Answer:
Cost-volume-profit (CVP) can be used to determine how changes in costs and volume affect a company's operating income and net income. CVP analysis requires that all the company's costs, including manufacturing, selling, and administrative costs, be identified as either variable or fixed.
A CVP analysis consists of five basic components that include: volume or level of activity, unit selling price, variable cost per unit, total fixed cost, and sales mix.
From the cost-volume-profit analysis, one can determine the sales quantity needed to break even as well as the sales quantity required to earn a desired profit margin
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For instance, given a fixed costs of $4,000 and contribution margin of $20, it becomes possible to determine the volume of sales in order for the entity to break-even (make no profit or loss). The break-even point = $4,000/$20 = 200. This implies that if the entity can sell 200 units with the current level of fixed and variable costs, and selling price, it can make no profit or loss. If more quantity is sold, then the entity can record some profit, and vice versa. Management can use the information provided to decide if production of a product or service can be continued or discontinued if it meets or does not meet the profit goal.
But, the CVP analysis is not always accurate. CVP analysis technique assumes that all costs in the company are completely fixed or completely variable. Fixed costs are costs that do not change with changes in production, such as rent or insurance costs. They are not always completely fixed as they may change periodically, then exhibiting a step fixed costs nature, though in the long run, all costs are variable.
Another issue with CVP analysis is that it is a short run, marginal analysis: it assumes that unit variable costs and unit revenues are constant, which is appropriate for small deviations from current production and sales, and assumes a neat division between fixed costs and variable costs.
Explanation:
Cost-volume-profit (CVP) analysis is a managerial accounting technique to determine how changes in costs and volume affect a company's operating income and net income.
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Little is known about how mutualistic interactions affect the distribution of species richness on broad geographic scales. Because mutualism positively affects the fitness of all species involved in the interaction, one hypothesis is that the richness of species involved should be positively correlated across their range, especially for obligate relationships. Alternatively, if mutualisms involve multiple mutualistic partners, the distribution of mutualists should not necessarily be related, and patterns in species distributions might be more strongly correlated with environmental factors. In this study, we compared the distributions of plants and vertebrate animals involved in seed‐dispersal mutualisms across the United States and Canada. We compiled geographic distributions of plants dispersed by frugivores and scatter‐hoarding animals, and compared their distribution of richness to the distribution in disperser richness. We found that the distribution of animal dispersers shows a negative relationship to the distribution of the plants that they disperse, and this is true whether the plants dispersed by frugivores or scatter‐hoarders are considered separately or combined. In fact, the mismatch in species richness between plants and the animals that disperse their seeds is dramatic, with plants species richness greatest in the in the eastern United States and the animal species richness greatest in the southwest United States. Environmental factors were corelated with the difference in the distribution of plants and their animal mutualists and likely are more important in the distribution of both plants and animals. This study is the first to describe the broad‐scale distribution of seed‐dispersing vertebrates and compare the distributions to the plants they disperse. With these data, we can now identify locations that warrant further study to understand the factors that influence the distribution of the plants and animals involved in these mutualisms.
Introduction
A central problem in ecology is to understand the patterns and processes shaping the distribution of species. There is a preponderance of studies of species richness at broad geographic scales (Hawkins et al. 2003, Rahbek et al. 2007, Stein et al. 2014, Rabosky and Hurlbert 2015) that has facilitated our understanding of why species are found where they are, a central tenet within the domain of ecology (Scheiner and Willig 2008). Most commonly, these studies find species distributions to be correlated with resource availability and use environmental variables (e.g. temperature and productivity; Rabosky and Hurlbert 2015) to explain putative determinants of the distributions. Environmental variables are only one determinant of species’ distributions. Another, species interaction, is a key and understudied determinant of species’ distributions (Cazelles et al. 2016). In fact, in some cases species interactions may be more important for determining distribution than environmental variables (Fleming 2005).
When species interact, we expect their geographic distributions to be correlated – either positively or negatively – depending on the effect (or sign of the interaction) of one species on the other (Case et al. 2005). For pairwise interactions, where one species benefits from another species, a positive relationship is expected between the distribution and abundance due to the increase in the average fitness of the benefitting species where they overlap (Svenning et al. 2014). Furthermore, most species interactions are not simply pairwise, but diffuse, consisting of multiple interacting species, here referred to as guilds (with guilds referring to species that use the same resource). It therefore follows that where one guild benefits from another guild, a positive relationship is expected between the distribution and richness of the guids. This should be true in the case of mutualisms, where both sides of the interaction share an increase in average fitness from being together (Bronstein 2015), and there is some evidence for correlated geographic distributions of mutualists in the New World (Fleming 2005). One example of a mutualism where both sides of the interaction have a fitness advantage in each other's presence is animal‐mediated seed dispersal. Because both interacting species and guilds in seed dispersal mutualism benefit from the relationship we would predict that the richness of animal‐dispersed plants ought to be correlated with the richness of their animal dispersers and vice versa. To our knowledge, this prediction has never been tested on a large geographic scale.
Answer:
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