Answer:
The data in the Venn diagram suggest about the realized niches of the cattle and buffalo and their ability to coexist with evidence is discussed below in detail.
Explanation:
2.
From the graph, it is observed that Cattle and Impala have the most overlay in their nutrition.
3.
When Sorensen's index worth is more like 1, there is a contestant among animals for their food manner needs.
From the Venn diagram, we recognize that Cattle and Impala have the most overlay in their nutrition. Also, Sorensen's index worth = 0.82 (closer to 1 concerning other partners.
4.
Sorensen's index for Buffalo and Cattle is 0.48 (cheapest among these 3 partners). So, they are less competitive. So, they can persist and generate easily.
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ANSWER:</h2>
While magma rises to the surface the pressure drops and the gases start to separate out.
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EXPLANATION:</h2>
Magma is a blend of liquid or semi-liquid shake, volatiles, and solids that are found underneath the surface of the Earth. The gases are caught under in magma and after that, they r in colossal weight. As magma ascends to the surface the weight diminishes and the gases start to isolate out.
In a home, living things include microorganisms (e.g., bacteria), pets and humans, whereas non-living things may include water, viruses, etc.
<h3>What is a living thing?</h3>
A living thing is any biological system composed of cells, which are the fundamental unit of life.
Viruses do not have cells and therefore they can be considered non-living parasitic entities, whereas abiotic factors such as water are also considered non living things.
In conclusion, living things include microorganisms and humans, whereas non-living things include water.
Learn more about biotic factors here:
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"Simple" actions like answering the phone are the result of the orchestrated activity of billions of neurons sending signals to each other.
Tracing this to individual neurons is a bit like asking "how does the activity of individual transistors enable my computer to do a simple action like showing me a web-page?"
The layers of complexity that connect the individual elements to seemingly "simple" behaviors is immense. In the case of computers, we understand in precise detail how all the layers work because we designed them. In the case of the brain, there is still much to figure out. We know in considerable detail how biological cells work from the activity of individual molecules, but we still have much to learn about the brain and its relationship to the activity of neurons.