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
Human Anatomy & Physiology (11th Edition)
11th Edition
ISBN:9780134580999
Author:Elaine N. Marieb, Katja N. Hoehn
Publisher:Elaine N. Marieb, Katja N. Hoehn
Chapter1: The Human Body: An Orientation
Section: Chapter Questions
Problem 1RQ: The correct sequence of levels forming the structural hierarchy is A. (a) organ, organ system,...
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Can you help me giving an example and a hypothesis about this question
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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.DP1.00066.001); some phenological traits (in plants and
herbivores especially) should track seasonal shifts in photosynthetically active radiation (al
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