1. Mortality as an early indicator of forest health issues. A case study using EAB. Andrew D. Hill Kirk M. Stueve Paul Sowers

2. Species specific catastrophic events are frequent. NameCommon nameIntorducedImpact Cryphonectria parasiticaChestnut blightaround 1900By 1950 few mature trees left. Ophiostoma ulmi, O. novo-ulmi, O. himal-ulmiDuch elm DiseaseFirst Reported 1928 Ongong efforts to combat disease Sirococcus clavigignenti-juglandacearumButternut cankerDiscovered 1967No trees > 21 by 2002. Discula destructivaDogwood AnthrocnoseIntroduced to US in 1978 nearly extrapated in some areas by 2000 Dendroctonus ponderosaeMountain pine beetle Endemic to Western North America160,000 Sq/Km impacted Agrilus planipennisEAB Introduced in the late 1990's 50-100 million ash trees killed. 7.5 Billion more threatened. USDA Forest Service, NRS-FIA

3. What would we gain by reducing time to detection of forest health threats?  Gain time to mitigate.  Better able to plan for coming changes.  Longer time to find a solution to the problem. USDA Forest Service, NRS-FIA

4. How have we attempted to find problems in the forest?  Walk in the woods.  Need to know what to look for.  May not yet be a problem where they do look.  Potential for missing by looking at the wrong place at the wrong time while in the field.  If the tree species is scattered then mortality might be rare enough to be ‘normal.’ USDA Forest Service, NRS-FIA

5. How have we attempted to find problems in the forest?  Remote Sensing  Cover larger area.  May have a lag in identifying diseased trees.  One must know what to look for in an image.  Systematic surveys  Are statistically sound.  May not be dense enough to detect problems early.  Must know what to look for. USDA Forest Service, NRS-FIA

6. What does a problem look like?  We usually know something is wrong because there is a larger number of dead trees than normal.  Indiscriminant death  Catastrophic fire  Widespread herbicide use  Development for other uses  Discriminant death  Epidemic effecting a limited set of trees. USDA Forest Service, NRS-FIA

7. Defining normal  Since trees don’t move they try and kill their neighbors.  We expect there to be a level of mortality as winners win and losers lose.  This will be a rate or percentage that varies by species.  Shade tolerance  Growth rate compared to neighboring trees  Fitness for the specific site compared to neighbors. USDA Forest Service, NRS-FIA

8. Using normal  If we know the normal (average) rate of mortality we should be able to:  Measure variation  Identify large deviations from average mortality rates.  Look at areas with large deviation and spot problems early. USDA Forest Service, NRS-FIA

9. EAB Example  Emerald Ash Borer is a non-native invasive that attacks ash trees.  It is fatal to the tree  Was found in Detroit, MI in 2002  Has spread through the State  Have data previous to the outbreak  MI has used traps to establish where EAB is located and tracked the dates of first detection. USDA Forest Service, NRS-FIA

10. Calculating mortality rate  We used the periodic inventories for 1980 and 1993 to establish a base line mortality rate of 2.4% for ash trees > 5 inches DBH over 5 years. USDA Forest Service, NRS-FIA

11. Data  We used 1957 plots of FIA data from the annual inventories from 2005 through 2010.  Had re-measurements  Species  DBH  Live/dead at both time 1 and 2  Plot location  MI provided dates of first detection and location of detection. USDA Forest Service, NRS-FIA

12. Data  On these 1957 plots were 11,484 ash trees.  86% of the 1957 plots with ash trees have only 1 or 2.  If we looked at only the plot level a death leads to a catastrophic estimate. USDA Forest Service, NRS-FIA

13. Methods  We developed a PYTHON script that examineded the plot and if there were < 15 trees created groups15 trees from the nearest plots with ash trees, and calculated the mortality rate fro the ploy or the group of 15 trees.  We expect that in a health forest a group of 15 trees with a mortality rate of 2.4% all the trees live most of the time. (Pr x < 14 = 4.5%). USDA Forest Service, NRS-FIA

14. Methods  Established a rule that mortality < 7.5 % be flagged.  Using GIS we used a nearest neighbor algorithm to smooth the data and create a map of ash mortality and compared this to the dates of EAB detected in the field. USDA Forest Service, NRS-FIA

15. Comparison of Early detection method vs Field measurements. USDA Forest Service, NRS-FIA

16. Results  In 2005 we detected EAB in 20 counties before it as confirmed by field crews.  In 2005 we showed 9 counties with elevated mortality rates.  The state started collecting EAB infestation data in 2003.  2005 was our first re-measurement year in MI. USDA Forest Service, NRS-FIA

17. Discussion  Given that the MI has data that begins before FIA completed the panel it seems that using increased mortality rates to detect changes in forest health may work.  May allow mitigation of future forest health epidemics.  May allow early detection of changes in forest health. USDA Forest Service, NRS-FIA

18. Questions?  Email: adhill@fs.fed.us USDA Forest Service, NRS-FIA