decision-making procedure. A codification of moralss is indispensable to organisations in the human services field. A codification of moralss identifies adequate behaviour. endorses high criterions of pattern. supplies a criterion to utilize for self appraisal. and establishes a construction for professional behaviours and duties. Human service professionals promote the unity and moralss of the profession. As a consequence. it is important for a human service professional to remain educated and knowing of the theoretical footing of their ethical belief system. other theoretical moralss. ethical and legal issues. and the ethical rules of their organisation. Every individual possesses a nucleus system of values. My personal ethical belief system is derived from my nucleus system of values. the people who helped determine those values. and the decision-making factors I use today to better them as necessary. My parents. pedagogues. Sunday school teachers. sermonizers. decision makers. God. leaders. and many others in the community aid determine my values and supply the footing for my ethical belief system. I articulate values in my associations with other people when I am loyal. dependable. honest. generous. swearing. trustworthy. experience a sense of answerability for my household. friends. coworkers. community. state. and the organisation where I volunteer. My parents taught me to be a Christian individual and to make what is morally right in the eyes of God. I met troubled times throughout my life. but I believe because of my strong morally sound background I could take those experiences and learn from them. My parents remain astonishing function theoretical accounts in my life. I attend church and congratulations God every hebdomad as a reminder of why I keep my moral values and belief system close and beloved to me. I choose to go on my instruction on many facets in my life to help in regulating decision-making factors. As a proud citizen of America. I realize the disagreement associating to Torahs and moralss. A jurisprudence is a edict or authorities regulation prepared to penalize those whom disobey. Torahs are consistent. universal. published. accepted. and enforced. Ethical motives is a sense of what is right and incorrect morally. However. there is a difference between moralss and morality. Harmonizing to Anstead. S. M. ( 1999 ) . â€Å"Morality refers both to the criterions of behaviour by which persons are judged. and to the criterions of behaviour by which people in general are judged in their relationships with others. Ethical motives. on the other manus. encompasses the system of beliefs that supports a peculiar position of morality. † The jurisprudence frequently integrates ethical criterions to which society subscribes. Most ethical determinations come with extended punishments. legion options. varied consequences. unsure and personal effects. My belief system is derived from my assurance that there is more to reality than what we see. I have faith in a religious side of world beyond what we can see or see. The footing of my ethical belief system comes from my values and those whom attributed to those values including my life as a truster in God and as a Christian. My metaphysiological position of theism supports my positions of ethical tyranny. I found this quotation mark interesting and supportive of my ethical belief system. Harmonizing to Worldview Dictionary ( 2011 ) . â€Å"Ethical tyranny is the belief that right and incorrect are unchanging. non determined by the person or the civilization ; revealed by God through both general and specific disclosure. † Ethical tyranny follows one cosmopolitan moral criterion. God is infinite. everlasting. and never-changing. God set the Torahs of the land therefore I must stay by what is determined by God as right and incorrect. God has revealed this truth through his creative activity and disclosure. God is absolute. God created all people as peers. As a truster in God. I serve him through service to others and my community. I take on duties of assisting others in my community. volunteering. and donating goods. At all times I abide by these rules: worship merely God. regard people. be low. be honest. populate a moral life. be generous with clip. pattern my positions. make non knock. justice. or condemn. make non keep a score. and forgive others. I believe it is right to defy enticement while cognizing that evil lurks around. I believe one-day God will return and seek judgement for his people. To populate morally and ethically divine is obligatory to hold ageless life. As a individual and professional. I understand and acknowledge that non all human existences embrace the same positions as me and I respect the positions of others. In the human service profession. my personal ethical belief system helps steer the work I do as a voluntary at Wise Choices Pregnancy Resource Center ( WCPRC ) . At WCPRC the mission is to assist adult females do life confirming picks. The services offered are free gestation proving. free limited echogram. acceptance referrals. abortion instruction. abortion recovery plans. and earn while you learn plan for babe demands. I chose this organisation specifically because it supports my beliefs on pro-life. Pro-life supports my beliefs as a Christian. I advocate supplying adult females with instruction and options to abortion. Harmonizing to Wise Choices Pregnancy Resource Center ( 2012 ) . â€Å"Through the old ages. Wise Choices. has been able to supply assorted sorts of aid and â€Å"counseling† to the adult females of Wise County and the environing countries. We non merely assist the client. but household or friends of the client. in respects to the state of affairs the client is confronting. We believe we can do a difference in the lives of adult females. one at a clip! As a Christian adult female. I think that it is of import to assist clients who are abortion minded or abortion vulnerable see the chances they have to salvage the life they have created. As an advocator. I ask the client if she would wish the chance to hear the Gospel. If she accepts I can portion the love God has for his kids including the client and her unborn kid. I besides have the singular opportunity to inquire the client if she wants to accept Jesus as her Jesus from wickednesss. I explain to my clients willing to accept and hear the Gospel that they may inquire God to forgive them for their wickednesss. It is besides an juncture to speak to my clients about abstention until matrimony. Each of the values listed above ushers me to be an advocator for the unborn kid who is defenseless in his or her right to life. Often I am met with challenges. such as a adult female who is pregnant from colza or incest. It is frequently difficult to explicate to a adult female with traumatic experiences that it is still of import to give birth to their kid even in palliating fortunes. Some of the hardest instances come from a adult female who is abortion minded or vulnerable and has no involvement in hearing the Gospel. I go place and pray for the client. her household. and the unborn kid. As a member of the National Organization for Human Services ( NOHS ) . the Code of Ethics is an of import counsel tool in ethical quandary. The Code of Ethics is a fit criterion of behavior for human service professionals to see in the ethical decision-making procedure. Harmonizing to the National Organization for Human Services ( 2009 ) . â€Å"Human service professionals respect the unity and public assistance of the client at all times. Each client is treated with regard. credence and self-respect. Statement two is an ethical rule non hard to adhere. As a human service professional the unity and public assistance of the client is my responsibility to recommend. I make a committedness to my client when I become his or her advocator. and that committedness involves seeking the best result for the public assistance of my client. As a Christian I see wholly people as equal. and I treat others with regard and self- respect at all times. It is of import to retrieve that it could be me in the same state of affairs. The NOHS Code of Ethics lists ethical rules that will non be difficult to adhere. However. I found a 1 that might turn out to be somewhat hard. Harmonizing to the National Organization for Human Services ( 2009 ) . â€Å"Human service pedagogues uphold the rule of broad instruction and encompass the kernel of academic freedom. abstaining from bring downing their ain personal views/morals on pupils. and leting pupils the freedom to show their positions without punishment. animadversion or ridicule. and to prosecute in critical thought. † The part of this statement that might be hard adhering to is bring downing my ain personal values if I were a human service pedagogue. I know that I could forbear from bring downing my ain personal values. but I find it would be hard. As a Christian. I think it is of import to portion the Gospel and what it means to me. For illustration in an moralss category pupils reflect upon their ethical belief systems and where they originated from. I think if I were the pedagogue it would be difficult to forbear myself from talking my ain worldviews and ethical motives to other pupils. However. as a professional it is of import to follow the ethical criterions set for the organisation in which I am an employee. and the NOHS Code of Ethics. A Code of Ethics is steering rules that apply to different facets of life. Ethical criterions pertain excessively many of the undermentioned general subjects: the usage of psychological trials in the courtroom. the lie sensor. boundaries of competency. unity. sexual torment. human differences. and the legal definition of insanity. * The Use of Psychological Trials in the Courtroom- The usage of psychological trials in the courtroom to back up the testimony of experts scopes from applaudable to debatable. There is a necessity for a sensible pattern of revelation of psychological trial information during tests and courtroom processs. afterwards the stuff may be sealed. Psychological trials in the courtroom should adhere to ethical criterions by stating the truth and non stating truth deliberately inaccurate. In Statement 28 of the NOHS Code of Ethics. human service professional’s duty to the profession is to move with unity and honestness. My ethical belief system incorporates honesty and unity at all times. The Lie Detector – After researching ethical criterions of the prevarication sensor. I found a Code of Ethical motives from the American Polygraph Association. The ethical criterions stated included: rights of testees. criterions for rendering polygraph determinations. post-examination presentment consequences. limitations on rendering sentiments. limitations on scrutinies. fees. criterions of coverage. advertizements. release of nonrelevant information. limitations on scrutiny issues. and APA oversight authorization. Lie sensors set up the difference between a prevarication and the truth ; if a individual has nil to conceal a lie sensor should be no job. Lie sensor usage brought justness to households over the old ages. and continues to make so with regard to the rights of all individuals who take the prevarication sensor trial. As portion of my personal moralss it is indispensable in the justness system. In Statement 35 of the NOHS Code of Ethics. answerability is maintained by the human service professional. * Boundaries of Competence – Human service professionals are merely to carry on research. Teach. and supply services merely within their boundaries of competency. based on personal experience. instruction. supervised experience. and survey. Reasonable stairss should be taken to guarantee competency in countries emerging and preparation does non yet exist. My personal moralss system is of import to pattern my positions. This relates to boundaries of competency I would non pattern something I did non believe to be true. In Statement 40 of the NOHS Code of Ethics human service pedagogues demonstrate high criterions of scholarships and remain current with developments in human services. * Integrity – Accuracy. honestness. and truthfulness is a function all human service professionals abide by. Integrity is something I incorporate in my personal ethical beliefs and value system. Ethically it is incorrect to steal. darnel. prevarication. fraud. or belie a client or a friend. Harmonizing to American Psychological Association ( 2012 ) . â€Å"In state of affairss in which misrepresentation may be ethically justifiable to maximise benefits and minimise injury. psychologists have a serious duty to see the demand for. the possible effects of. and their duty to rectify any resulting misgiving or other harmful effects that arise from the usage of such techniques. My belief in God’s written words of the Bible helps determine my ethical position on unity. Statement five of NOHS Code of Ethics protects the unity of client records. * Sexual Harassment – Sexual torment is solicitation. verbal or gestural sexual behaviour. and sexual progresss. All people have a right to gain a life free from relentless and permeant Acts of the Apostless of sexual torment. It is non right to endanger an employee’s dignity. regard. and possible promotion in the workplace. As a professional. behavior should compare to higher criterions of unity and safety in the workplace. Statement 24 of NOHS Code of Ethics provinces that human service professionals should describe unethical behaviour of co-workers. My personal ethical beliefs in this state of affairs associate back to my Christianity and the Torahs of the land that an act of sexual torment is non moving suitably in the eyes of God. * Human Differences – Human service professionals create public trust through ethical and moral Acts of the Apostless. Human diverseness is one of those Acts of the Apostless. It is imperative to be culturally sensitive to all persons at all times. I promise to function all people with the purpose to protect their public assistance without judgement of any sort. In my ethical beliefs God is the justice. and he will return to make that one twenty-four hours. Statement two of the Code of Ethics for NOHS says human service professionals treat clients with regard and esteem their public assistance. and Statement 20 refers to diverse backgrounds. * The Legal Definition of Insanity – In my ethical belief system a individual who commits an act out of insanity is still responsible for what he or she has done. It is one’s responsibility to make what is morally right. Questions of right and incorrect are overriding. and hence as I believe absolute. Statement 37 of the NOHS Code of Ethics addresses the demand for womb-to-tomb acquisition. and I relate that to this instance as a duty the human service professional has to its client. as the individual who commits a offense out of insanity has a duty to pay the effects. As a member of Team C throughout this class associating to moralss and jurisprudence in the human services profession we did a squad presentation on deontology theory. as presented earlier in the paper. Harmonizing to Alexander and Moore ( 2008 ) . â€Å"The word deontology derives from the Grecian words for responsibility ( deon ) and scientific discipline ( or survey ) of ( Son ) . This normative theory dressed ores on what she should make from a moral point of view. Deontology is a theory that helps to steer and entree our picks in what we ought to make. † Deontology theory assumes at least three of import characteristics. The first characteristic concludes that responsibility should be done for duty’s interest. An illustration. Acts of the Apostless of promise breakage. lying. or slaying are incorrect per se. and it is the responsibility of worlds non to make these things. Second. worlds ought to be treated as topics of intrinsic moral value ; intending an terminals in themselves and neer as a mere means to another terminal. The 3rd characteristic is a moral rule is a definite indispensable that is universalizable ; intending it must be applicable for everyone whom is in the same moral state of affairs. The theoretical footing of my personal ethical belief system falls under the deontology theory. Deontological theory claims the moral rightness or inappropriateness of an action does non depend upon the nature of its effects. but on its intrinsic qualities. Deontology theory was founded by Immanuel Kant. Kant was motivated by the deficiency of a function for responsibility in Utilitarianism. something he believed to be the foundation of all morality. Deontology supports moral tyranny. Actions are either moral or immoral regardless of the beliefs of an person. society. or civilization. Ethical motives of the existence are intrinsic in the Torahs of the existence and the nature of humanity. Therefore. the theoretical footing of my personal belief system as a Christian is supported by deontology theory. My belief in God supports ethical tyranny. deontology. and my personal ethical belief system.

Marketing case write up Essay Example | Topics and Well Written Essays - 500 words

Marketing case write up - Essay Example McGraw intends to retain Oscar Mayer’s (OM) reputation within Kraft Foods as the fastest growing profit maker, and hopefully achieve a +4% volume growth and +15% profit growth for the coming year. His division has two business lines: the traditional OM meat-based products line and the recently acquired/fast growing Louis Rich (LR) turkey-based products line. He has to tailor his strategy which would balance the interests of both lines and yet, achieve his targets. Strengths: OM products enjoyed customer confidence for nearly 100 years, and contribute a massive 82% or $110 MM of the total profits. Acquisition of LR and investment in its line of white meat products has proven to be a strategically wise decision, as shown by the strong volume growth of its products. Weaknesses: There is a significant shift in consumer preference towards less fat/salt food products, i.e., the LR line, while OM line has been giving the maximum profits; its prices are out of tune with the market. Investment costs for acquisitions and/or A&P to buttress LR business will further depress OM business and depress short-term profits; competition from unbranded products will add to the pressure on pricing and bottom line. LR products are susceptible to copying. In terms of convenience, taste, price and customer satisfaction factors, there is a greater negative bias on OM products. OM’s frozen product ‘stuff n burger’ has not been an outstanding success. Opportunities: LR product line business is showing promise of further growth, albeit at the expense of OM product line. LR can add further range to its existing products through in-house R&D efforts that are already underway. Or, it can acquire one or more mid-size firms dealing in white meat products to complement present facilities and products. Threats: Consumers are shifting to healthier and more convenient foods, directly impacting OM range. LR range of products is easy to copy and competition from branded as well