Supplementary Information (S1-S2)
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Threat to the point: improving the value of comparative extinction risk analysis for
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conservation action
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Kris A. Murray, Luis Verde, Ana Davidson, Moreno Di Marco, and Martina Di Fonzo
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S1: References included in the literature review (p2-8)
S2: Comparing extinction risk models with and without threats (p9-12)
- Figure S2.1 (p10)
- Figure S2.2 (p11)
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S1: References included in the literature review
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S2: Comparing extinction risk models with and without threats
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Methods
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Amphibians: For detailed methods for continental amphibian analysis, see Murray et al.
(2011). For the present study, we reran the same models but excluded all threat variables
in order to test for the effect of omitting threats. We then compared model performance
statistics and species’ classifications (see main text). Graphical results (variable
importance plots, conditional inference tree) of models with threats are shown in Murray
et al. (2011). In the present study, we do not present variable importance plots or
conditional inference trees for models that do not contain threats, as they contain limited
comparative value. The value of these plots is that they graphically highlight the
predictive value of threat variables and their interactions with other variables; hence,
excluding threat variables by definition eliminates the value of their depiction. The key
result from the analyses is that the addition of threats to models improves model
predictive performance relative to intrinsic trait-only models (main text), modifies the
classification of species (main text), and can provide additional explanatory information
about the causes of species declines. These properties make models with threats
potentially more useful for conservation management. See mammal analysis for further
details.
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Mammals: Detailed methods for global mammals analysis. The modeling framework
follows that developed in Davidson et al. (2009). However, in the present study, extrinsic
variables have been added and the traits database has been considerably expanded.
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For 4019 terrestrial mammal species (out of a total of over 5,400 mammals) we compiled
a database of quantitative and categorical intrinsic species’ traits, including body mass,
geographic range size (Ceballos et al., 2005), speed of life history (calculated as the
mass-specific production rate (Hamilton et al., 2011, Sibly & Brown, 2009), population
density, social group size, home range, activity period (diurnal, nocturnal, or both), type
of landmass (continent or island), habitat mode (aquatic, arboreal, fossorial, marine,
marine births on land, terrestrial, or volant), and trophic category (carnivore, omnivore,
herbivore). We also gathered data on environmental variables/geographic settings: mean
annual net primary production (NPP) (Zhao 2005) and mid-latitude and mid-longitude
(Jones et al., 2009) and composite human threat variables: mean human influence index
(Sanderson et al., 2002) and 5th percentile human population density (Jones et al., 2009).
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Missing trait data were imputed using missForest package in R (Stekhoven 2012). We
also gathered data on environmental variables/geographic settings: mean annual net
primary production (NPP) (Zhao 2005) and mid-latitude and mid-longitude (Jones et al.,
2009) and composite human threat variables: mean human influence index (HII)
(Sanderson et al., 2002) and 5th percentile human population density (Jones et al., 2009).
For NPP and HII, we used ESRI’s ArcGIS v9.3 to calculate spatial statistics within the
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geographic range of each species. We used the zonal statistics tool to overlay each
species’ range on top of the extrinsic variable raster dataset and counted pixels that fell
within each range. We then used this tool to calculate mean values of NPP and HII
experienced by each mammal species. Mid-latitude, mid-longitude, and human
population density within each species range were provided directly from the Pantheria
Database (Jones et al., 2009).
The analyses excluded cetaceans, IUCN Data Deficient species and those listed on the
basis of small geographic range (IUCN criterion B) (see Davidson et al., 2009). We used
a dichotomous response variable to represent extinction risk: species classified as
vulnerable, endangered, or critically endangered by the IUCN were considered
“threatened”, and species of lower risk, near threatened and least concern, were
considered “non-threatened” (IUCN, 2008).
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Results
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Fig S2.1. Variable importance plot from Random Forest indicating the rank order of
importance of the different predictors included in the analyses.
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Fig S2.2. Conditional inference tree showing interaction of intrinsic species’ traits,
environmental variables, and human threats in determining extinction risk in mammals. +
sign indicates higher or larger predictor value, and – sign indicates lower or smaller
predictor value.
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and without threats
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