Journal of Biogeography
SUPPORTING INFORMATION
Combining trade data and niche modelling improves predictions of the origin and
distribution of non-native European populations of a globally invasive species
Laura Cardador, Martina Carrete, Belinda Gallardo, José L. Tella
APPENDIX S2
Presence-absence models, effect of sample size differences and Multivariate
Environmental Similarity Surface (MESS) analysis
Presence-absence models
To assess the potential influence of unequal geographic birding effort in our distribution
models, we conducted Maxent models using ‘real’ absences instead of Maxent’s default
10.000 background points located within 100-500 km distances from occurrence points.
‘Real’ absences consisted of surveyed locations at a maximum distance of 500 km from
occurrence locations, where the species has not been detected according to eBird. These
models produced predictions that were highly correlated to those derived using
Maxent’s default background points (Pearson correlation coefficient, r = 0.75 for Asia
and r = 0.65 for Africa), supporting the idea that observed patterns are not affected by
sampling bias. However, the goodness-of-fit of models using ‘real’ absences was
slightly inferior, particularly for the Asian range (AUC = 0.62, TSS = 0.20 for Asia;
AUC = 0.86, TSS = 0.72 for Africa). This may be related to the lower number of
absences available (n=1829 for Asia and n=1119 for Africa) compared to the 10.000
background points provided by Maxent. Our results also raise concerns about the
reliability of absence information, since failing to detect a species does not guarantee
that the species is absent from a given grid, particularly if this data does not come from
intensive surveys (as is the case) (Brotons et al., 2004). Additionally, the absence of the
species can occur in areas with conditions equal to those occupied due to other
processes not considered by our models. This could constrain the obtained range nearer
to the realized distribution, which may underestimate potential suitable areas (Chefaoui
& Lobo, 2008). Since we were interested in providing model predictions able to reflect
all the environmental suitable places in which a species can occur according to selected
environmental variables we retained Maxent models based on random background
selection within 100-500 km distances from occurrence points and bias grid files as
final models (Phillips et al., 2009, Elith et al., 2010).
Sample size differences
To minimize the potential effect of sample size differences between African and Asian
occurrences in model performance, we conducted Maxent models using 10 random
subsamples of the Asian dataset equal to the number of occurrences available for Africa.
This model predicted occurrences within the training region with high AUC (AUC =
0.94 ± 0.01) and TSS values (TSS = 0.77 ± 0.02) and produced highly concordant
predictions to Maxent models using the complete data set (Pearson correlation
coefficient range: 89 – 0.93). Sample size differences were thus not likely to affect our
results, and thus the complete data set for the Asian range was retained for final models.
MESS analyses
Since model transferability can be problematic when extrapolating to areas with
conditions non-analogous to those where the model was calibrated, we conducted
Multivariate Environmental Similarity Surface (MESS) analysis in Maxent. MESS
measures the similarity of any given pixel in the invaded range to a reference set of
pixels in the calibration area with respect to the chosen predictor variables. A pixel with
a positive value indicates that it falls within the range of environmental values present
in the calibration area, while a pixel with a negative value indicates that at least one
variable has a value that is outside of the range of environmental values present in the
calibration area. Negative values close to zero could mean that the environmental
conditions are only slightly different from those prevailing in the calibration area, thus
we defined analogous climatic conditions with a threshold value of -20 (Mateo et al.,
2014). MESS maps (Fig. S4) showed that most areas of Europe appear to have
background environmental conditions within the range of that found in the Asian
calibration range. In the case of Africa, while Eastern Europe does not have
environmental conditions within the environmental range in Africa, most of Western
Europe (e.g. Portugal, Spain, France, Netherlands, Great Britain, Germany, Southern
Sweden) have. Interestingly, even in those areas (which coincide with areas predicted as
suitable for Asian parakeets, see Fig. 2), African models do not predict the presence of
the species. This result is likely to be related to habitat selection patterns of African
parakeets. That is, although available, African parakeets do not occupied habitats
similar to those found in those areas of Europe in their native range and thus models do
not predict them as suitable. At least in such regions, we can be confident that
differences in African and Asian predictions are not related to model extrapolation
outside of calibration conditions.
Figure S4. MESS maps for Maxent models calibrated in Asian and African native
ranges.
References
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