APPENDIX S2
Construction of IBD, IBR and IBR models
We produced different models presenting different hypotheses governing pine marten geneflow,
using ARCGIS version 9.3 (ESRI 2009), with a raster cell size set to 50 m (Table 1). As
suggested by (Anderson et al. 2010) the sampling grain selected (i.e. 50 x 50 m) is adequate to
infer landscape effects on gene flow as is smaller than the average home-range size of the study
species ( i.e. > 0.5Km2 (Zalewski & Jędrzejewski 2006). In addition, this resolution allows
representation of small landscape patches, but also those smaller elements in the landscape that
will be crucial for the resulting effective distances, including linear elements such roads and
highways (Adriaensen et al. 2003)4.
Land use information was obtained in vector format from the most forest map of Spain (Spanish
Ministry of the environment, 2006) and from national road network maps (Spanish National
Geographic Institute (2008).
The alternative models governing geneflow evaluated are: 1) a simple model of isolation by
distance (IBD), 2) isolation by barriers (IBB) obtained from both clustering approaches (i.e.
GENELAND and STRUCTURE) and 3) three different landscape resistance models (IBR)
previously optimized for the study species at a lower spatial scale (i.e. the Basque Country area)
(Ruiz-González et al. 2014).
1) Isolation by distance (IBD): Our first hypothesis and null model was a test of isolation by
distance across a uniform resistance landscape (Wright 1943; Jenkins et al. 2010). In this model
we assumed movement could occur with equal facility in any direction, with all raster cell
values equal in resistance (i.e. resistance value 1).
2) Isolation by barrier (IBB): Our second hypothesis proposes that the identified clusters
correctly identified population structure in the study area, with boundaries between populations
matching potential barriers to gene flow. To create this model we produced a model matrix,
which reported whether or not each pair of sampled individuals were in the same or different
cluster according to STRUCTURE (IBB_STR) or GENELAND (IBB_Gen) results. This
hypothesis does not assign resistance based on landscape conditions, but is based on the highest
STRUCTURE or GENELAND group membership (q).
3) Isolation by landscape resistance maps (IBR): Our third hypotheses propose that some
land uses promote genetic connectivity for forest dependant species, such as the pine marten,
that specialize in such habitats (Zalewski & Jędrzejewski 2006; Pereboom et al. 2008), while
others resist gene flow. Ruiz-Gonzalez et al. (2014), using partial Mantel tests in a reciprocal
causal modeling framework (Cushman et al. 2013)(Cushman et al. 2013) and competing 59
alternative landscape models, including IBD as a null model, identified that pine marten gene
flow in the Basque country area, is facilitated by natural vegetation, and is resisted by
anthropogenic landcover types and roads. In this study they found as the most supported models
1) two binary landscape models, where preferential land uses for dispersal were assigned a
value of 1 (i.e. Habitat), while non-favourable to dispersal habitat (i.e. non-habitat) sites were
assigned a value of 5, 25, 50 or 100, depending on the scenario. The most supported binary
landscape models (i.e. Land_Fb100, and LandFb_50), indicates that pine marten gene flow in is
facilitated by forests, forestry plantations, scrubland, agroforestry mosaics and pastures and
meadows, and that crops have roughly 100 (LandFb_100) or 50 (LandFb_50) times higher
resistance than optimal habitat. Further, this uniquely supported model indicates that
anthropogenic barriers, such as national roads, highways, urban areas, reservoirs and quarries
and wetlands likely pose much greater resistance to marten gene flow. In addition to these two
binary IBR models, Ruiz-González et al. (2014) also found that the resistance map previously
utilized in the design of ecological corridors linking forest Natura 2000 areas of the Basque
Country (Gurrutxaga et al. 2010) was also a good surrogate of the pine marten gen flow . This
resistance map was based on the assignment of different resistance levels to each land use and
parameterized through bibliographical review and expert opinion (Gurrutxaga et al. 2010).
In this study we used these 3 previously optimized IBR models (at a lower scale and with
smaller sample size), to evaluate if they correctly predict pine marten geneflow in a bigger scale
and to compare their performance with regards the IBB and IBD models.
The effective and Euclidean distances between each pair of individuals were calculated
with PATHMATRIX 1.1 (Ray 2005), following the resistances values outlined in Table 1. Pairwise effective distances between individuals were calculated as the accumulated cost through
the least cost paths (LCP) throughout each resistance surface (Adriaensen et al. 2003; Ray
2005).
Overall, we proposed 6 alternative models: IBD; IBB (GENELAND and
STRUCTURE) and IBR: (LandFb_100, LandFb_50 and EN; see Table 1 for further details) that
were tested within a reciprocal causal modelling framework
Landscape resistance maps (Raw ascii files of the optimal resistance models i.e. IBR_
EN, IBR_LandFB_100 and IBR_LandFb_50), LCD matrices for IBD, IBR (IBR_ EN,
IBR_LandFB_100, IBR_LandFb_50) and IBB (IBB_STR and IBB_GEN) models and genetic
distance (Rousset’s a) matrix are accessible on Dryad Digital Repository,
doi:10.5061/dryad.2k4b7 .
References
Adriaensen F, Chardon JP, De Blust G et al. (2003) The application of “least-cost” modelling as a
functional landscape model. Landscape and Urban Planning, 64, 233–247.
Anderson CD, Epperson BK, Fortin M-J et al. (2010) Considering spatial and temporal scale in
landscape-genetic studies of gene flow. Molecular Ecology, 19, 3565–3575.
Cushman SA, Wasserman TN, Landguth EL, Shirk AJ (2013) Re-Evaluating Causal Modeling with
Mantel Tests in Landscape Genetics. Diversity, 5, 51–72.
Gurrutxaga M, Lozano PJ, del Barrio G (2010) GIS-based approach for incorporating the connectivity of
ecological networks into regional planning. Journal for Nature Conservation, 18, 318–326.
Jenkins DG, Carey M, Czerniewska J et al. (2010) A meta-analysis of isolation by distance: relic or
reference standard for landscape genetics? Ecography, 33, 315–320.
Pereboom V, Mergey M, Villerette N et al. (2008) Movement patterns, habitat selection, and corridor use
of a typical woodland-dweller species, the European pine marten (Martes martes), in fragmented
landscape. Canadian Journal of Zoology-Revue Canadienne De Zoologie, 86, 983–991.
Ray NDO-10. 1111/j. 147.-8268. 2004. 00843. . (2005) PATHMATRIX: a geographical information
system tool to compute effective distances among samples. Molecular Ecology Notes, 5, 177–180.
Ruiz-González A, Gurrutxaga M, Cushman SA et al. (2014) Landscape Genetics for the Empirical
Assessment of Resistance Surfaces: The European Pine Marten (Martes martes) as a Target-Species
of a Regional Ecological Network. PLoS ONE, 9, 19.
Wright S (1943) Isolation by distance. Genetics, 28, 114–138.
Zalewski A, Jędrzejewski W (2006) Spatial organisation and dynamics of the pine marten Martes martes
population in Białowieza Forest (E Poland) compared with other European woodlands. Ecography,
29, 31–43.