mortality as an early indicator of forest health issues. a case study using eab. andrew d. hill kirk...

Mortality as an early indicator of forest health issues. A case study using EAB.Andrew D. HillKirk M. StuevePaul Sowers

USDA Forest Service, NRS-FIA

Species specific catastrophic events are frequent.

Name Common name Intorduced ImpactCryphonectria parasitica Chestnut blight around 1900 By 1950 few mature trees left.

Ophiostoma ulmi, O. novo-ulmi, O. himal-ulmi Duch elm Disease First Reported 1928Ongong efforts to combat disease

Sirococcus clavigignenti-juglandacearum Butternut canker Discovered 1967 No trees > 21 by 2002.

Discula destructiva Dogwood Anthrocnose Introduced to US in 1978nearly extrapated in some areas by 2000

Dendroctonus ponderosae Mountain pine beetleEndemic to Western North America 160,000 Sq/Km impacted

Agrilus planipennis EABIntroduced in the late 1990's

50-100 million ash trees killed. 7.5 Billion more threatened.


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.


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.’


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.


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.


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.


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.


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.


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.


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.


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.


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%).


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-FIAComparison of Early detection method vs Field measurements.


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.


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.


Questions?

Email: [email protected]

mortality as an early indicator of forest health issues. a case study using eab. andrew d. hill kirk...

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