Nature Tour Mobile App Project

Nature Tour Mobile App Project

Nature Tour Mobile App Project

The U.C. Berkeley Forest Pathology Lab has partnered with Calflora and Calfire to generate a guided tour of the Calfire Soquel Sate Demonstration Forest (Santa Cruz County) through the Nature Tour App. The App can be downloaded for free in its test version from Google Play wherever there is good cell reception. It then can be used in the forest to learn about its biodiversity, history, current management, and ongoing research activities through a self guided tour across 18 different stops. Signage with each stop number clearly visible is placed along an educational route through the forest. By tapping the number on the App that matches the number on the sign at each stop, the user will be able to play a pertinent audio description for that site. The audio can be easily interrupted, rewound, and restarted at any time. The language can be switched from English to Spanish, by changing the language in the general settings of your phone.

Example images from Nature Tour Mobile App

Soquelpicsv3

Learn more about what you will hear during the Nature Tour of Soquel Demonstration State Forest by clicking here or by going to the Soquel Demonstration State Forest

To download the App

(1) https://play.google.com/apps/testing/org.sodmap.naturetour

(2) https://play.google.com/store/apps/details?id=org.sodmap.naturetour

Learn more about our first Nature Tour by clicking here

Jepson Workshop 2018

Jepson Workshop 2018

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)


Annurev.phyto.graniti1 (cypress canker)

Download (PDF, 175KB)


AnnualReviewGarbelottoGonthier (heterobasidion)

Download (PDF, 1.29MB)

Download (PDF, 3.56MB)

 

Link to Power Point Presentations

SOD Treatments – 2015 Spring Update

SOD Treatments – 2015 Spring Update

Download (PDF, 6.09MB)

Disclaimer: Mention of any company, trade name, or commercial product does not constitute endorsement by the University of California or recommendation for use. Always follow the manufacturer’s directions, restrictions, and precautions on the product label.

 

Citizen science helps predict risk of emerging infectious disease

Citizen science helps predict risk of emerging infectious disease

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.