1
Appendix S1
Library development
We developed a novel microsatellite library for the cactus wren using a modification of
the standard techniques of Hamilton et al. (1999). Libraries were constructed by excising
genomic DNA using the restriction enzyme HincII, and these fragments were ligated to an SNX
linker. Using biotinylated oligonucleotide probes with trinucleotide and tetranucleotide repeats,
microsatellite repeat regions were isolated in the genome. These fragments were PCR amplified
and sequenced on a 454 GL FLX (Roche) in the Evolutionary Genetics Core Facility (EGCF) at
Cornell University. In 3,350 resulting sequences, 414 had microsatellite regions. We mapped
these to the Zebra Finch (Taeniopygia guttata) genome to identify the physical locations of each
marker, and eliminated any that mapped to sex chromosomes to avoid complications in statistical
analysis. We screened 52 loci for variation using a three-primer technique (Schuelke 2000).
Genotyping runs were performed on an ABI 3730 DNA Analyzer using the GS600 size standard
(Life Technologies) either in the CSUPERB Microchemical Core Facility at San Diego State
University or at Bio Applied Technologies Joint, Inc. in San Diego, CA.
References
Hamilton MB, Pincus EL, DiFiore A, Fleischer RC (1999) Universal linker and ligation
proceedures for construction of genomic DNA libraries enriched for microsatellites.
BioTechniques, 27, 500-507.
Schuelke, M (2000) An economic method for the fluorescent labeling of PCR fragments. Nature
Biotechnology, 18, 233-234.
2
Appendix S2
Landscape Variables and Habitat Suitability Model
We used cactus wren occurrence data and an environmental dataset characterizing
southern California to construct a partitioned Mahalanobis D2 model predicting suitable habitat
for cactus wren dispersal (Rotenberry et al. 2002, 2006). The environmental data set consists of
climatic, topographic, and vegetation variables calculated for a grid of points spaced 150 m apart
using Geographic Information Systems (GIS) ESRI ArcMap 10.2 software (ESRI 2013). The
environmental dataset encompasses southern California from the US-Mexico border north to
Maricopa (Kern Co., CA) and from the Pacific Ocean to the east of El Centro (Imperial Co.,
CA). We calculated climate variables at each grid point using normalized 30-year modeled
estimates (1981-2010; Daly et al. 2008, PRISM 2013) for minimum January temperature (°C),
maximum July temperature (°C), and annual precipitation (mm). Topographic variables
included elevation, slope (degrees), northness (sine of aspect), and eastness (cosine of aspect),
extracted for each point from a 10 m digital elevation model (Gesch et al. 2002, Gesch 2007).
We derived topographic heterogeneity, a measure of ruggedness, for a 30 m x 30 m
neighborhood of 10 m cells at each grid point (Sappington et al. 2007). Cactus wrens disperse
through shrublands and we calculated the percent of the landscape within 1 km of each grid point
that was classified as coastal sage scrub or as chaparral using the Fire Resource Assessment
Program vegetation map published in 2002 (FRAP 2002) and updated in 2006.
Mahalanobis D2 measures the similarity between the multivariate mean for a set of
environmental variables calculated at locations where a species occurs and at points in the
landscape being modeled (Clark et al. 1993). For model calibration, we randomly selected 65%
of cactus wren records from a dataset of 353 spatially distinct locations (only one location per
150 m x 150 m grid cell) and used the remaining 35% to evaluate performance. To avoid biasing
3
the model towards environmental conditions at locations with relatively large concentrations of
cactus wrens, we bootstrapped the calibration data by limiting each genetic cluster to 10 or fewer
occurrences per iteration and performed a Principal Components Analysis (PCA) on each of
1000 samples (Knick et al. 2013). The final model consisted of the average of the PCA output
for all iterations after correcting for sign ambiguity (Bro et al. 2008). A continuous habitat
similarity index (HSI) was calculated by rescaling D2 from 0 – 1 with an HSI value calculated
for each 150 m x 150 m grid cell in the environmental dataset for southern California. An HSI of
“0” represents environmental conditions least similar to the multivariate mean for the species
occurrence data set used to calibrate the model (i.e., unsuitable), while a “1” represents those
conditions that are most similar (i.e., suitable). A second GIS layer representing urban areas in
southern California was used to create a fragmentation index among aggregations and to
calculate the percent of urban development surrounding populations. Urban areas were derived
from an impervious surfaces layer with 30 x 30 m grid cells with more than 20% of their surface
area covered by at least 20% imperviousness categorized as urban (Fry et al. 2011).
We delineated the amount of suitable dispersal habitat available to cactus wren
populations as well as the amount of urban area. Population polygons were calculated by
merging contiguous 10-km buffers for each cactus wren location that fell into a particular
population. The 10 km distance represents a maximum dispersal distance for individual cactus
wrens. We calculated the percentages and amounts (ha) of suitable dispersal habitat (HSI ≥ 0.3),
unsuitable habitat (HSI < 0.3), and urban area for each population polygon.
To calculate distances among aggregations, first a habitat resistance surface was
calculated from the habitat model as the –ln (HSI scores + 1) for 150m grid cells (following
Spear et al. 2010). Least cost paths and weighted cost distances among all pairs of aggregations
4
were calculated using the Landscape Genetics ArcToolbox (Etherington 2011) in ArcGIS 10.2.
Resistances were calculated in Circuitscape 4.0 (McRae et al. 2008) under the “connect eight
neighbors” and average conductance settings. While least cost path measures a single connection
route between all pairs of populations, Circuitscape incorporates information on all pathways
between pairs. All of these “habitat” distance measures, as well as Euclidean distance were
compared to genetic distances among sites using Mantel Tests in IBDWS (Jensen et al. 2005).
We compared resulting correlation coefficients to assess the relative fit of each distance measure.
To further assess the effects of urban fragmentation, we calculated a binary index based on
whether least cost paths crossed through urban areas (1) or not (0) and used partial Mantel tests
to compare this matrix to pairwise genetic distances while accounting for geographic distance.
Model Results
Overall, the cactus wren dispersal habitat model performed well with a median HSI of
0.71 for the calibration dataset (n = 0.71) and 0.69 for the validation dataset (n = 123). The
exception was near the Aguanga population where the calculated second-lowest amount of
suitable habitat was likely due to poor model fit for the area.
REFERENCES
Bro R, Acar E, Kolda TG (2008) Resolving the sign ambiguity in the singular value
decomposition. Journal of Chemometrics 22, 135-140.
Clark JD, Dunn JE, Smith KG (1993) A multivariate model of female black bear habitat use for a
geographic information system. Journal of Wildlife Management 57, 519-526.
Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J and Pasteris PA
(2008) Physiographically-sensitive mapping of temperature and precipitation across the
coterminous United States. Journal of Climatology 28, 2031-2064.
Etherington TR. (2011) Python based GIS tools for landscape genetics: visualising genetic
5
relatedness and measuring landscape connectivity, Methods in Ecology and Evolution 2,
52-55.
Fire Resource Assessment Program, Department of Forestry and Fire Protection (FRAP) (2002)
Methods for Development of Habitat Data: Forest and Range 2002 Assessment.
Technical Working Paper 8-19-02.
http://frap.fire.ca.gov/projects/frap_veg/methods/Methods_Development_Habitat_Data_0
2_2.pdf
Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold N, Wickham J (2011)
Completion of the 2006 National Land Cover Database for the coterminous United
States, Photogrammetric Engineering and Remote Sensing 77, 858-864.
Gesch DB (2007) The National elevation dataset, In: Digital Elevation Model Technologies and
Applications: The DEM Users Manual, 2nd Edition (ed, Maune, D). American Society
for Photogrammetry and Remote Sensing, Bethesda, Maryland, 99-118.
Gesch D, Oimoen M, Greenlee S, Nelson C, Steuck M, Tyler D (2002) The national elevation
dataset. Photogrammetric Engineering and Remote Sensing 68 5-11.
Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genetics 6,
13.
Knick ST, Hanser SE, Preston KL (2013) Modeling ecological minimum requirements for
distribution of greater sage-grouse leks: implications for population connectivity across
their western range, USA. Ecology and Evolution 3, 1539-1551.
McRae, BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity
in ecology and conservation. Ecology 10, 2712-2724.
PRISM. 2013. PRISM climate GIS dataset. PRISM Climate Group, Northwest Alliance for
Computational Science and Engineering. http://www.prism.oregonstate.edu/
Rotenberry JT, Knick ST, Dunn JE (2002) A minimalist approach to mapping species’ habitat:
Pearson’s plane of closest fit. In: Predicting Species Occurrences: Issues of Accuracy
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Rotenberry JT, Preston KP, Knick ST (2006) GIS-based niche modeling for mapping species’
habitat. Ecology 87, 1458-1464.
Sappington JM, Longshore KM, Thompson DB (2007) Quantifying landscape ruggedness for
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6
Appendix S3
We would like to acknowledge each of these partners and cooperators individually for their
assistance in this study:
AECOM
Audubon California Starr Ranch Sanctuary
Bureau of Land Management
Cactus Wren Working Group
California Department of Fish & Wildlife
California State Parks & Recreation
California State Polytechnic University, Pomona
California State University, Channel Islands
California Department of Transportation
Center for Natural Lands Management
City of Chula Vista
City of Diamond Bar
City of El Cajon
City of Fullerton
City of Glendora
City of Irvine
City of Los Angeles, Dept. of Recreation and Parks
City of Moorpark
City of San Diego
City of San Dimas
City of Thousand Oaks
City of Whittier
Conejo Open Space Conservation Authority
Conejo Recreation and Parks District
Conservation Biology Institute
Cooper Ecological Monitoring, Inc.
County of Los Angeles, Dept. of Parks and Recreation
County of San Diego
Crystal Cove State Park
Fallbrook Naval Weapons Station
Helix Water District
Irvine Ranch Conservancy
Many Private Landowners
Marine Corps Base Camp Pendleton
Orange County Water District
Orange County Parks
Outdoor Resorts Rancho California, Inc.
Pala Band of Mission Indians
Palos Verdes Peninsula Land Conservancy
Puente Hills Habitat Preservation Authority
San Bernardino County Dept. of Public Works
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San Bernardino County Flood Control District
San Bernardino County Water Conservation District
San Bernardino Valley Municipal Water District
San Diego Audubon Society
San Diego Gas & Electric
San Diego Monitoring and Management Program
San Diego National Wildlife Refuge
San Diego Zoo Institute for Conservation Research
San Dieguito River Park
San Dieguito River Valley Conservancy
Santa Ana Watershed Association
Southern California Edison Viejo Conservation Easement
Sweetwater Authority
The Nature Conservancy
UC-Irvine Ecological Preserve
US Fish & Wildlife Service
Vulcan Materials Company
Western Riverside County MSHCP
Western Riverside County Regional Conservation Authority
Western Foundation for Vertebrate Zoology