Buds, Leaves and Global Warming John OKeefe Harvard Forest - - PowerPoint PPT Presentation

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Buds, Leaves and Global Warming John OKeefe Harvard Forest jokeefe@fas.harvard.edu www.harvardforest.harvard.edu/schoolyard-lter-program www.harvardforest.harvard.edu/buds-leaves-global- warming

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SLIDE 1

Buds, Leaves and Global Warming

  • www.harvardforest.harvard.edu/schoolyard-lter-program
  • www.harvardforest.harvard.edu/buds-leaves-global-

warming

  • www.harvardforest.harvard.edu/autumn-foliage-color

John O’Keefe Harvard Forest

jokeefe@fas.harvard.edu

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SLIDE 2

What is phenology?

The science of the relations between climate and periodic biological phenomena (i.e. leaf emergence, flowering, leaf senescence/drop, animal migration, hibernation etc.)

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SLIDE 3

Why study phenology?

  • Data provide markers to track mass and energy

interactions between the atmosphere and biosphere.

  • Long-term data sets are records of the

biosphere’s responses to global change.

  • Individual plant observations, ‘phenocam”

images and satellite data document the timing and pattern of annual ‘green-up’ and ‘green- down’.

  • Native species and inter-regional indicator plant

(clones) observations can be used to calibrate satellite and ‘phenocam’ digital data.

  • Leafout and leaf senescence in temperate

regions influence meteorological (cloud cover/type) and hydrological (stream flow) phenomena.

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SLIDE 4

What are the main factors affecting the timing of woody species leaf phenology?

  • Spring leafout

– Cold treatment – Cumulative heat sum (growing degree days) – Day length

  • Fall leaf drop

– Temperature and frosts – Day length – Drought – Wind

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SLIDE 5

Harvard Forest Study

  • Started in 1990 (spring) and 1991 (fall, but fall 1992 not

done).

  • Originally 33 species of trees and shrubs (3-5 individuals

per species), but in 2002 decreased to 15 species in fall and 9 species in spring to reduce the time needed for the study.

  • I observe about weekly, but more often in late April-early

May and early October when events are progressing most rapidly.

  • I observe and estimate % values (leaf emergence, leaf

development, leaf color, leaf drop) over the entire tree (rather than a set number of tagged leaves/bids), which is in fact easier than labeling and counting individual leaves, but doesn’t work with younger students.

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SLIDE 6

10 20 30 40 50 60 70 80 90 100 90 100 110 120 130 140 150 % leaves emerged Day of year

% leaves emerged by tree - 4 species 2012

Series1 Series2 Series3 Series4 Series5 Series6 Series7 Series8 Series9 Series10 Series11 Series12 Series13 Series14 Series15

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SLIDE 7

10 20 30 40 50 60 70 80 90 100 110 115 120 125 130 135 140 145 % leaves emerged Day of year

% leaf emergence by tree - 4 spp 2013

RM-1 RM-2 RM-3 RM-4 RM-5 YB-1 YB-2 YB-3 WO-1 WO-2 WO-3 RO-1 RO-2 RO-3 RO-4

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SLIDE 8

10 20 30 40 50 60 70 80 90 100 120 125 130 135 140 145 150 155 160 % leaves emerged Day of year

% leaf emergence by tree - 4spp 2014

RM-1 RM-2 RM-3 RM-4 RM-5 YB-1 YB-2 YB-3 WO-1 WO-2 WO-3 RO-1 RO-2 RO-3 RO-4

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SLIDE 9

10 20 30 40 50 60 70 80 90 100 110 115 120 125 130 135 140 145 150 % leaves emerged Day of year

% leaf emergence by tree 4 spp -2015

RM-1 RM-2 RM-3 RM-4 RM-5 YB-1 YB-2 YB-3 WO-1 WO-2 WO-3 RO-1 RO-2 R0-3 RO-4

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SLIDE 10

100 105 110 115 120 125 130 135 140 145 ACRU1BB ACRU2BB ACRU3BB ACRU4BB ACRU5BB

50% budbreak date for 5 red maples 1990-2015

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SLIDE 11

100 120 140 160 180 200 220 240 260 280 300 320 DAY OF YEAR YEAR

Mean 50% bud break(BB), 75% leaf development(75) and 50% leaf fall(L50) for 4 species (Acer rubrum-ACRU n=5, Betula alleghaniensis-BEAL n=3, Quercus rubra-QURU n=4 and Q. alba- QUAL n=3)

ACRUBB BEALBB QURUBB QUALBB ACRU75 BEAL75 QURU75 QUAL75 ACRUL50 BEALL50 QURUL50 QUALL50

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SLIDE 12

Mean 50% budbreak dates for four species 1990-2015

R² = 0.0002 110 115 120 125 130 135 140 145 150 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

ACRUBB BEALBB QURUBB QUALBB Linear (ACRUBB) Linear (BEALBB) Linear (QURUBB) Linear (QUALBB)

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SLIDE 13

MEAN BB50 (4 SPP, N=15)

R2 = 0.0738 105 110 115 120 125 130 135 140 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 YEAR DAY OF YEAR MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 14

R² = 0.0745 110 115 120 125 130 135 140 DAY OF YEAR YEAR

MEAN BB50 (4 SPP, N=15)

MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 15

R² = 0.0193 110 115 120 125 130 135 140 DAY OF YEAR YEAR

MEAN BB50 (4 SPP, N=15)

MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 16

R² = 0.0154

110 115 120 125 130 135 140

DAY OF YEAR

YEAR

MEAN BB50 (4 SPP, N=15)

MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 17

MEAN LF50 (4 SPP, N=15)

R2 = 0.1496 275 280 285 290 295 300 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 YEAR DAY OF YEAR

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 18

R² = 0.0735 280 285 290 295 300 DAY OF YEAR YEAR

MEAN LF50 (4 SPP, N=15)

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 19

R² = 0.0602 280 285 290 295 300 DAY OF YEAR YEAR

MEAN LF50 (4 SPP, N=15)

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 20

R² = 0.1251 280 285 290 295 300

DAY OF YEAR

YEAR

MEAN LF50 (4 SPP, N=15)

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 21

LEAVES ON DAYS (4 SPP, N=15)

R2 = 0.1505 140 145 150 155 160 165 170 175 1991 1994 1997 2000 2003 2006 2009 2012 YEAR # OF DAYS LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)

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SLIDE 22

R² = 0.1108 145 150 155 160 165 170 175 # OF DAYS YEAR

LEAVES ON DAYS (4 SPP, N=15)

LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)

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SLIDE 23

R² = 0.0328 140 145 150 155 160 165 170 175 # OF DAYS YEAR

LEAVES ON DAYS (4 SPP, N=15)

LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)

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SLIDE 24

R² = 0.057

140 145 150 155 160 165 170 175

# OF DAYS YEAR

LEAVES ON DAYS (4 SPP, N=15) LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)

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SLIDE 25

Mean Annual Temperature at Harvard Forest Meteorological Station

Mean annual temperature has increased 0.3C per decade, though with large interannual variability, and seasons independently of annual mean

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SLIDE 26

Choosing a Site and Trees

  • Sites with a variety of native trees with branches in

easy reach of students, located in an easily monitored area, are best.

  • Trees in reach-each study tree should have two or

more branches on which students can reach and monitor 6 leaves.

  • Trees that will last-try to pick trees that will have a low

chance of being cut for maintenance or vandalized. This can be a challenge/

  • Tree variety-a variety of native tree species is best,

especially for comparing results across the region.

  • Tree branches-try to use two or more branches on

each tree (for replication), with one branch for each student research team.

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SLIDE 27

Tree ID tips

  • The first thing to look at is the arrangement of leaves,

buds and branches. Are they opposite each other or staggered alternately along the branch or stem.

  • Only a few native trees (maples, ashes, dogwoods –

MAD) have opposite leaves/branches. The rest are alternate.

  • Are the leaves simple (each leaf has a bud at the base of

its stem or petiole) or compound (the leaf stem that is attached to the woody twig next to the bud has many leaflets along it)? The ashes, hickories, walnut, butternut and sumacs are the main compound leaf species in this region.

  • Then look at leaf shape, edges and vein pattern, bud

shape and check for twig smell and bark characteristics.

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SLIDE 28

Site preparation

  • You will need one branch with 6 leaves/buds for each

student team participating in the study.

  • Label (with flagging) each tree in your study, 1 through X

( X= total number of trees) and record the species of each tree. Plan to observe at least two branches on each study tree.

  • Label (with flagging) each branch being studied on each

tree with a letter, A, B, C,…etc. So each study branch will be identified with a tree number and branch letter (i.e. 1A, 1B, 1C, 2A etc.)

  • If a branch (or tree) dies, not that unusual, try to pick

another branch on that tree and use the next letter, pick a branch on another study tree of the same species and use the next letter, or try to find another tree of that species and add it to your study with new tree and branch labels.

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SLIDE 29

Labeling leaves/buds

  • This is probably the hardest part of this study, but it is

necessary to ensure consistency in data collection. The teacher should choose and label trees and branches (6 leaves/buds per branch) before bringing students to the site.

  • Branches are labeled by tying a piece of flagging (with

the tree and branch number/letter) just behind the 6 study leaves/buds on the branch.

  • When choosing and labeling leaves/buds do not use the

terminal/tip leaf/bud, but start counting at the next leaf from the tip as #1, then the next as #2, etc. On opposite leaved trees #1 and #2 will be paired across from each

  • ther. If there is a side branch on your main branch

before you reach #6, use the tip bud on the side branch as the next # and continue using buds down the side branch until you reach #6 or, if necessary, return to leaves on the main branch. Note, you do use the tip bud

  • n side branches, just not on the main branch.
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SLIDE 30

Spring Data Collection

  • Typically start data collection in late March, but this may vary

depending on March weather. I strongly recommend bringing shoots of study species inside in mid-March to force.

  • Try to collect data once a week.
  • Each student team will observe the 6 live buds closest to the

branch tip (skipping the terminal bud if there is one) that have been previously labeled.

  • On the first visit it is a good idea to have them sketch the

branch and study buds and bring the sketch on later visits to help identify the study buds.

  • They will record how many buds have recognizable leaves,

not just leaf tips, emerged from them.

  • Once leaves have emerged, record the length (not including

stem) of the largest leaf.

  • The teacher will combine all data for each tree and submit to

Harvard Forest.

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SLIDE 31

Field Notes/Observations

  • These notes are optional and not

submitted, but represent the type of

  • bservations scientists make when they

are collecting their data.

  • Typical observations might include

temperature, cloud cover, precipitation, wildlife observations, any unusual conditions or recent events/changes such as a strong windstorm or frost/freeze.

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SLIDE 32
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SLIDE 33
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SLIDE 34
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SLIDE 35
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SLIDE 36
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SLIDE 37
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SLIDE 38
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SLIDE 39
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SLIDE 40
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SLIDE 41

Buds, Leaves and Global Warming

  • www.harvardforest.harvard.edu/schoolyard-lter-program
  • www.harvardforest.harvard.edu/buds-leaves-global-

warming

  • www.harvardforest.harvard.edu/autumn-foliage-color

John O’Keefe Harvard Forest

jokeefe@fas.harvard.edu

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SLIDE 42
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SLIDE 43

Why study phenology?

  • Long-term data sets are records of the

biosphere’s responses to global change.

  • Data provide markers to track mass and energy

interactions between the atmosphere and biosphere.

  • Native species and inter-regional indicator plant

(clones) observations can be used to calibrate satellite and ‘phenocam’ data.

  • Leafout and leaf senescence in temperate

regions influence meteorological phenomena.

slide-44
SLIDE 44

Harvard Forest Study

  • Started in 1990 (spring) and 1991 (fall, but fall 1992 not

done)

  • Originally 33 species (3-5 individuals per species, but in

2002 decreased to 15 species in fall and 9 species in spring to reduce time needed for the study

  • I observe about weekly, but more often in late April-early

May and early October when events are progressing most rapidly

  • I observe and estimate % values (leaf emergence, leaf

development, leaf color, leaf drop) over the entire tree (rather than a set number of tagged leaves/bids), which is in fact easier but doesn’t work with younger students

slide-45
SLIDE 45

Mean 50% bud break(BB), 75% leaf development(75) and 50% leaf fall(L50) for 4 species (Acer rubrum-ACRU n=5, Betula alleghaniensis-BEAL n=3, Quercus rubra- QURU n=4 and Q. alba-QUAL n=3)

100 120 140 160 180 200 220 240 260 280 300 320 1990 1993 1996 1999 2002 2005 2008 2011 YEAR DAY OF YEAR ACRUBB BEALBB QURUBB QUALBB ACRU75 BEAL75 QURU75 QUAL75 ACRUL50 BEALL50 QURUL50 QUALL50

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SLIDE 46
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SLIDE 47

MEAN BB50 (4 SPP, N=15)

R2 = 0.0032 110 115 120 125 130 135 140 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 YEAR DAY OF YEAR MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 48

MEAN BB50 (4 SPP, N=15)

R2 = 0.0557 105 110 115 120 125 130 135 140 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 YEAR DAY OF YEAR MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 49

MEAN BB50 (4 SPP, N=15)

R2 = 0.0377 105 110 115 120 125 130 135 140 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 YEAR DAY OF YEAR MEANBB Linear (MEANBB) Linear (MEANBB)

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SLIDE 50

MEAN LF50 (4 SPP, N=15)

R2 = 0.2388 275 280 285 290 295 300 1 9 9 1 1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9 2 1 2 3 2 5 2 7 2 9 YEAR DAY OF YEAR

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 51

MEAN LF50 (4 SPP, N=15)

R2 = 0.1984 275 280 285 290 295 300 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 YEAR DAY OF YEAR

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 52

MEAN LF50 (4 SPP, N=15)

R2 = 0.2268 275 280 285 290 295 300 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 YEAR DAY OF YEAR

MEANLF50 Linear (MEANLF50) Linear (MEANLF50)

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SLIDE 53

LEAVES ON DAYS (4 SPP, N=15)

R2 = 0.0584 140 145 150 155 160 165 170 175 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 YEAR # OF DAYS LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)

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SLIDE 54

LEAVES ON DAYS (4 SPP, N=15)

R2 = 0.1403 140 145 150 155 160 165 170 175 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 YEAR # OF DAYS LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)

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SLIDE 55

LEAVES ON DAYS (4 SPP, N=15)

R2 = 0.1264 140 145 150 155 160 165 170 175 1 9 9 1 1 9 9 4 1 9 9 7 2 2 3 2 6 2 9 YEAR # OF DAYS LEAVES ON DAYS Linear (LEAVES ON DAYS) Linear (LEAVES ON DAYS)