274 development, the resulting model after pruning included only DBH (Table 3). Because of this sin- gle variable and single decision, these two models had the lowest AIC value. Even though the dif- ference in the mean probability of mortality for live and dead trees for these two models was less than other successful models, the difference between means was still significant. However, this single variable model had the lowest correct prediction value, meaning it failed to correctly predict mortality in nearly a third of the assess- ment trees. Sixty percent of incorrect predictions were, again, in the trees that actually survived. percentage of correct predictions for the assessment trees (Table 3). However, this is due to the issue that the model failed to predict any mortality, with a maximum probability of mortality = 0.29. As a result of that issue, all of the trees would inherently be predicted to survive, exaggerating these model predictions. Further, because of their inability to predict mortality, the trees used to assess these models did not have significantly different probabil- ModelE and ModelF ities of mortality. Overall, these two models (ModelE and ModelF that would that would survive. Table 2. Model assessment trees that survived and died during 2009–2012 in Huron-Clinton Metroparks, Michigan, and 2011–2014 in City of Fort Wayne Parks, Indiana. Assessment tree Live Dead Testz P-value Count 81 67 DBH 18.3 (1.5) 24.2 (2.1) -2.37 0.019 Crown dieback 9.4 (1.4) 43.5 (3.3) -10.07 <0.001 Bark roughness 2 2 8.47 0.004 Vigor 1 3 75.31 <0.001 z Mean comparison with Student’s t-test for means (DBH, crown dieback = 1-tailed); Mood’s median test for bark roughness and vigor; and χ2 With signs/symptoms 42 (51.9) 61 (91.0) 26.62 <0.001 test for signs/symptoms. Table 3. Decision models developed and assessed with trees from Huron-Clinton Metroparks, Michigan, and City of Fort Wayne Parks, Indiana. Model code ModelA ModelB ModelC ModelD ModelE ModelF ModelG u u Decision criteria Percent crown diebacky Vigor ratingv , DBHx , bark splitsw , DBH, bark splits Bark splits, DBH, woodpecker activityw DBH Ten-year growth ratet Five-year standard errort DBH ModelH u Bark splits, DBH, woodpecker activityw x Diameter at breast height (cm). w Presence, absence (1,0). v Categorical 1–5 (1 healthiest, 5 poorest health). u ModelD t Mean ring basal area growth (mm2 s df = 33. , ModelG , and ModelH r Mean ring basal area standard error. included other variables as inputs but only DBH met the pruning protocol. ). , five-year growth rater Correct (%) 83.8 86.5 75.7 67.6 82.9 82.9 67.6 75.7 z Calculated from the probability of mortality for each tree at the node terminus. y Range = 5%–100%. AIC 3.15 1.30 2.85 -1.32 0.53 0.53 -1.32 2.85 Mean probability of mortality (SE)z Live 0.23 Dead 0.73 (0.03) 0.22 (0.03) 0.29 (0.03) 0.44 (0.02) 0.04 (0.02) 0.05 (0.02) 0.44 (0.02) 0.29 (0.03) (0.24) 0.76 (0.20) 0.63 (0.23) 0.58 (0.17) 0.10 (0.00) 0.00 (0.00) 0.58 (0.17) 0.63 (0.23) t(2),146 -11.04 -13.22 -7.26 -4.62 -1.14s -1.14s -4.62 -7.26 P-value < 0.001 < 0.001 < 0.001 < 0.001 0.261 0.261 < 0.001 < 0.001 ) could not differentiate between trees die and those Clark et al.: Ash Mortality Model Development returned a relatively high ©2015 International Society of Arboriculture
September 2015
| Title Name |
Pages |
Delete |
Url |
| Empty |
Ai generated response may be inaccurate.
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success.
Downloading PDF
Generating your PDF, please wait...
This process might take longer please wait
