Management of Biological Invasions (2015) Volume 6, Issue 3: 231–241
doi: http://dx.doi.org/10.3391/mbi.2015.6.3.02© 2015 The Author(s). Journal compilation © 2015 REABIC

Open Access

Management in PracticeThe San Francisco Bay Area Early Detection Network
Mark Frey1*, Mike Perlmutter2, Andrea Williams3 and Dan Gluesenkamp 41

National Park Service, 4598 MacArthur Blvd, NW, Washington, DC 20007, USA
City of Oakland, 250 Frank H. Ogawa Plaza, Suite 5301, Oakland, CA 94612, USA
3
Marin Municipal Water District, 220 Nellen Ave., Corte Madera, CA 94925, USA
4
California Native Plant Society, 2707 K Street, Suite 1, Sacramento, CA 95816-5113, USA
2E-mail: mark_frey@nps.gov (MF), mperlmutter@oaklandnet.com (MP), awilliams@marinwater.org (AW), dgluesenkamp@cnps.org (DG)

*Corresponding author
Received: 26 June 2014 / Accepted: 18 November 2014 / Published online: 13 January 2015
Handling editor: Catherine de Rivera

Abstract
This paper summarizes the strategies of the San Francisco Bay Area Early Detection Network (BAEDN). BAEDN coordinates andimplements Early Detection of and Rapid Response to infestations of target invasive plants throughout the nine county San Francisco Bay
Area. Limited-distribution invasive plant species in the Bay Area were identified by analyzing occurrence records within the Calflora
database. Target species were then prioritized according to whether or not they were documented as invasive and by expert opinion. Weed
Heuristics: the Invasive Population Prioritization for Eradication Tool was used to prioritize the 272 known occurrences of the 73 target
priority species based on relative impact, invasiveness, and feasibility of treatment. BAEDN coordinated two years of removal of these
populations. Lessons learned and next steps are reviewed. As new populations and species are identified additional funds are needed to carryout coordinated evaluation, prioritization, and control efforts.
Key words: rapid response, regional, San Francisco, California, prioritization, WHIPPET

The need for BAEDN
The need for early detection and rapid response
Carried out at a regional scale, the benefits ofearly detection and rapid response (EDRR) at
one site contribute both site-specific and regional
benefits, as the threat of spread by new invasives
to new sites is pre-empted. Early detection is theprocess of identifying new populations of known
or potentially invasive species. Rapid response isaction taken to control infestations of species
with limited distributions. Control of early infestations is a critical component of any successful
invasive species management strategy (Mehta etal. 2007; Moody and Mack 1988). Treatment ofinvasive plant infestations when they are small
greatly increases treatment efficacy and saves
money (Cusack et al. 2009). In addition, early
detection reduces ecosystem damage, treatment
impacts (physical, biological, and chemical), andfinancial costs. EDRR represents an incentive forcoordination, information sharing, and partnership
across jurisdictional boundaries to maintaincommon goals.
The need for a San Francisco Bay Area solution
The San Francisco Bay Area comprises the ninecounties bordering San Francisco Bay in Northern
California: Alameda, Contra Costa, Marin, Napa,
San Francisco, San Mateo, Santa Clara, Solano,
and Sonoma (Figure 1). The region includes thecities of San Francisco, Oakland, and San Jose.
The area exhibits a Mediterranean climate acrossits 17,885.5 km2 (US Census Bureau 2013) andincludes a wide variety of plant communities,
elevations, and microclimates. The area includes
portions of the Coast Range, Central California
Foothills and Coastal Mountains, and Central
California Valley Level III ecoregions (EPA2013). The metropolitan centers of San Francisco,
Oakland and San Jose are forecast to have a
population of 8 million by 2020 (ABAG 2002).
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M. Frey et al.

Figure 1. The San Francisco Bay
Area region and the position of thestudy area on North America.

The San Francisco Bay Area lies at the heart of
the California Floristic Province, one of the world’s
biodiversity hotspots (Myers et al. 2000). Invasive
species are threatening biodiversity in California(Bossard et al. 2000). The area is also a major
hub of international trade, potentially increasing
the rate of new species introductions.
The threats posed by invasive species areprojected to increase under the altered environmental
conditions associated with predicted climatechange scenarios (IPCC 2014). Higher wintertemperatures, longer and warmer growing seasons,and more frequent abnormal disturbance events(e.g., drought, storm events, atypical runoff timing)
may all favor invasive species. Such conditions
are predicted to be less favorable to native species
adapted to local conditions, while simultaneously
being more favorable to pest species (IPCC 2014).
Acting on the problem early represents the best
opportunity to protect rare ecosystems.
Recognizing the extraordinary significanceand exposure to threats in the region, the United
Nations Educational, Scientific and Cultural
Organization (UNESCO) Man and the Biosphere(MAB) program designated the Central California
International Biosphere Reserve in 1988.
What others have done
Early detection is a core function of bordersecurity at the national or island level. Bordersecurity efforts are essential at stopping manypests from a wide range of taxa. Many border
232

efforts are conducted by national governments
although there are some island border programs
that are run by non-governments. For example,
islands off the coast of California (USA) havebeen the subject of a Biosecurity Plan (California
Islands Biosecurity Program Subcommittee 2013).
Partnerships are an important part of successful
Invasive Plant Management (Higgins et al. 2007).
Many states and regions in the United States havepartnerships in the form of Cooperative Weed
Management Areas (CWMA) – organizations
that bring together public, private, and tribal
stakeholders to combat invasive plants. These
CWMAs are particularly common in rural areas
where they often spend time and money controlling
species that are agricultural pests. In recent years
some groups have modified the CWMA model,
increased their focus in non-agricultural weeds,
and called themselves Cooperative Invasive Species
Management Areas. In addition, there are many
Exotic Pest Plant Councils (EPPC) or Invasive
Plant Councils (IPC) that work collaboratively toraise awareness of and control invasive plants.
The National Association of Exotic Pest Plant
Councils is an umbrella group for all of theEPPCs and IPCs (NAEPPC 2014). Each of theseorganizations has its own priorities but mostinclude early detection and rapid response.
In the United States there are only a handfulof regional organizations that expressly focus on
the early detection of invasive species including
the Great Lakes Early Detection Network

The San Francisco Bay Area Early Detection Network(GLEDN; Crall et al. 2012), Mid Atlantic Early
Detection Network (MAEDN; MAEDN 2014),
Southeast Early Detection Network (SEEDN;SEEDN 2014), and the Bay Area Early Detection
Network (BAEDN). Outside of the United States
there appear to be fewer collaborative early
detection/rapid response organizations but a larger
commitment from government entities. For
example, South Africa has a program funded by
the national government (Wilson 2013). A plantfocused early detection program has been set up
on a regional scale in Saxony-Anhalt, Germany(Schneider 2012).
BAEDN overview
The Bay Area Early Detection Network (BAEDN)
was initiated in December 2006 to apply theprinciples of EDRR at one of the world’s
biodiversity hotspots. Professionals met for afull-day workshop at which partners presented
updates on current early detection efforts andneeds, shared information regarding existing
networks, and discussed research and technicalinnovations available to support such efforts.
Programs with EDRR components that werediscussed included the National Park Service’s
San Francisco Area Network Inventory and Monitoring Program (Williams and Speith 2008), theCalifornia Department of Food and Agriculture’s
California District Biologists (Schoenig 2005),
the Invasive Spartina Project, The California
Invasive Plant Council, and the US Fish andWildlife Service’s efforts at the San Pablo Bay
National Wildlife Refuge (USFWS 2011). Participant’s discussions centered on refining the vision,
identifying additional partners, defining the
project scope, and setting strategies for building
a robust and efficient Early Detection Network.
Key principles for an effective Early Detection
Network were defined. The new network must:
1) Be coordinated by paid staff (volunteers andexisting professional capacity are insufficient).
2) Include all counties adjoining the San Francisco
Bay.
3) Include all major stakeholders from the service
region.
4) Provide stakeholders with the opportunity to help
structure the network.
5) Apply science-based techniques in a rigorous andtransparent manner.
6) Adapt existing tools rather than create new tools.
7) Share the techniques used to prioritize populations
and occurrences.

Guided by these principles, the BAEDN
partnership was formalized by a Memorandum ofUnderstanding (MOU) which brought together
partners from the San Francisco Bay area to“work cooperatively to develop and implement
an ecological and integrated approach to the early
detection of harmful and noxious weeds, and therapid response to these environmental impairments”.
The MOU provided a framework for partners tocollaboratively and voluntarily “identify priorities
and obtain funding to facilitate effective weed
control,” “share scientific and technical expertise(including monitoring, results of researchfindings, weed ecology and biology information,
GIS and other mapping capabilities, andintegrated pest management methods),” and meetto coordinate BAEDN priorities.
In 2007 BAEDN began developing financial
support; contributed funds supported staff, contractors,
and operating costs. Funds were administered bya non-profit partner that served as BAEDN’s
fiscal sponsor. Having a fiscal sponsor removed
the administrative burden of achieving non-profit
status for BAEDN itself.
BAEDN was publicly launched during California’s
Invasive Weed Awareness Week, July 2009. The
launch included targeted outreach through articles
in numerous agency and organization newsletters.
This campaign sought to inform and involve all
key stakeholders in early formation of the
network. New partners either signed the MOU or
established less formal agreements to collaborate
with BAEDN. The partner list grew from about
one dozen organizations in 2007 to over one
hundred in 2010. The public launch both brought
together professionals to guide the work of theorganization and raised BAEDN’s profile among
Bay Area land managers.
Major funding for BAEDN ended in 2011. In
2013 BAEDN became a project of the California
Invasive Plant Council. The shift from dedicatedfunding source to a project of a larger organization has
brought about a significant slowdown in the paceof activity. However, it has also increased accessto the fund-raising, outreach, and administrative
capacity of a well-established and very active
organization focused on invasive plants in
California. The early infusion of funds jumpstarted an enduring project. BAEDN strives toachieve long-term control of early detection targets.
As new populations and species are identified
additional funds are needed to carry out coordinatedevaluation, prioritization, and control efforts.
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M. Frey et al.

Data collection
Calflora
Calflora’s online tools and data were essential to
BAEDN’s success. Calflora is a non-profit
organization that runs a web-accessible databaseproviding information about plants that growwild in California. Information is derived fromdata submitted by public agencies, non-profits,scientists, and private citizens. The website
received 364,094 unique visitors in 2012 andincludes information on almost 12,000 planttaxa. Because most land managers in the San
Francisco Bay Area were already familiar withthe database both as a repository and a source of
information it was relatively easy to expand theuse of the database and request additional data.
Because Calflora was well-established and wellused, land managers did not need to provide data
to a new source and they already had theexperience in the database being useful.
Data collection tools
To facilitate the use and collection of occurrencedata, BAEDN worked with Calflora to develop
the BAEDN Occurrence Reporting Database in2009. The BAEDN database is an extension of
Calflora’s database and is a shared repository for
new and existing plant occurrence data collectedby agencies and the public. BAEDN worked with
Calflora to develop additional tools for data
entry and extraction: an upload tool for geotagged
photos, a smart phone application for effective
and easy field mapping, a “My Observations”
portal to allow users to edit and manage theiroccurrence reports, and Web applications for
uploading and downloading larger datasets in avariety of formats. With these tools, BAEDN andpartners worked to consolidate previously unshared
mapping data into a single shared database.
Calflora adapted code from What's Invasive(http://www.whatsinvasive.org/index.html) to create the
Calflora Observer smartphone app for fielddocumenting plant occurrences with location andphotographs. This app can run from any GPSenabled smartphone even without a phone planor signal. All of the activity resulted in morerecords entered into the online database, enhancing
its completeness and utility for early detection.
Since the launch of the online and mobile
BAEDN tools in the summer of 2008 until May
21, 2014, Calflora has logged 10,183 entries
from BAEDN tools (Powell, pers. comm. May
21, 2014). Providing a range of tools all funneling234

data to Calflora increased participation because
land managers understood how their contributions
helped their own efforts and regional efforts totrack and control invasive species.
Creating a list of target species
Compilation of candidate species
Prioritization has been recognized as an importantcomponent of invasive species management at
the international policy level (COP 10 2010). To
create a list of prioritized species the first task
was to compile a comprehensive list of candidate
species with the potential to be invasive in
California. Consulted sources of plant species
and traits included: the Calflora database of nonnative plant occurrences (Calflora 2010), California
Invasive Plant Council (Cal-IPC) Invasive Plant
Inventory (Cal-IPC 2008), California Invasive
Plant Council Mediterranean weed list (Brusati
et al. 2014), California Invasive Species Council
Species Scorecards (CISAC 2010), United Statesfederal noxious weed list (USDA 2010a), statenoxious weed lists for all available states (e.g.,
AZDA 2010, CDFA 2010, NDA 2010, NWCB 2010,
and ODA 2010) a published list of California
non-native plant species (Shierenbeck et al. 2007),
Presidio of San Francisco, prohibited plant list
(Presidio Trust, unpublished data), Weed Risk
Assessments from Florida (Gordon et al. 2008),
Hawaii (Daehler et al. 2004), and Queensland(unpublished by Csurhes, 2010), and the World
Wildlife Fund’s Australian National list ofnaturalized invasive and potentially invasive garden
plants (Groves et al. 2005). After initial compilation,
records were corrected to remove misspellings andformatting errors and each taxon was associatedwith its unique USDA Plants database code
(USDA 2010b). All synonymous entries wereconsolidated using the USDA Plants code. The
resulting list included 1417 plant taxa.
For the purposes of the work outlined in thispaper we use the term occurrence instead of theterm population because each plant record in the
Calflora database is made by different individuals
and each individual may have his or her own
definition of what an occurrence is – it may not
be the same as a population. We applied a buffer
to all records to help eliminate some of theinherent error with this approach. See below fora description of the buffering technique. Taxa
were put into broad categories according to thenumber of occurrences in Bay Area counties
reported in the Calflora occurrence database(Calflora 2010): No Bay Area Reports, Limited Bay

The San Francisco Bay Area Early Detection Network

Figure 2. Decision tree showing the species prioritization summary for the Bay Area Early Detection Network. Note: the Watchlist has
not yet been created.

Area Reports (fewer than 100 known occurrences),
Widespread (more than 100 known occurrences).
Williams, Gluesenkamp, Perlmutter, and otherregional botanists used their expert opinion tomove some species form the Limited Bay Area
Reports category to a new category, Underreported.
Underreported species had few mapped occurrences
but were known to be more common. For example,
Kniphofia uvaria (L.) T. Durand and Schinz(redhot poker), 1894 had been reported 19 times
in all of California but was known fromhundreds of locations so it was assigned to theUnderreported category. In all cases, each species
was carefully reviewed by experts and The No
Bay Area Reports list was set aside for further
evaluation in the creation of a watch list. The
Widespread and Underreported taxa were removed
from further consideration, since they wereconsidered too abundant to be early detection
and rapid response candidates. Figure 2 summarizes
the species prioritization process. No Bay Area
Reports included 255 taxa, Limited Bay Area
Reports included 286 taxa, and the Underreported
list plus the Widespread list totaled 876 taxa.

Prioritization of candidate species
Taxa in the Limited Bay Area Reports list wereclassified as “known to be invasive” if they had aweed ranking in any of the databases, weed lists,
and assessments or “not known to be invasivefrom consulted sources,” if there was no suchranking. A small set of taxa not listed in consulted
sources were categorized as invasive based on
expert opinion (e.g., Limonium ramosissimum(Poiret) Maire, 1936 [Algerian sea lavender] andDanthonia pilosa Brown, 1810 [hairy wallaby
grass]). These species were either only known asinvasive in the San Francisco Bay Area or they
were recently identified as invasive so they did
not appear on the consulted lists.
The resulting list of species that were both
known to be invasive and had limited reported
occurrences was shared with additional experts
from county Weed Management Areas, county
Agricultural Commissioners, weed scientists, CalIPC, and local land managers to verify limited
distribution and invasiveness. The final list of 73
taxa comprised the 2010 BAEDN Early Detection235

M. Frey et al.

Table 1. 2010 target species list.
Latin nameCommon name

Latin nameCommon name

Acacia paradoxa Candolle, 1813

paradox acacia

Gunnera tinctoria (Molina) Mirbel, 1805

Chilean gunnera

Acaena novae-zelandiae Kirk, 1871

biddy-biddy

Halimodendron halodendron Voss, 1894

common salttree

Helichrysum petiolare Hilliard & Burtt, 1973

licorice-plant

Hypericum canariense Linnaeus, 1753

Canary Island St.
Johnswort

great ragweed

Isatis tinctoria Linnaeus, 1753

Dyer's woad

white bladderflower

Lepidium campestre (Linnaeus), 1812

field pepperweed

Ligustrum lucidum Aiton, 1810

glossy privet

Achnatherum brachychaetum (Godron)
Barkworth, 1993

shortbristled needlegrass

Aegilops triuncialis Linnaeus, 1753

barbed goatgrass

Ambrosia trifida Linnaeus, 1753
Araujia sericifera Brotero, 1818
Arctotheca calendula (Linnaeus) Levyns,
1942

Capeweed

Arrhenatherum elatius Palisot de Beauvois,
1812

tall oatgrass

Asparagus asparagoides (Linnaeus) Wright,
1909

African asparagus fern

Asphodelus fistulosus Linnaeus, 1753

onionweed

Brachypodium sylvaticum (Linnaeus) Palisot
de Beauvois, 1812

slender false brome

Buddleja davidii Franchet, 1888

orange eye butterflybush

Cardaria pubescens (Meyer) Jarmolenko,
1934

hairy whitetop

Ligustrum ovalifolium Hort ex Decaisne, 1877

California privet

Limonium ramosissimum (Poiret) Maire, 1936

Algerian sea lavender

Linaria genistifolia ssp. dalmatica (Linnaeus) Miller,
1768

Dalmatian toadflax

Linaria vulgaris Hill, 1756

butter and eggs

Lonicera japonica Thunberg, 1784

Japanese honeysuckle

Lythrum salicaria Linnaeus, 1753

purple loosestrife

Nassella formicarum (Delile) Barkworth, 1990; N.
manicata (Desvaux) Barkworth, 1990

tropical needlegrass

Nassella tenuissima (Trinius) Barkworth, 1990

finestem needlegrass

Onopordum acanthium Linnaeus, 1753

Scotch cottonthistle
Illyrian cottonthistle

Carduus acanthoides Linnaeus, 1753

spiny plumeless thistle

Onopordum illyricum Linnaeus, 1753

Carex pendula Hudson, 1762

hanging sedge

Paspalum urvillei Steudel, 1853

Vasey's grass

Carthamus leucocaulos Smith, 1837

whitestem distaff thistle

Polygonum aubertii Henry, 1907

Bukhara fleeceflower

Centaurea diffusa Lamarck, 1785

diffuse knapweed

Centaurea iberica Treviranus ex Sprengel,
1826

Polygonum cuspidatum Wildenow ex Sprengel, 1825

Japanese knotweed

Iberian knapweed

Centaurea maculosa Lamarck, 1785

spotted knapweed

Polygonum polystachyum Wallich, 1829

cultivated knotweed

Pyracantha coccinea Roemer, 1847

scarlet firethorn

Centaurea repens Linnaeus, 1763

hardheads

Pyracantha crenulata (Don) Roemer, 1847

Nepalese firethorn

Centaurea sulphurea Wildenow, 1809

sulphur knapweed

Ricinus communis Linnaeus, 1753

castorbean

Cestrum parqui L'Heritier, 1788

night jessamine

Rubus laciniatus Wildenow, 1806

cutleaf blackberry

Chondrilla juncea Linnaeus, 1753

rush skeletonweed

Rumex dentatus Linnaeus, 1771

toothed dockCirsium undulatum Sprengel, 1826

wavyleaf thistle

Sapium sebiferum (Linnaeus) Roxburgh, 1814

Chinese tallow

Coprosma repens Hooker, 1844

creeping mirrorplant

Scolymus hispanicus Linnaeus, 1753

common goldenthistle

Crupina vulgaris Persoon ex Cassini, 1818

common crupina

Senecio jacobaea Linnaeus, 1753

stinking willie

Senna multiglandulosa (Jacquin) Irwin & Barneby, 1783

glandular senna

Cuscuta japonica Choisy, 1855

Japanese dodder

Cytisus striatus (Hill) Rothmaler, 1944

striated broom

Danthonia pilosa Brown, 1810

hairy wallaby grass

Sesbania punicea Bentham, 1884

rattlebox

Echium plantagineum Linnaeus, 1771

salvation jane

Solanum carolinense Linnaeus, 1753

Carolina horsenettle

Euphorbia esula Linnaeus, 1753

leafy spurge

Solanum rostratum Dunal, 1813

buffalobur nightshade

Euphorbia terracina Linnaeus, 1762

Geraldton carnationweedsmooth cordgrass

Festuca pratensis Hudson, 1762

meadow fescue

Spartina alterniflora Loiseleur-Deslongchamps, 1849
(hybrids)

Gaura drummondii Torrey & Gray, 1840

Drummond's
beeblossom

Spartina densiflora Brongniart, 1876

denseflower cordgrass

Gaura sinuata Nuttall ex Serringe, 1828

wavyleaf beeblossom

Spartina patens (Aiton) Muhlenberg, 1813

saltmeadow cordgrass

Gazania linearis Druce, 1917

treasureflower

Tripidium ravennae ssp. ravennae (Linnaeus) Scholz,
2006

ravennagrass

list of species (Table 1). The species prioritization
list was used to raise awareness about the specieson the list. In addition, once the species list was
created, individual land managers wereapproached and asked to provide additional
236

occurrence data for the targeted species. By
creating a relatively short list and making
personal appeals, we were able to increase thequantity of occurrence information for the next
step in our process.

The San Francisco Bay Area Early Detection Network

Box 1
Canary Island St. John’s wort (Hypericum canariense) easily grows to 10 feet tall in dense single species stands (Figure 3). The plant is highlyinvasive elsewhere in the world but California has just a few occurrences. The species was found in a few populations along the coast of
California from San Diego to Marin. BAEDN helped control small populations on land managed by California State Parks and the non-profit
organization, Audubon Canyon Ranch. The largest known California population was on the San Mateo Coast near Año Nuevo State Park wherea large stretch of high-quality coastal scrub habitat had been invaded. In 2010, with the support of BAEDN, a collaboration of non-profit
organization (San Mateo Weed Management Area and the Peninsula Open Space Trust), government entities (California State Parks, San Mateo
Resource Conservation District, California Department of Food and Agriculture, and the US Fish and Wildlife Service San Francisco Bay
Coastal Program), and a private landowner worked to control and contain this large population. A successful petition was made to the California
Department of Food and Agriculture to list the plant as a noxious weed, the status gives county personnel and landowners important tools toensure effective response. BAEDN secured funding to do an assessment in 2012 of new EDRR targets on lands along the coast and Hypericum
caneriense came out as one of the highest management priorities. BAEDN, now a part of Cal-IPC, is coordinating with local partners (Resource
Conservation District, San Mateo County Department of Agriculture, Peninsula Open Space Trust, and local landowners) to find funding tofollow up.

Figure 3. Hypericum canariense infestation in central California - just south of Gazos Creek, east of Highway 1 in San Mateo County.
Neal Kramer©

Creating a list of target occurrences
Compilation of occurrences
Georeferenced Calflora data for all occurrencesof the 73 target species were converted to an
ArcGIS shapefile and viewed using ArcMap 9.3(ESRI 2010) to allow for spatial analysis andtransformation. Reports were buffered to reduce
the number of redundant reports, convert point
data to polygon data and reduce the effect oferrors such as varying degrees of spatial precision,
limitations of zooming to reporting locations on

aerial imagery, mapping offsets used in the field,
and varying inter-patch distances. 10, 50 and 100
meter buffers were tested. After ground truthing
the effects of choosing each of these buffer sizes,
the 100 meter buffer was selected because it wasmost effective at consolidating redundant
records, was within the range of short-term
dispersal ability of most species, and generated
patch areas that were within the ability of field
personnel to effectively search. Approximately
800 records were merged into 272 distinct
occurrences. The data were spatially joined witha layer of California counties in order to assign
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M. Frey et al.

county locations corresponding to each nonnative occurrence polygon. The resulting attributetable was then exported to Microsoft Excel(Microsoft 2007), where number of occurrencesof each species was determined by county acrossthe nine county San Francisco Bay Area.
Prioritization of occurrences
Each of the 272 occurrences of a priority species
was prioritized using WHIPPET (Skurka Darin
2010). WHIPPET (Weed Heuristics: the Invasive
Population Prioritization for Eradication Tool)
prioritizes eradication targets based on relative
impact, invasiveness, and feasibility of eradication.
Proximity to high value assets and vectors of
spread were measured and scored for eachoccurrence using Geographic Information Systems
software. Propagule pressure is an importantfactor in biological invasions (Simberloff 2009)
but geoprocessing models were not available forpropagule pressure. Rather than generating scoresbased on distance to nearest conspecific population, we scored conspecific populations equallyon this factor based on the number of populations in the region, with higher scores going to
species with fewer populations. We provided
maps by County to all stakeholders and fosteredcollaboration across boundaries to control prioritizedtargets. We also produced maps for individual land
management agencies. Occurrence prioritization
was well received by land managers.
Taking action
BAEDN identified rapid response partners, provided
training, funded contracts, and coordinated partners
to treat and track priority invasive plant populations throughout the region. We responded to
the first reported North American population ofHypericum grandiflorum Salisbury, 1796 (largeflowered St. Johnswort) Large-flowered St John'swort, helped to contain the Helichrysum
petiolare Hilliard and Burtt, 1973 (licorice-plant)
in Marin County state and national parks, helped
to contain the largest population of Hypericum
canariense Linnaeus, 1753 (Canary Island St.
Johnswort) in California (see Box 1), increased
awareness and control of Limonium ramosissimum(Poiret) Maire, 1936 (Algerian sea lavender)
within San Francisco Bay wetlands, worked on
the leading edge of the Napa county populationof Tripidium ravennae ssp. ravennae (Linnaeus)
Scholz, 2006 (ravennagrass) to protect neighboring
counties, supported removal of Brachypodium
238

sylvaticum (Linnaeus) Palisot de Beauvois, 1812
(slender false brome) within San Mateo County,
and re-started removal of various high priority
invasives for which funding had been lost as partof state budget cuts (e.g, Lythrum salicaria Linnaeus,
1753 [purple loose-strife], Centaurea iberica
Treviranus ex Sprengel, 1826 [Iberian Knapweed],
Ricinis communis Linnaeus, 1753 [castorbean], andSesbania punicea Bentham, 1884 [rattlebox]).
Although many intensive invasive plant
management projects were being undertaken before
BAEDN was established, the formation of
BAEDN allowed the actions to be prioritized andcoordinated. BAEDN partners and staff completed
two nine‐county rapid response field seasons in2010 and 2011. By the end of 2011, 6.6% ofpriority occurrences were believed extirpated,
7.7% were totally removed and under surveillance,and 52.4% were actively being treated. The
management success record from before the
BAEDN approach is hard to quantify but anecdotalreports from land managers suggest that more
occurrences of priority species are being reported
and being eradicated than before the BAEDN
was established.
Lessons learned
Data quality
McGeoch et al. (2012) identify many of thechallenges faced when attempting to create
prioritized lists of invasive plants including
access to data, inconsistent distribution of data,
and difficulty accessing accurate data. Although
we faced these challenges, the online Calflora
database provided essential pre-existing plant
occurrence data and a sharable repository for new
data. However, data were unevenly distributed inspace, varied in quality by taxon, were especiallylimited on private land, had unknown spatial
accuracy, and were collected by a range of
observers with varying identification and mapping
skills. Common weed species which are not
management targets for land managers (e.g., Aira
caryophyllea Linnaeus, 1753 [silver hairgrass])are especially underreported. For example, as of
September 5, 2014 there are no Calflora recordsof Aira caryophyllea within the lands from 0 km
to 10 km north of the Golden Gate Bridge inMarin County but the authors know of thousandsof plants in this zone. Uneven reporting may
give a false impression that well reported regions
have more invasives than poorly reported regions.
Populations reported multiple times by separate

The San Francisco Bay Area Early Detection Network

observers may lead to overestimation of the numberof occurrences for a given species (our buffering
technique helps with this problem but must be
applied after extracting data). Overestimation
may also result from inclusion of historical
populations no longer present, as there is nobuilt-in method in place to track population andtreatment status of reported populations. Species
identification errors are also a concern. Because
limited-range weed species are often unfamiliar toland managers, they may be under-reported.
Underreporting may give a false impression thatspecies are less common or widespread than they
really are. Managers must assume that occurrence
data are incomplete (Chen et al. 2013); we used
expert opinion to mitigate data limitations.
Encouraging the use of an existing online datarepository (e.g., http://www.calflora.org, http://www.
eddmaps.org, http://www.inaturalist.org, http://www.eur
ope-aliens.org, http://www.ispot.org.za/) will improve
the quality of available data or at least improveits quantity. Having the capacity to record
absence data will also help (Václavik andMeentemeyer 2009). Starting with as thoroughan inventory of occurrences as possible provides asolid framework to build upon. Data on the
distribution of species occurrences is essential
for success and sharing data leads to better data
for all (Simpson et al. 2009).
Prioritization
BAEDN used existing species assessments rather
than conduct a regionally-adapted assessment foreach of the 1417 candidate species. This approach
carried the benefit of previous research results
and presented an efficient first ranking of invasiveness. Adapting an existing ranking system, suchas the 49 question weed risk assessment processdeveloped for Australia by Pheloung et al.
(1999), for the San Francisco Bay Area would
have yielded useful region-specific information,
but would have required significant time to first
adapt the assessment and then to implement foreach taxa. Average time to complete a weed risk
assessment is reported to be between 6 and 24
hours (Gordon et al. 2008). We chose to use invasive
elsewhere as our primary filter because analysis
of formal weed risk assessment performance has
indicated that abridged weed risk assessments
using the single question “Is the species a weed
elsewhere?” resulted in the same or higher
accuracy than that of the full 49 question weed
risk assessment (Gordon et al. 2008; Kolar andLodge 2001; Mack 1996; Reichard and Hamilton1997). Taken together, the consulted sources casta wide net for determining invasiveness.
Most land managers are anxious to focus theirlimited resources on priority species or occurrences.
Prioritizing species down to a reasonable number
can increase the likelihood of land managers
using the created list; a short list can motivate
and inspire rather than overwhelm and frustrate.
Prioritizing occurrences of target species rather
than just the species provides a clear benefit to
managers, and provides a bigger positive impact
at the landscape scale (Skurka Darin et al. 2010).
Valuable resources can be wasted if low priority
occurrences are treated before high priority
occurrences. By providing a clear framework andauthoritative priorities BAEDN increased
participation.
Partnerships
Forming relationships across political boundaries
helps lead to successful invasive plant management
programs (Higgins et al. 2007). Building thepartnership ahead of time can facilitate data
sharing and rapid response. Having many partners
brings knowledge, skills, land access, and data to
the effort. Creating a formal Memoranda ofUnderstanding was useful because the formality
encouraged managers to articulate the need forearly detection to organizational leadership. In
addition, organizations were able to provide
resources in the form of funding, staff time, data,and other ways not envisioned when the agreement
was established. However, less formal agreements
are needed too and have their place.
Outreach
Although controlling invasive plants is BAEDN’s
focus, investments in reporting and outreachmaterials, the kickoff event, personalized outreach,and training all contributed to success. Reports
are useful tools to demonstrate the organization’s
long-term existence, support funding requests,
and provide a record of the work of the organization.
Reports can be viewed at http://www.cal-ipc.org/
WMAs/BAEDN/. Our kick-off event publicized thework of the organization and motivated collaboration. Personalized outreach to land managers
resulted in more shared data and the treatment ofprioritized occurrences. BAEDN trainings increased
awareness of the issues, motivated managers,
and encouraged sharing of methods and data.
Producing useful tools for managers increased
efficacy.
239

M. Frey et al.

State-wide integration
Cal-IPC is supporting continued work on Bay
Area populations that have been selected for
eradication. BAEDN’s regional coordination set
a strong precedent, and the effort now joins a
growing network of regional partnerships
coordinated by Cal-IPC to develop landscapelevel priorities. Cal-IPC has incorporated lessons
learned from BAEDN into their process forworking with resource managers in other regionswith setting their own strategies. As these regional
efforts develop, Cal-IPC is working to coordinate
them with each other in a statewide network.
Summary
BAEDN was able to harness pre-existing dataand other resources to initiate a regional EDRR
organization. Early collaboration positioned theorganization to secure funding to prioritize species
and occurrences, share information, create newtools, and remove high priority occurrences. By
adopting strategies that made BAEDN a successother regional groups can either add a robust
EDRR component to what they already do or newgroups can be formed. Additional information canbe found at http://www.cal-ipc.org/WMAs/BAEDN/.
Acknowledgements
Without the engaged support of more than 100 partners the
project would not have begun. Generous grants were received
from the American Recovery and Reinvestment Act, California
Department of Food and Agriculture (CDFA), National Fish andWildlife Foundation, U.S. Fish and Wildlife Service (USFWS) San Francisco Coastal Programs, U.S. Forest Service (USFS), andthe San Francisco Bay Joint Venture. Thank you to Doug Johnson
for his long-term and tireless commitment to protecting
California’s natural areas and taking on BAEDN as a project of
Cal-IPC. We also thank John Wilson and an anonymous reviewer
for their edits that greatly improved the manuscript.

References
ABAG (2002) ABAG Projections 2002. Association of Bay Area
http://www.abag.ca.gov/abag/overview/pub/pGovernments.
2002/ (Accessed September 25, 2014)
AZDA (2010) Arizona Department of Agriculture, http://www.
azda.gov/PSD/RegulatedRestrictedNoxiousWeeds.aspx(Accessed June 1, 2010)
BAEDN (2007) Bay Area Early Detection Network 2006–2007
ReportBAEDN (2009) Bay Area Early Detection Network 2008–2009
ReportBAEDN (2011) Bay Area Early Detection Network 2010–2011
ReportBossard CC, Randall JM, Hoshovsky MC (2000) Invasive Plantsof California's Wildlands. University of California Press.
Berkeley, CA, 360 pp

240

Brusati ED, Johnson DW, DiTomaso JM (2014) How to identify
ornamental plants likely to invade California. California
Agriculture 68(3): 89–95, http://dx.doi.org/10.3733/ca.v068
n03p89Calflora: Information on California plants for education, researchand conservation. [web application] (2010) Berkeley,
California: The Calflora Database [a non-profit organization].
http://www.calflora.org/ (Accessed May 10, 2010)
California Islands Biosecurity Program Subcommittee (2013)
California Islands Biosecurity Program. Unpublished
Cal-IPC (2008) California Invasive Plant Inventory. http://www.
cal-ipc.org/ip/inventory/index.php (Accessed June 1, 2010)
CDFA (2010) California Noxious weed list. http://www.cdfa.
ca.gov/plant/ipc/weedinfo/winfo_photogal-frameset.htm

Chen G, Kery M, Plattner M, Ma K, Gardner B (2013) Imperfectdetection is the rule rather than the exception in plant
distribution studies. Journal of Ecology 101: 183–191,
http://dx.doi.org/10.1111/1365-2745.12021

CISAC (2010) The California Invasive Species List. Presented on
April 21, 2010 by the California Invasive Species Advisory
Committee (CISAC) to the Invasive Species Council of
California (ISCC). http://www.iscc.ca.gov/docs/CaliforniaInva
siveSpeciesList.pdf

COP 10 (2010) Strategic plan for biodiversity. Adopted at theConference of the Parties, Nagoya, Japan
Crall AW, Renz M, Panke BJ, Newman GJ, Chapin C, Graham J,
Bargeron C (2012) Developing cost-effective early detection
networks for regional invasions. Biological Invasions 14(12):2461–2469, http://dx.doi.org/10.1007/s10530-012-0256-3
Cusack C, Harte M, Chan S (2009) The Economics of Invasive
Species. Prepared for the Oregon Invasive Species Council.
2009. Oregon Sea Grant, Oregon State University
Daehler CC, Denslow JS, Ansari S, Kuo HC (2004) A Risk
Assessment System for Screening Out Invasive Pest Plantsfrom Hawaii and Other Pacific Islands. Conservation Biology
18: 360–368, http://dx.doi.org/10.1111/j.1523-1739.2004.00066.x
EPA (2013) Level III Ecoregions of the Conterminous UnitedStates, US Environmental Protection Agency, Office of
Research and Development (ORD) - National Health andEnvironmental Effects Research Laboratory (NHEERL),
Corvallis, OR, published April 16, 2013. Data location:
ftp://ftp.epa.gov/wed/ecoregions/us/us_eco_l3.zip, http://edg.epa.gov

ESRI (2010) ArcGIS Desktop: Release 9.3. Redlands, CA:
Environmental Systems Research InstituteGordon DR, Onderdonk DA, Fox AM, Stocker RK, Gantz C(2008) Predicting Invasive Plants in Florida using the Australian Weed Risk Assessment. Invasive Plant Science andManagement 1:178–195, http://dx.doi.org/10.1614/IPSM-07-037.1
Groves RH, Boden R, Lonsdale WM (2005) Jumping the Garden
Fence: Invasive Garden Plants in Australia and theirenvironmental and agricultural impacts. CSIRO report
prepared for WWF-Australia. WWF-Australia, Sydney
Higgins A, Serbesoff-King K, King M, O'Reilly-Doyle K (2007)
The power of partnerships: landscape scale conservation
through public/private collaboration. Natural Areas Journal
27(3): 236–250, http://dx.doi.org/10.3375/0885-8608(2007)27[236:
TPOPLS]2.0.CO;2

IPCC (2014) Summary for Policymakers. In: Field CB, BarrosVR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE,
Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B,
Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White
LL (eds), Climate Change 2014: Impacts, Adaptation, andVulnerability. Part A: Global and Sectoral Aspects.
Contribution of Working Group II to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, pp 1–32

The San Francisco Bay Area Early Detection Network
Kolar CS, Lodge DM (2001) Progress in invasion biology:
predicting invaders. Trends in Ecology & Evolution 16: 199–
204, http://dx.doi.org/10.1016/S0169-5347(01)02101-2
Mack RN (1996) Predicting the identity and fate of plant
invaders: emergent and emerging approaches. BiologicalConservation 78: 107–121, http://dx.doi.org/10.1016/0006-3207(96)00021-3

MAEDN (2014) Mid Atlantic Early Detection Network,
http://www.eddmaps.org/midatlantic/ (Accessed Sept. 10, 2014)
McGeoch MA, Spear D, Kleynhans EJ, Marais E (2012)
Uncertainty in invasive alien species listing. EcologicalApplications 22(3): 959–971, http://dx.doi.org/10.1890/11-1252.1
Mehta SV, Haight RG, Homans FR, Polasky S, Venette RC(2007) Optimal detection and control strategies for invasive
species management. Ecological Economics 61: 237–245,
http://dx.doi.org/10.1016/j.ecolecon.2006.10.024

Microsoft (2007) Microsoft Excel [computer software].
Redmond, Washington: Microsoft
Moody ME, Mack RN (1988) Controlling the spread of plantinvasions: the importance of nascent foci. Journal ofApplied Ecology 25: 1009–1021, http://dx.doi.org/10.2307/240
3762

Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GA,
Kent J (2000) Biodiversity hotspots for conservation
priorities. Nature 403(6772): 853–858, http://dx.doi.org/10.10
38/35002501

NAEPPC (2014) National Association of Exotic Pest Plant
Councils. http://www.naeppc.org (Accessed September 10,
2014)
NDA (2010) Nevada Department of Agriculture. http://agri.nv.
gov/Plant/Noxious_Weeds/Noxious_Weeds_Home/ (Accessed June
1, 2010)NWCB (2010) Washington State Noxious Weed Control Board.
http://www.nwcb.wa.gov (Accessed June 1, 2010)
ODA (2010) Oregon Department of Agriculture. http://www.
oregon.gov/ODA/plant/weeds/Pages/statelist2.aspx (Accessed June
1, 2010)
Pheloung PC, Williams PA, Halloy SR (1999) A weed risk
assessment model for use as a biosecurity tool evaluating
plant introductions. Journal of Environmental Management
57: 239–251, http://dx.doi.org/10.1006/jema.1999.0297
Reichard SH, Hamilton CW (1997) Predicting invasions ofwoody plants introduced into North America. Conservation
Biology 11: 193–203, http://dx.doi.org/10.1046/j.1523-1739.1997.
95473.x

Schneider K (2012) Management of invasive plants on a regional
scale in Saxony-Anhalt, Germany [abstract]. In: Halting
Biological Invasions in Europe: from Data to Decisions,
Neobiota 2012: 7th European Conference on BiologicalInvasions. Pontevedra (Spain) September 12-14, 2012, pp
120–121
SEEDN (2014) Southeast Early Detection Network, http://www.
eddmaps.org/southeast/ (Accessed September 10, 2014)

Shierenbeck KA, Ellstrand NC, Blosser G (2007) Spatiotemporal
patterns in the non-native flora of California, USA. Madroño
54(2): 105–116, http://dx.doi.org/10.3120/0024-9637(2007)54[105:SPITNF]2.0.CO;2

Schoenig S (2005) California Noxious and Invasive Weed Action
Plan, 56 pp
Simberloff D (2009) The role of propagule pressure in biologicalinvasions. Annual Review of Ecology, Evolution, and Systematics 40: 81–102, http://dx.doi.org/10.1146/annurev.ecolsys.1103
08.120304

Simpson A, Jarnevich C, Madsen J, Westbrooks R, Fournier C,
Mehrhoff L, Browne M, Graham J, Sellers E (2009) Invasive
species information networks: collaboration at multiple scales
for prevention, early detection, and rapid response to invasive
alien species. Biodiversity 10(2–3): 5–13, http://dx.doi.org/
10.1080/14888386.2009.9712839

Skurka Darin GM, Schoenig S, Barney JN, Panetta D, DiTomaso
JM (2010) WHIPPET: A novel tool for prioritizing invasiveplant populations for regional eradication, Journal ofEnvironmental Management 92(1): 131–139, http://dx.doi.org/
10.1016/j.jenvman.2010.08.013

US Census Bureau (2013) U.S. Census State and County
QuickFacts. Data derived from Population Estimates,
American Community Survey, Census of Population andHousing, State and County Housing Unit Estimates, County
Business Patterns, Nonemployer Statistics, Economic
Census, Survey of Business Owners, Building Permits
USDA (2010a) Federal Noxious Weeds. http://plants.usda.gov/java/
noxious?rptType=Federal (Accessed June 1, 2010)
USDA (2010b) The PLANTS Database. http://plants.usda.gov.
National Plant Data Team, Greensboro, NC 27401-4901 USA(Accessed June 1, 2010)
USFWS (2011) San Pablo Bay National Wildlife Refuge Final
Comprehensive Conservation Plan. San Francisco Bay
National Wildlife Refuge Complex, 119 pp
Václavík T, Meentemeyer RK (2009) Invasive species
distribution modeling (iSDM): Are absence data anddispersal constraints needed to predict actual distributions?
Ecological Modelling 220(23): 3248–3258, http://dx.doi.org/
10.1016/j.ecolmodel.2009.08.013

Williams AE, Speith E (2008) Invasive plant species early
detection in the San Francisco Bay Area Network: 2007
annual report. Natural Resource Technical Report
NPS/PWR/SFAN/NRTR—2008/135. National Park Service,
Fort Collins, Colorado
Wilson JR, Ivey P, Manyama P, Nänni I (2013) A new national
unit for invasive species detection, assessment anderadication planning. South African Journal of Science
109(5–6): 01–13

241