Answer:
limiting factor
Explanation:
In an ecosystem, certain factors hinder the growth or abundance of the population of organisms. These factors are called LIMITING FACTORS. Limiting factors, which can be biotic, food, drought, predation, disease etc, are factors that inhibits a population of organism from becoming enlarged. There are two types of limiting factors viz: density dependent (depends on size) and density independent (not dependent on size).
This is the case of a drought that dries up the stream that inhabits a population of salamanders. The drought causes the unavailability of water, which will ultimately have an effect on the growth of the salamander population. Hence, the DROUGHT is considered a LIMITING FACTOR.
Explanation:
<h2>It is interesting to note that CO2 is still believed to be the No 1 greenhouse gas instead of water vapour. Many excellent climate scientist (e.g. Richard Lindzen, Roy Spencer, John Christy, etc) have dealt with the issue and shown both in books and research articles that CO2 is a very minor player governing global climate.</h2><h2>So what drives climate?</h2><h2>The answer must obviously be found in the hydrological cycle, where the oceans play a major role together with extraterrestrial process with the Sun having the ultimate role. We know that solar energy (insolation) does not vary sufficiently to explain the climatic excursion our planet has experienced on a short and long term. It is sufficient to consider the Little Ice Age and the Medieval Warm Period, not mentioning the past ice ages, to understand that there are many complicated factors to consider before we can explain climate variability.</h2><h2>Solar activity is naturally a major player but this does not mean only total solar insolation (TSI) but also solar magnetic activity. Also the gravitational influence of the entire solar system must be taken in account, not forgetting our own natural satellite, the Moon, influencing at least ocean tides. Very interesting views on climate variability and cosmic activity have been presented by Henrik Svensmark.</h2><h2>A very simplistic example how the water cycle could adjust climate is the following mental construct: The Sun warms the ocean surface increasing evaporation. Increase in water vapour content decreases the density of the air, which thus rises to higher altitudes where eventually adiabatic cooling reaches a level where water vapour starts to condense. The availability of condensation nuclei, possibly enhanced by high energy cosmic radiation especially during low level solar magnetic activity, leads to strong cloud formation. This eventually limits solar warming of the ocean surface and decreases evaporation with less cloud formation. This entire cycle can be compared to a very effective thermostat, by some aptly termed the water thermostat responsible for keeping global temperatures at a suitable level depending on local conditions</h2>
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Answer:
The grazing food chain
Explanation:
There are two kinds of food chains one starting with autotrophs, the grazing food chain, and the other starting with dead organic matter, the detrital food chain.
Answer:
D
Explanation:
all living things are made up of many cells
Imagine you are surveying a population of a mountain range where the inhabitants live in the valleys with no inhabitants on the large mountains between. If your sample area is the valleys, and you use this to estimate the population across the entire mountain range, <u>you overestimate the actual population size</u>
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Explanation:
- An estimate that turns out to be incorrect will be an overestimate if the estimate exceeded the actual result, and an underestimate if the estimate fell short of the actual result.
- The mean of the sampling distribution of a statistic is sometimes referred to as the expected value of the statistic. Therefore the sample mean is an unbiased estimate of μ.
- Any given sample mean may underestimate or overestimate μ, but there is no systematic tendency for sample means to either under or overestimate μ.
- Bias is the tendency of a statistic to overestimate or underestimate a parameter. Bias can seep into your results for a slew of reasons including sampling or measurement errors, or unrepresentative samples
