Appendix S1 – Table listing sources of museum specimen records and recent state/province
harvest records (with locational uncertainty less than 8500 m) used in SDM creation and/or
relative density analysis.
Type
Museum
Harvest
Location
Museums listed in Mammal Networked Information System (MaNIS)
(www.manisnet.org)
Museums listed in CONABIO (www.conabio.gob.mx)
Philip L. Wright Zoological Museum, Missoula, Montana
Virginia Museum of Natural History, Martinsville, Virginia
North Carolina Museum of Natural Sciences, Raleigh, North Carolina
Carnegie Museum of Natural History, Pittsburgh, Pennsylvania
Bell Museum of Natural History, Minneapolis, Minnesota
Museum of Cultural and Natural History,
Cleveland Museum of Natural History, Cleveland, Ohio
University of Arkansas Collections, Fayettevelle, Arkansas
University of Alabama Museums, Tuscaloosa, Alabama
University of Northern Iowa Museums, Cedar Falls, Iowa
Royal British Columbia Museum, Victoria, British Columbia
Manitoba Museum, Winnipeg, Manitoba
Royal Saskatchewan Museum, Regina, Saskatchewan
Royal Alberta Museum, Edmonton, Alberta
Idaho Museum of Natural History, Pocatello, Idaho
Connecticut State Museum of Natural History, Storrs, Connecticut
Alaska
Alberta
Arkansas
British Columbia
Connecticut
Idaho
Maine
Manitoba
Massachusetts
Minnesota
Nevada
New Mexico
Nova Scotia
Ontario
Oregon
Quebec
Rhode Island
Appendix S2 – Additional details on methods employed in this manuscript
Environmental Data
Climatic variables were obtained from the WorldClim database (Hijmans et al., 2005), which
gives a variety of climatic data averaged over the years 1960-1991. We also calculated long-term
(1979-2000) average winter (October-March) snow depth and snow cover using data from the
North America Regional Reanalysis dataset (Mesinger et al., 2006). For species distribution
models, we also included information regarding the ecoregion of each grid cell (Omernick,
1987), but excluded information on human disturbance and land use, due to potential
discrepancies between the time period of coyote presence records and available vegetation and
human disturbance layers. We performed an initial screening of the 19 bioclimatic variables
given by WorldClim by running a MaxEnt model including all potential predictor variables, and
eliminated variables that had a small relative influence. We then examined pair-wise correlations
between the remaining variables, and for those pairs that were highly correlated (r > 0.85), we
retained only the most biologically meaningful variable. In total, six bioclimatic variables were
used in the final modeling (minimum temperature coldest month, maximum temperature
warmest month, precipitation coldest quarter, precipitation warmest quarter, temperature
seasonality, diurnal range), as well as % snow cover, elevation and ecoregion.
For the harvest models, we calculated mean values of environmental variables within
each trapline, township, or county as the predictor variables in the analysis. Given sample size
considerations, we a priori restricted the climatic layers to 4 variables related to temperature and
precipitation (see Table 1). We also calculated mean values of the human influence index
(SEDAC, 2005), as a measure of overall human disturbance. This index combines layers related
to human population pressure, infrastructure, land use, and access (roads, rivers, etc.) to produce
an overall map of human disturbance. We calculated mean tree cover (broadleaf or needleleaf) in
each trapline or county based on the Globcover 2009 product (Arino et al. 2012) as a simple
measure of land use that likely influences canid abundance.
Species Distribution Models
Model development
The program MaxEnt was used to create species distribution models separately for the historic
and each expanding population (all expansion points considered together, and NE, NW, and SE
expansion fronts), and were projected across all of the United States and Canada. These models
are hereafter referred to as historic, full expansion (note that the "full expansion" designation
does not include expansions to the south of Mexico, due to a lack of information in this area), NE
expansion, NW expansion, and SE expansion models. MaxEnt models can be highly dependent
on the definition of the background from which pseudo-absences are drawn (Anderson & Raza,
2010). Background data should reflect the area potentially available to a species (Barve et al.,
2011). For our historic coyote population, we defined the background as a 200 km buffer around
the historic range (as an estimate of the area available to dispersing coyotes at the edge of the
range). For each expansion front, we defined the background as a 200km buffer around a
minimum convex polygon formed from the presence records in each expansion zone. As a
subsidiary analysis, we let all of North America function as the background for each population.
Results were similar to the 200km buffer background included in the main text of the
manuscript, but models much more severely constrained to each region (a common problem with
large background areas; Anderson and Raza 2010), which we felt was overly conservative and
these models were therefore not considered further.
Our locality data used to build distribution models suffered from several sources of sampling
bias (Newbold, 2010), which is problematic for MaxEnt modeling (Phillips et al., 2009). We
subsampled records to reduce the unevenness in density of presence records. We created two
subsamples, one with a single presence record for every 900 km2 area, and one with a single
presence record every 10,000 km2 area. This latter subsample reduced unevenness in the
presence records, but also discarded a large number of usable locations. The first subsample still
contained an uneven density of coyote presence records that was reflective of sampling effort,
and therefore bias in the presence records was further addressed by creating a bias grid following
procedures outlined in Elith et al. (2010). The bias grid is used to down-weight the importance of
presence records from areas with more intense sampling. Results of MaxEnt models developed
from the 10000km2 subset and the 900km2 subset with bias file were qualitatively similar, and so
we present the results of the 10000km2 subset only.
MaxEnt models were fit using only hinge features, which allow non-linear fitted
functions similar to a generalized additive model (Elith et al., 2011). We used a 10-fold crossvalidation approach in developing models and calculating response curves to environmental
variables. MaxEnt logistic output was used to plot models and compare niches of historic and
expanding populations (see below). As a general indicator of how well historic models predicted
expansion locations, we calculated area-under-the-curve (AUC) values from historic models
using 10 independent sets of expansion front test locations. For the historic range MaxEnt model,
we calculated a Multivariate Environmental Similarity Surface (MESS; Elith et al, 2010) to
examine the degree to which environments within the historic range were reflective of the
expansion range. The MESS calculation shows how similar a given point or location is to a
reference set of points, given a certain set of predictor variables (Elith et al. 2010). The MESS
calculates, with respect to the predictor climatic variables used in this analysis, how similar a
point is to a reference set of points (in this case, the reference set corresponds to the historic
training data for coyotes). In the figure below, negative values (shown in red) indicate novel
climate (where values of one or more predictors fall outside the range of those layers in the
historic reference set). Positive values (shown in blue) indicate values more similar to the median
values in the historic reference set.
.
Niche comparison
Niche overlap was calculated between historic and each expansion front population by
comparing the logistic output of MaxEnt models using the I statistic (see Warren et al., 2008 for
formula). This statistic compares suitability values of two models at every grid cell in the study
area to determine the degree of niche overlap, and ranges from 0 (no niche overlap) to 1 (niche
identity). Two MaxEnt models that have similar suitability values (i.e., logistic output) for each
grid cell will have higher values for the I statistic, than two models that diverge greatly in their
predicted suitability. Because sample size and environmental space differed between each
comparison (historic-full expansion, historic-NE expansion, historic-NW expansion, historic-SE
expansion), and background environments in each expansion zone varied in their similarity to
background environments in the historic range, niche overlap values could not be compared
directly (Peterson, 2011). Instead, to determine changes in niche overlap we compared the results
of tests of niche similarity, which tests the hypothesis that niches of two populations are more
similar than would be expected based on chance alone (outlined in Warren et al., 2008). To test
for niche similarity, we randomly located presence points within the range of each expanding
population. The number of random presence points was equal to the total number of presence
points within each expansion front. This process was repeated 100 times for each expansion
front. Niche models were created from each random pseudo-replicate and overlap values
between the historic model and these pseudo-replicates were calculated to create a null
distribution of overlap values (niche overlap calculation was performed using EMNTools;
Warren et al., 2010). If the actual overlap values between historic and any given expansion
population falls outside of this distribution (at the α =0.05 level), this indicates that the niche is
more similar than expected given random background differences in the environmental space
(Warren et al., 2008). In contrast, if actual overlap falls within the randomly generated null
distribution, this indicates that the niche is not more similar than expected from random. The
advantage of the niche similarity test is that it accounts for background differences in available
environments as driving the niche overlap through use of the random null model, and therefore
detects similarity between niches caused by actual commonalities in habitat selection between
two populations. We predicted that NE expanding populations would have less similar niches
compared to the historic model (i.e., the actual overlap values would lie closer to the null
distribution of values) than the NW or SE expanding populations.
Appendix S3 – Coyote presence records. Top = all presence records, applying a 900km2 subset
filter, Bottom = all presence records, applying a 10000km2 subset filter. We fit MaxEnt models
using the 900km2 subset and a bias file (sensu Elith et al. 2010), and with the 10000km2 subset
without a bias file. Results were qualitatively equivalent, so we present in the manuscript only
the analysis based on the 10000km2 subset.
Appendix S4: Marginal response curves to environmental variables from historic (solid black
line), NE expansion front (long dashed line), and NW expansion front (short dashed line)
models. Y-axis indicates habitat suitability, and x-axis displays each of 8 continuous
environmental variables used in this study.
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