Alpha Diversity Metrics
Alpha Diversity Metrics - Related Videos
13:04
Community Diversity - Student Protocol
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2019
Measuring Biodiversity in Areas with Different Disturbance Regimes ExpandNOTE: In this experiment, you will compare the biodiversity in a less disturbed area against a more highly disturbed area. HYPOTHESES: The experimental hypothesis could be that a less disturbed area will contain a greater diversity of species than a highly disturbed area. The null hypothesis could be that there will be no difference in diversity between less disturbed and highly disturbed areas. To begin, divide into...
05:49
Community Diversity - Concepts
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2019
Populations do not live in isolation; thus, every population interacts with others in certain ways. These interactions give rise to a network of populations. Hence, an ecological community is composed of such population networks of various species interacting with each other within the same area. These biological, or biotic, components may also closely interact with non-living, or abiotic, components, forming an ecosystem. Ecosystems may be as small as the microbial communities inside the human...
01:09
Community Diversity - Instructor Prep
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2019
Setting up for the Lab and Choosing Field Sites ExpandBefore the experiment, first ensure that the student computers have Microsoft Excel or Google Sheets installed. If using Excel, make sure the data analysis tool pack is loaded. Next, you will need to identify the two fields where the students will take measurements. Both fields should ideally have lengths in any direction of at least 30 meters. One field should be a shrubby or weedy field that has not been mowed recently. The second field...
06:40
Plant Diversity - Concepts
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2019
From Water to Land Kingdom Plantae first appeared about 410 million years ago as green algae transitioned from water to land. Though challenging, this transition benefited early colonizers in several ways. Initially, most living organisms (including plants and animals) were ocean dwelling, making aquatic environments crowded and highly competitive. In contrast, land was a relatively uncolonized environment with ample resources and little to no predators or competitors. Terrestrial environments...
06:08
Animal Diversity - Concepts
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2019
Kingdom Animalia is composed of a range of organisms united by a set of common characteristics. Barring a few exceptions, animals are multicellular eukaryotes that move, consume organic matter, and reproduce sexually. Although these attributes are shared, species within this kingdom are also extremely diverse. This diversity is due to adaptation of each species to a different niche. The niche of a species includes the area, function, and interrelationship of that species with other biotic and...
FAQs
The metrics apply different weighting schemes to community structure. Observed richness counts detected taxa, whereas Chao1 gives greater attention to rare taxa when estimating richness. Shannon incorporates richness and the distribution of abundances, while Simpson places greater emphasis on common taxa and evenness. Selecting among them changes which features of a microbial community most influence the reported result.
Observed richness reflects the number of taxa detected in a sample, while Chao1 is designed to estimate richness with greater consideration of rare taxa. Consequently, communities containing many low-abundance taxa may be characterized differently by these measures. Comparing both can help distinguish the directly observed taxon count from a richness estimate influenced by rare members.
Shannon and Simpson provide information about how abundances are distributed across taxa, not only how many taxa occur. Shannon combines richness with abundance distribution, whereas Simpson emphasizes common taxa and evenness. Two samples can therefore have similar taxon counts but different index values if one community is more evenly distributed or dominated by a smaller number of common taxa.
Researchers derive these metrics from sequencing data for each biological sample, then compare the resulting values across defined patient groups, disease states, or treatment groups. The comparison can use a selected index or several indices together, allowing the analysis to examine observed richness, estimated richness, abundance distribution, and evenness rather than relying on a single description of community structure.
Cancer studies can apply these measures to microbial communities associated with tumors, the gut, or other relevant biological sites. Evaluating different sites helps characterize community structure in the context of the disease setting under investigation. The resulting comparisons may support analyses of how microbial diversity varies among patients, disease states, or treatment groups.
Differences in within-sample microbial diversity can be examined alongside therapeutic response, cancer progression, or factors associated with disease state. Comparing patient or treatment groups may reveal patterns in richness, abundance distribution, or evenness that are relevant to these outcomes. Such results support characterization of tumor-associated and other cancer-relevant microbiomes within broader research analyses.