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Hypothesis Creation Develop a hypothesis that is of interest to you! Examine relationships between one or two phenological stages (called phenophases) for a single species (or phenophases for mutualist / parasite /herbivore pairs) and abiotic variables, such as temperature. You could also correlate field observations directly with NEON's biotic data. For example, you could: test the hypothesis that increasing temperatures (NEON dataset) are correlated with delayed fall leaf color change (NPN dataset) in a deciduous tree species (e.g., red maple, sycamore) ● test the hypothesis that increasing atmospheric CO2 (NEON dataset) is related to accelerated flowering (NPN dataset) in a spring wildflower species. Fall color change of deciduous trees in the southern Appalachians; evergreens are also shown. photo courtesy of Howard Neufeld. NEON datasets that will be most useful in these analyses are listed below. air temperature (NEON.DP1.00003.001); for instance, warmer biological temperatures could trigger earlier phenological events. note that these data can be sorted by spatial and temporal variables atmospheric CO2 (NEON.DP4.00067.001); for instance, increases in CO2 levels over time could shift the timing or intensity of botanical phenomena like flowering and fruiting elevation (NEON.DP3.30024.001); for example, organisms at higher elevations might have delayed phenologies leaf area index (NEON.DP3.30012.001); leaf area index (LAI), a biotic type of data, could correlate with bud burst, such that LAI increases after bud opening photosynthetically active radiation (NEON.RP1 00066.001) herhiu

Hypothesis Creation Develop a hypothesis that is of interest to you! Examine relationships between one or two phenological stages (called phenophases) for a single species (or phenophases for mutualist / parasite /herbivore pairs) and abiotic variables, such as temperature. You could also correlate field observations directly with NEON's biotic data. For example, you could: test the hypothesis that increasing temperatures (NEON dataset) are correlated with delayed fall leaf color change (NPN dataset) in a deciduous tree species (e.g., red maple, sycamore) ● test the hypothesis that increasing atmospheric CO2 (NEON dataset) is related to accelerated flowering (NPN dataset) in a spring wildflower species. Fall color change of deciduous trees in the southern Appalachians; evergreens are also shown. photo courtesy of Howard Neufeld. NEON datasets that will be most useful in these analyses are listed below. air temperature (NEON.DP1.00003.001); for instance, warmer biological temperatures could trigger earlier phenological events. note that these data can be sorted by spatial and temporal variables atmospheric CO2 (NEON.DP4.00067.001); for instance, increases in CO2 levels over time could shift the timing or intensity of botanical phenomena like flowering and fruiting elevation (NEON.DP3.30024.001); for example, organisms at higher elevations might have delayed phenologies leaf area index (NEON.DP3.30012.001); leaf area index (LAI), a biotic type of data, could correlate with bud burst, such that LAI increases after bud opening photosynthetically active radiation (NEON.RP1 00066.001) herhiu

Biology (MindTap Course List)
Biology (MindTap Course List)
11th Edition
ISBN:9781337392938
Author:Eldra Solomon, Charles Martin, Diana W. Martin, Linda R. Berg
Publisher:Eldra Solomon, Charles Martin, Diana W. Martin, Linda R. Berg
Chapter54: Community Ecology
Section: Chapter Questions
Problem 17TYU: INTERPRET DATA Examine the top and middle graphs in Figure 54-5. Are these examples of exponential...
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Can you help me giving an example and a hypothesis about this question

● ● Hypothesis Creation Develop a hypothesis that is of interest to you! Examine relationships between one or two phenological stages (called phenophases) for a single species (or phenophases for mutualist / parasite /herbivore pairs) and abiotic variables, such as temperature. You could also correlate field observations directly with NEON's biotic data. For example, you could: • test the hypothesis that increasing temperatures (NEON dataset) are correlated with delayed fall leaf color change (NPN dataset) in a deciduous tree species (e.g., red maple, sycamore) test the hypothesis that increasing atmospheric CO2 (NEON dataset) is related to accelerated flowering (NPN dataset) in a spring wildflower species. Fall color change of deciduous trees in the southern Appalachians; evergreens are also shown. photo courtesy of Howard Neufeld. NEON datasets that will be most useful in these analyses are listed below. air temperature (NEON.DP1.00003.001); for instance, warmer biological temperatures could trigger earlier phenological events. note that these data can be sorted by spatial and temporal variables • atmospheric CO2 (NEON.DP4.00067.001); for instance, increases in CO2 levels over time could shift the timing or intensity of botanical phenomena like flowering and fruiting • elevation (NEON.DP3.30024.001); for example, organisms at higher elevations might have delayed phenologies leaf area index (NEON.DP3.30012.001); leaf area index (LAI), a biotic type of data, could correlate with bud burst, such that LAI increases after bud opening photosynthetically active radiation (NEON.DP1.00066.001); some phenological traits (in plants and herbivores especially) should track seasonal shifts in photosynthetically active radiation (al www
Transcribed Image Text:● ● Hypothesis Creation Develop a hypothesis that is of interest to you! Examine relationships between one or two phenological stages (called phenophases) for a single species (or phenophases for mutualist / parasite /herbivore pairs) and abiotic variables, such as temperature. You could also correlate field observations directly with NEON's biotic data. For example, you could: • test the hypothesis that increasing temperatures (NEON dataset) are correlated with delayed fall leaf color change (NPN dataset) in a deciduous tree species (e.g., red maple, sycamore) test the hypothesis that increasing atmospheric CO2 (NEON dataset) is related to accelerated flowering (NPN dataset) in a spring wildflower species. Fall color change of deciduous trees in the southern Appalachians; evergreens are also shown. photo courtesy of Howard Neufeld. NEON datasets that will be most useful in these analyses are listed below. air temperature (NEON.DP1.00003.001); for instance, warmer biological temperatures could trigger earlier phenological events. note that these data can be sorted by spatial and temporal variables • atmospheric CO2 (NEON.DP4.00067.001); for instance, increases in CO2 levels over time could shift the timing or intensity of botanical phenomena like flowering and fruiting • elevation (NEON.DP3.30024.001); for example, organisms at higher elevations might have delayed phenologies leaf area index (NEON.DP3.30012.001); leaf area index (LAI), a biotic type of data, could correlate with bud burst, such that LAI increases after bud opening photosynthetically active radiation (NEON.DP1.00066.001); some phenological traits (in plants and herbivores especially) should track seasonal shifts in photosynthetically active radiation (al www
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