Author: Doug Schmidt

Emergent forest diseases threatening California Ecosystems

The course is organized in eight parts:

1. Introduction to tree diseases and their causal agents
2. Ecology of forest diseases, differences between native and non-native diseases
3. Principles of Biological Invasions and diseases caused by off-site hosts or by forestry practices
4. Emergent diseases in California caused by exotic fungi
5. Emergent diseases in California caused by exotic oomycetes
6. Newest threats

Readings and resources:

The following readings are general on the theory of emergent diseases:

Desprez2007
Fisher2012
Garbelotto2008phytomed
Haydenetal2012
Santini2012

The following readings are specific to the diseases covered in the workshop:

WhitepinesRibesandBlisterRust (white pine blister rust)

Download (PDF, 1MB)

PitchcankerReviewWingfield (pine pitch canker)

Download (PDF, 1.02MB)

Eukaryotic Cell (sudden oak death)

Download (PDF, 938KB)

Dutchelmbrasier2001 (dutch elm disease)

Download (PDF, 570KB)

Download (PDF, 175KB)

Download (PDF, 1.29MB)

Download (PDF, 3.56MB)

Link to Power Point Presentations

UC Berkeley’s 2nd Annual Big Give! https://biggive.berkeley.edu/ #CalBigGive #seethebiggerpicture

Audio segment from KAZU news 5/22/15.

Engaging citizen scientists is becoming an increasingly popular technique for collecting large amounts of ecological
data while also creating an avenue for outreach and public support for research. Here we describe a unique
study, in which citizen scientists played a key role in the spatial prediction of an emerging infectious disease. The
yearly citizen-science program called “Sudden Oak Death (SOD) Blitz” engages and educates volunteers in detecting
the causal pathogen during peak windows of seasonal disease expression. We used these data – many of
which were collected from under-sampled urban ecosystems – to develop predictive maps of disease risk and to
inform stakeholders on where they should prioritize management efforts. We found that continuing the SOD
Blitz program over 6 consecutive years improved our understanding of disease dynamics and increased the accuracy
of our predictive models. We also found that self-identified non-professionals were just as capable of detecting
the disease as were professionals. Our results indicate that using long-term citizen-science data to predict the
risk of emerging infectious plant diseases in urban ecosystems holds substantial promise.