Ecological Archives E096-122-A2

Meryl C. Mims, Ivan C. Phillipsen, David A. Lytle, Emily E. Hartfield Kirk, and Julian D. Olden. 2015. Ecological strategies predict associations between aquatic and genetic connectivity for dryland amphibians. Ecology 96:13711382. http://dx.doi.org/10.1890/14-0490.1

Appendix B. Additional description of landscape resistance methods and tables describing landscape resistance values and source data, correlation coefficients between resistance/distance values, mixed-effects modeling results for major genetic clusters of canyon treefrogs and Mexican spadefoots, and results of multiple regression with distance matrices.

Landscape resistance and distance details and construction

We hypothesized relationships between genetic and structural connectivity along a gradient of species water requirements. Final hypotheses were organized into six categories. Two categories imply no correlation with landscape factors: isolated populations (high genetic differentiation between sampling locations driven primarily by low migration, genetic drift, and/or small populations) and panmixia (low genetic differentiation due to high gene flow between all sampling locations and/or large populations). The other four categories imply a specific relationship between genetic connectivity and some landscape factor. These categories are summarized in Table B1. Each category – terrestrial, aquatic, topography, and isolation-by-distance – was evaluated using one or multiple landscape resistance surfaces built with spatial data. These resistance surfaces and data are summarized in Table B1, and details for data layers and sources are included in Table B2.

Hypothesized resistances of structural connectivity between sampling locations were built using CIRCUITSCAPE (McRae 2006), a program utilizing circuit theory to simulate gene flow through a resistance surface. CIRCUITSCAPE allows for gene flow (i.e., "current") to travel across multiple pathways, reporting pairwise summations of resistance between sampling locations. To generate these pairwise data, we built raster maps of resistance (low to high) using data in Table B2. A geographic information system (ArcGIS 10.1, Environmental Systems Research Institute) was used to catalog and manipulate landscape data and generate resistance raster maps. Each resistance map was scaled for hypothesized landscape resistance to gene flow from 1 - 100 where 1 indicates low resistance and 100 indicates high resistance. The scale of resistance values was arbitrary and was designed to reflect hypothesized relationships between landscape features and genetic connectivity. We examined two additional scales of resistance (1 - 1000 and 1 - 10,000), but resistances and the relationships with genetic connectivity were highly correlated (r > 0.99). Thus only results for resistances from 1 – 100 are included in this manuscript.

To balance demand for computational resources with maintaining enough detail to realistically examine structural connectivity, all resistance maps were scaled to 60 m resolution, which involved resampling of some data layers. The spatial extent of resistance rasters ensured a buffer of at least 30 km from the edge of the raster to a given sample site. With this grain and extent, we were able to perform all CIRCUITSCAPE analyses in the pairwise source/ground modeling mode and using a cell connection scheme of eight neighbors, allowing maximum freedom of current flow. Due to the location of one Mexican spadefoot sampling site near the US-Mexico border, the 30 km buffer required use of spatial data from the US and Mexico. Data are generally more widely available and higher resolution for the US, and in some cases in our study Mexico's data are courser resolution (Table B2). However, it is unlikely that the resolution of data for Mexico would skew structural connectivity estimates between populations of any species, particularly with only one sample site very near the border.

Some landscape resistance layers had moderate to strong correlations with one another. These correlations are summarized in Table B3 and were taken into account in all analyses of genetic and structural connectivity relationships.

Mixed-effects modeling results for canyon treefrog and Mexican spadefoot genetic clusters

Mixed-effects modeling results (individual factors and couplet models) for canyon treefrog and Mexican spadefoot genetic clusters are presented in Table B4. Methods and results are presented in the main text.

Multiple regression with distance matrices (MRDM) methods and results

MRDM evaluates relationships between one or many explanatory distance matrices and a response distance matrix and uses permutation to determine statistical significance of the overall model. MRDM also informs significance of each explanatory variable in a model, and those significance values can help elucidate drivers among correlated structural connectivity hypotheses. We included one distance matrix (with the highest R²β value as calculated by mixed-effects modeling) from each structural connectivity category to build global MRDM models. For cases in which multiple variables were included in the supported model, we evaluated the variance inflation factor of each predictor variable. A variance inflation factor > 10 is considered a threshold at which collinearity of variables is problematic for interpreting model results (Kutner et al. 2004). Variance inflation factor was evaluated using the linear form of each model with the R package "car" (Fox and Weisberg 2011). Pairwise landscape resistances were then dropped according to significance (least significant dropped first) until only one resistance layer remained. We inferred strength of model fit by evaluating overall R² and the significance of explanatory variables (pairwise landscape resistances) included in the model. MRDM results are summarized in the main text of the manuscript and are presented in Table B4 of this appendix.

Literature cited

Fox, J., and S. Weisberg. 2011. An R Companion to Applied Regression, Second Edition. Thousand Oaks CA: Sage. URL: http://socserv.socsci.mcmaster.ca/jfox/Books/Companion

Kutner, M., C. Nachsheim, and J. Netter. 2004. Applied Linear Regression Models. McGraw-Hill, New York, New York, USA.

McRae, B. H. 2006. Isolation by resistance. Evolution 60:1551–1561.

 

Table B1. Descriptions and data used to construct resistance layers for each connectivity hypothesis (data described in Table B2, and note that data used often include different sources for Arizona and Mexico). Isolated population and panmixia hypotheses presented in text are not included here because they are not associated with effects of any particular landscape driver. Additional details available upon request from M.C. Mims.

Resistance layers

Description

Data used

Type

Terrestrial

Canopy

Resistance decreases with increased canopy cover.

Canopy Cover - Arizona; Canopy Cover - Mexico

Categorical

Urban

Resistance increases with development.

Urbanization - Arizona; Urbanization - Mexico

Categorical

LandCov

Resistance is lowest with high canopy cover and highest for high development.

Combination of Canopy and Urbanization resistance layers (reclassified)

Categorical

Aquatic

Stream

Resistance is lowest for streams and ponds, moderate for ephemeral streams, and highest for areas with no aquatic habitat.

Streams - Arizona; Streams - Mexico; Ponds and lakes - Arizona; Geology - Arizona; Geology - North America; Streamflow permanence - geological inference; Streamflow permanence - known perennial reaches

Categorical

PrecipET

Resistance decreases as summer precipitation-evapotranspiration increases.

Precipitation - Arizona; Precipitation - Mexico; Evapotranspiration

Continuous

AvgWater

Resistance is lowest where precipitation is highest and aquatic habitat is available and is highest in dry areas with no aquatic habitat.

Average Stream and PrecipET resistance layers

Categorical + Continuous

Topography

Slope

Resistance increases with slope.

Slope from digital elevation model (DEM)

Continuous

Isolation-by-Distance

Euclidean

Pairwise Euclidean distance between sampling locations.

Euclidean distance

Continuous

Null

Uniform landscape resistance.

Null model

Uniform

 

Table B2. Data, details, resolution, and source information for spatial and environmental data used to create resistance layers (Table B1).

Data

Details

Resolution

Source

Canopy cover - Arizona

2001 NLCD canopy density dataset.

30m

http://www.mrlc.gov/nlcd01_data.php

Canopy cover - Mexico

USGS Land Cover Institute.

250m

http://landcover.usgs.gov/nalcms.php

Urbanization - Arizona

2006 NLCD land cover dataset.

30m

http://www.mrlc.gov/nlcd2006.php

Urbanization - Mexico

USGS Land Cover Institute.

250m

http://landcover.usgs.gov/nalcms.php

Streams - Arizona

NHDPlus Version 2, downloaded from National Map Viewer (USGS).

shapefile

http://viewer.nationalmap.gov/viewer/

Streams - Mexico

Stream network supplied by Dale Turner, The Nature Conservancy.

shapefile

Dale Turner, The Nature Conservancy, personal request.

Ponds and lakes - Arizona

NHDPlus Version 2, downloaded from National Map Viewer (USGS).

shapefile

http://viewer.nationalmap.gov/viewer/

Geology - Arizona

Surface deposits identified for characterization of ephemeral streams.

shapefile

http://mrdata.usgs.gov/geology/state/state.php?state=AZ

Geology - North America

Surface deposits identified for characterization of ephemeral streams.

shapefile

http://ngmdb.usgs.gov/gmna/

Streamflow permanence - geological inference

Steamflow permanence records (Huachuca Mountains) as a function of geology.

N/A

Kristin Jaeger and Julian Olden, unpublished data.

Streamflow permanence - known perennial reaches

The Nature Conservancy, US Fish and Wildlife Service: documented and well-known perennial stream reaches.

N/A

http://azconservation.org/map_gallery/san_pedro_river_surface_water
http://www.fws.gov/southwest/federal_assistance/pdfs/chapter%2010%20santa%20cruz%20river%20watershed.pdf

Precipitation - Arizona

PRISM: 30-year precipitation averages for months of June-Oct, 1981-2010.

800m

http://prism.nacse.org/normals/

Precipitation - Mexico

Climate Wizard: 50+ year precipitation averages, June-Oct, 1951-2002.

50km

http://www.climatewizard.org/

Table B2, continued.

Data

Details

Resolution

Source

Evapotranspiration

MODIS Global Terrestrial Evapotranspiration Data Set, June-Oct, 2000-2010.

5km

http://www.ntsg.umt.edu/project/mod16

Slope

Calculated with 9-m Digital Elevation Model from National Elevation Dataset.

9m

http://ned.usgs.gov/downloads.asp

Euclidean distance

Pairwise distances calculated using UTM location data in PASSaGE 2.

distance

http://www.passagesoftware.net/

Null model

Uniform resistance layer.

flexible

 

 

Table B3. Pearson correlation coefficients between pairwise resistance/distance values for each species and their major genetic clusters, identified in Fig. 2 of the main text.

Canyon treefrog

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.811

 

 

 

 

 

 

 

Urban

0.967

0.745

 

 

 

 

 

 

LandCov

0.859

0.990

0.794

 

 

 

 

 

PrecipET

0.946

0.853

0.906

0.897

 

 

 

 

Slope

0.167

-0.066

0.265

-0.026

0.024

 

 

 

Stream

0.955

0.707

0.920

0.756

0.811

0.278

 

 

Null

0.971

0.748

0.998

0.798

0.913

0.245

0.922

 

Eucl

0.965

0.744

0.995

0.794

0.904

0.239

0.921

0.997

Red-spotted toad

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.842

 

 

 

 

 

 

 

Urban

0.894

0.800

 

 

 

 

 

 

LandCov

0.898

0.988

0.873

 

 

 

 

 

PrecipET

0.945

0.876

0.857

0.913

 

 

 

 

Slope

0.394

0.047

0.357

0.159

0.311

 

 

 

Stream

0.966

0.758

0.845

0.820

0.830

0.412

 

 

Null

0.946

0.826

0.951

0.894

0.925

0.446

0.883

 

Eucl

0.906

0.797

0.894

0.861

0.882

0.457

0.848

0.949

Mexican spadefoot

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.842

 

 

 

 

 

 

 

Urban

0.894

0.800

 

 

 

 

 

 

LandCov

0.898

0.988

0.873

 

 

 

 

 

PrecipET

0.945

0.876

0.857

0.913

 

 

 

 

Slope

0.394

0.047

0.357

0.159

0.311

 

 

 

Stream

0.966

0.758

0.845

0.820

0.830

0.412

 

 

Null

0.946

0.826

0.951

0.894

0.925

0.446

0.883

 

Eucl

0.906

0.797

0.894

0.861

0.882

0.457

0.848

0.949

Canyon treefrog - west

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.724

 

 

 

 

 

 

 

Urban

0.955

0.648

 

 

 

 

 

 

LandCov

0.786

0.991

0.707

 

 

 

 

 

PrecipET

0.911

0.795

0.859

0.849

 

 

 

 

Slope

0.080

-0.237

0.234

-0.189

-0.176

 

 

 

Stream

0.950

0.595

0.900

0.658

0.738

0.247

 

 

Null

0.960

0.649

0.998

0.710

0.871

0.209

0.899

 

Eucl

0.872

0.591

0.922

0.639

0.751

0.306

0.846

0.923

Table B3, continued.

Canyon treefrog - Huachuca Mountains

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.523

 

 

 

 

 

 

 

Urban

0.948

0.412

 

 

 

 

 

 

LandCov

0.637

0.981

0.513

 

 

 

 

 

PrecipET

0.933

0.700

0.836

0.798

 

 

 

 

Slope

-0.284

-0.588

-0.106

-0.542

-0.497

 

 

 

Stream

0.935

0.285

0.914

0.406

0.747

-0.039

 

 

Null

0.956

0.401

0.998

0.506

0.850

-0.133

0.918

 

Eucl

0.846

0.348

0.886

0.431

0.756

-0.204

0.804

0.894

Red-spotted toad - north

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.877

 

 

 

 

 

 

 

Urban

0.894

0.754

 

 

 

 

 

 

LandCov

0.926

0.984

0.851

 

 

 

 

 

PrecipET

0.956

0.798

0.908

0.851

 

 

 

 

Slope

0.093

-0.225

0.194

-0.091

0.086

 

 

 

Stream

0.966

0.887

0.802

0.923

0.849

0.067

 

 

Null

0.892

0.752

1.000

0.849

0.907

0.199

0.800

 

Eucl

0.819

0.642

0.968

0.751

0.868

0.256

0.700

0.966

Red-sotted toad - Huachuca Mountains

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.585

 

 

 

 

 

 

 

Urban

0.927

0.363

 

 

 

 

 

 

LandCov

0.687

0.985

0.494

 

 

 

 

 

PrecipET

0.972

0.548

0.956

0.665

 

 

 

 

Slope

-0.271

-0.427

0.036

-0.397

-0.204

 

 

 

Stream

0.983

0.603

0.865

0.688

0.914

-0.324

 

 

Null

0.915

0.324

0.998

0.457

0.943

0.064

0.855

 

Eucl

0.786

0.182

0.913

0.311

0.864

0.090

0.689

0.918

Mexican spadefoot - east

 

AvgWat

Canopy

Urban

LandCov

PrecipET

Slope

Stream

Null

Canopy

0.821

 

 

 

 

 

 

 

Urban

0.881

0.779

 

 

 

 

 

 

LandCov

0.881

0.988

0.856

 

 

 

 

 

PrecipET

0.941

0.858

0.846

0.900

 

 

 

 

Slope

0.265

-0.139

0.197

-0.028

0.207

 

 

 

Stream

0.961

0.728

0.827

0.795

0.814

0.278

 

 

Null

0.940

0.805

0.941

0.878

0.923

0.299

0.867

 

Eucl

0.904

0.782

0.879

0.848

0.883

0.291

0.837

0.948

 

Table B4. Mixed-effects modeling results for major genetic clusters for canyon treefrogs (CT-W: canyon treefrog – west; CT-H canyon treefrog - Huachucas) and Mexican spadefoot (MS-E: Mexican spadefoot - east). Genetic clusters are identified in Figure 2 of the main text. Top R²β values for single-resistance (top) and couplet-resistance (below) models are highlighted in bold font. IP and P hypothesize no landscape effect, indicated by poor model performance across all other models. A dash indicates no support for IP or P. All R²β correlation coefficients are positive with the exception of a negative relationship with Slope in models denoted with underlined text.

Hypotheses

Resistance layers/distance

R²β , mixed-effects models

Species

 

CT-W

CT-H

 

MS-E

Isolated populations (IP)

N/A

-

-

 

-

 

 

 

 

 

 

Terrestrial (TE)

Canopy

0.64

0.06

 

0.22

 

Urban

0.73

0.80

 

0.21

 

LandCov

0.68

0.51

 

0.23

 

 

 

 

 

 

Aquatic (A)

Stream

0.71

0.79

 

0.23

 

PrecipET

0.74

0.76

 

0.22

 

AvgWater

0.73

0.79

 

0.23

 

 

 

 

 

 

Topography (T)

Slope

0.46

0.61

 

0.16

 

 

 

 

 

 

Isolation-by-distance (IBD)

Eucl

0.61

0.58

 

0.29

 

Null

0.72

0.80

 

0.22

 

 

 

 

 

 

Panmixia (P)

N/A

-

-

 

-

 

 

 

 

 

 

TE + T

Best of TE + Slope

0.86

0.85

 

0.44

A + T

Best of A + Slope

0.85

0.84

 

0.22

IBD + T

Eucl + Slope

0.73

0.67

 

0.39

 

Null + Slope

0.86

0.85

 

0.46

 

Table B5. MRDM results by species. Correlation between genetic distance and landscape resistance were consistent within a species or group and are shown as positive (+) or negative (-). R² and p values generated from permutation tests are shown for the overall model, and p values for each factor are included. Best models shown in bold font, and for best models with > 2 factors, variance inflation factors (vif) are included in italicized text just below each factor in the model. *For Mexican spadefoots, the Stream-only and Euclidean-only models performed similarly. A partial mantel test was used to evaluate correlation between genetic distance and either Stream or Euclidean while controlling for the other. The partial mantel statistic was more significant for Stream (p = 0.030) than for Euclidean (p = 0.093). †vif > 10 indicates high collinearity among predictor variables. Note that for both canyon treefrog clusters, vif values suggest interpreting results with caution.

Canyon treefrog

 

Null

AvgWat

Slope

LandCov

R²

p-val

correlation

+

+

-

-

 

 

1

0.201

0.980

0.945

0.915

0.54

0.001

2

0.002

 

0.954

0.915

0.54

0.001

3

0.002

 

 

0.928

0.54

0.001

4

0.001

 

 

 

0.54

0.001

Red-spotted toad

 

Null

Stream

Slope

Urban

R²

p-val

correlation

-

+

+

+

 

 

1

0.097

0.001

0.019

0.335

0.62

0.002

2

0.009

0.001

0.023

 

0.61

0.001

  vif

   6.0

  5.58

  1.39

 

 

 

3

0.121

0.009

 

 

0.47

0.003

4

 

0.001

 

 

0.40

0.001

Mexican spadefoot

 

Eucl

Stream

Slope

LandCov

R²

p-val

correlation

+

+

+

+

 

 

1

0.256

0.179

0.668

0.956

0.44

0.001

2

0.234

0.129

0.51

 

0.44

0.001

3

0.138

0.095

 

 

0.43

0.001

4*

 

0.001

 

 

0.40

0.001

5

0.001

 

 

 

0.38

0.001

Red-spotted toad - north

 

Null

Stream

Slope

Lan

R²

p-val

correlation

-

+

+

+

 

 

1

0.068

0.259

0.034

0.928

0.73

0.027

2

0.026

0.017

0.025

 

0.73

0.007

  vif

  2.95

  2.85

  1.07

 

 

 

3

 

0.15

0.059

 

0.48

0.046

4

 

 

0.061

 

0.40

0.061

 

Table B5, continued.

Red-spotted toad - Huachuca Mountains

 

Null

Stream

Slope

Lan

R²

p-val

correlation

-

+

+

+

 

 

1

0.303

0.383

0.237

0.552

0.42

0.582

2

0.179

0.171

0.302

 

0.36

0.384

3

0.527

0.593

 

 

0.08

0.737

4

0.731

 

 

 

0.01

0.731

Canyon treefrog - west

 

Null

PrecipET

Slope

Urban

R²

p-val

correlation

+

-

-

-

 

 

1

0.109

0.001

0.003

0.465

0.72

0.001

2

0.001

0.001

0.002

 

0.71

0.001

  vif

  9.50

  9.37

  2.36

 

 

 

3

0.001

0.173

 

 

0.51

0.001

4

0.001

 

 

 

0.45

0.001

Canyon treefrog - Huachuca Mountains

 

Null

AvgWat

Slope

Urban

R²

p-val

correlation

+

-

-

-

 

 

1

0.006

0.006

0.256

0.047

0.78

0.001

2

0.005

0.006

 

0.019

0.74

0.001

  vif

  15.39

  16.44

 

  1.44

 

 

3

0.014

0.071

 

 

0.55

0.012

  vif

  11.65

  11.65

 

 

 

 

4

0.001

 

 

 

0.34

0.001

Mexican spadefoot - east

 

Eucl

AvgWat

Slope

LandCov

R²

p-val

correlation

+

+

-

-

 

 

1

0.594

0.048

0.167

0.305

0.36

0.006

2

 

0.028

0.186

0.36

0.36

0.006

3

 

0.001

0.345

 

0.34

0.001

4

 

0.001

 

 

0.31

0.001


[Back to E096-122]