The Ecological Hierarchy In Order From Largest To Smallest Is
Ecosystems are organized to better understand the frame of reference in which they are being studied. They are organized from smallest to largest; organism, population, community, ecosystem. An organism is a single living thing, a population is all of the organisms of the same species in the same place at the same time, a community is all populations in the same place at the same time (all living things), and an ecosystem is the reactions between living and nonliving components in a given area. Ecosystems can vary in size depending on the scope of study.
the ecological hierarchy in order from largest to smallest is
Living things are highly organized and structured, following a hierarchy that can be examined on a scale from small to large. The atom is the smallest and most fundamental unit of matter. It consists of a nucleus surrounded by electrons. Atoms form molecules. A molecule is a chemical structure consisting of at least two atoms held together by one or more chemical bonds. Many molecules that are biologically important are macromolecules, large molecules that are typically formed by polymerization (a polymer is a large molecule that is made by combining smaller units called monomers, which are simpler than macromolecules). An example of a macromolecule is deoxyribonucleic acid (DNA) (Figure 1), which contains the instructions for the structure and functioning of all living organisms.
Biological organisation is the hierarchy of complex biological structures and systems that define life using a reductionistic approach.[1] The traditional hierarchy, as detailed below, extends from atoms to biospheres. The higher levels of this scheme are often referred to as an ecological organisation concept, or as the field, hierarchical ecology.
A system exists independently from his components and is generally self-organizing, so that it can be considered a cybernetic organization. Landscape has an organized complexity, and in order to understand a system it is necessary to focus on the level of organization. In fact, considering the complexity of a system it is important to select the best spa-
Distributions of extant organismal size within each of the vertical levels. Coloured shaded areas highlight the total size range occupied by living genera within a given level. As the hierarchy is ascended from viruses to multicellular eukaryotes, the modal size as well as the total range in size increases. (Online version in colour.)
Our results are most consistent with the hypothesis that minimum size constraints arise from physical factors while constraints on maximum size arise from physiological factors [30,31]. This is almost certainly the case for prokaryotes, where the smallest species have just enough cell volume to contain the necessary genome, ribosomes and proteins to function as prokaryotes, whereas the largest species are limited by diffusion of materials across their surface area [15]. The largest known prokaryote, Thiomargarita namibiensis Schulz and others 1999, is approximately eight orders of magnitude larger in biovolume than the modal prokaryote. These extremely large prokaryotes have large vacuoles that occupy up to 98% of the cell's volume [32], thus limiting the metabolically active portion of the cell to a thin outer shell and shortening the distance over which materials must diffuse into and out of the cell. The extreme sizes of protists are likely to be limited by the same factors: a small cell must be large enough to contain all its necessary parts, while at large size transporting materials into, out of and within a cell becomes limiting. Protists, like all eukaryotes, use cytoplasmic streaming to facilitate intracellular transport. Animals appear to follow a similar pattern whereby the smallest animals, the parasitic myxozoans, are composed of only a few cells and possess highly reduced genomes [33]. The sizes of some of the largest multicellular eukaryotes, the baleen whales, are potentially constrained by a number of physiological factors such as thermoregulation [34] and the ability to acquire enough food [35,36].
Simple scaling calculations illustrate the metabolic challenges of being very large. Consider time scales of O2 transport within a cell by both diffusion and cytoplasmic streaming, where the former scales with the square of length while the latter scales with length ([37] and references therein). It would take O2 approximately 104 s to diffuse through a spherical prokaryote that is 1 mm in diameter. In a same-sized protist, mixing via cytoplasmic streaming at a typical streaming rate would take approximately 102 s. Scaling up the sizes of these hypothetical organisms two orders of magnitude to a diameter of 10 cm, the diffusion and mixing times would be approximately 107 and 104 s, respectively. These times for intracellular O2 transport do not preclude the evolution of centimetre-scale bacteria or metre-scale protists, but such organisms would necessarily have extremely low metabolic rates. Intra-organism transport calculations are not so simple for multicellular organisms, which have evolved respiratory organs and circulatory systems. Nevertheless, there are metabolic constraints to being very large. For example, the energetic cost of lunge feeding increases with size such that whales larger than the largest known blue whale would require too much recovery time after each feeding lunge to meet the total metabolic demand [36].
Our data on the total size ranges occupied at each level of complexity are not consistent with either of these predictions. The two oldest groups considered here are the viruses and prokaryotes. The age of the first virus is not known because viruses lack a fossil record, but given that they are obligate parasites on cellular organisms, the simplest assumption is that they postdate the prokaryotes, which originated approximately 3.8 Ga. The fossil record shows that solitary eukaryotes first appeared approximately 1.9 Ga, followed by metazoans at or shortly before 0.6 Ga. Given this evolutionary sequence, we would expect prokaryotes and viruses to occupy the largest range in size, followed by protists and then multicellular eukaryotes (figure 2; electronic supplementary material, figure S1). This prediction is not borne out by the data. The smallest range in size is occupied by the viruses, followed by prokaryotes, protists and multicellular eukaryotes.
Cluster analysis includes two classes of techniques designed to find groups of similar items within a data set. Partitioning methods divide the data set into a number of groups pre-designated by the user. Hierarchical cluster methods produce a hierarchy of clusters, ranging from small clusters of very similar items to larger clusters of increasingly dissimilar items. This lecture will focus on hierarchical methods.
Transformations may be needed to put samples and variables on comparable scales; otherwise, clustering may reflect sample size or be dominated by variables with large values. For example, ecological count data are commonly percent-transformed within rows to remove the effect of sample size. For ecological data and other data, it is common to perform a percent-maximum transformation within columns to prevent a single large variable from overwhelming important variations in numerically small variables.
This example will show how to apply cluster analysis to ecological data to identify groups of collections that have similar sets of species in similar proportions. The data consist of counts of 38 taxa in 127 samples of marine fossils from the Late Ordovician of the Cincinnati, Ohio region. Notice that most of the values are zero: samples typically contain only a few taxa, although many different taxa are present in the entire data set.
Because this data is based on counts of fossils where the number of specimens differs greatly among the samples (the smallest sample has 15 specimens and the largest has 295), we want the cluster analysis to reflect differences in the relative proportions of species, not sample size, so all abundances should be converted to percentages. To do this, we perform a percent transformation on rows, sometimes called standardization by totals. In addition, some taxa are very abundant and others are rare. Rather than have the cluster analysis dominated by the most abundant taxa, we follow the percent transformation with a percent maximum transformation on the columns, which will convert every value to a percent of the maximum abundance for each taxon. In this way, all values will range from zero (taxon is absent) to one (taxon is at its most abundant). The vegan package has a helpful set of transformation in the decostand() function that can perform both transformations easily. The .t1 and .t2 notation indicates transformation 1 and transformation 2.
It is useful to understand why clusters group together, that is, it is useful to summarize the composition in each cluster. For ecological data, we could add up the abundance of each taxon in that cluster, re-express them as percentages, and sort them from the lowest to the highest. The first step in this process is to find the samples that mark the endpoints of each cluster. From our dendrogram, we can see that sample 2D001 is the left-most sample in cluster 1 and 2D046 is the rightmost. For cluster two, these endpoints are 2D004 and 2D069, and for cluster three, they are 2D003 and 2S032. The order object in agnes is a vector that gives the order of our original row numbers (in fossilsT2Bray, which is the same order as fossilsT2. and fossils) on the dendrogram. We need to find the set of original row numbers in each of the clusters, and that can be done with the which() command.
Cluster analysis can also be applied to non-ecological data to find groups of similar samples. In this example, it is applied to the Nashville carbonates geochemistry data, which consists of geochemical measurements on limestone. The geochemical measurements are extracted from the data frame, and a log+1 transformation is applied to the right-skewed major elements.