DOI: 10.5061/dryad.08kprr54c
title: Factors influencing transferability in species distribution models

description: Species distribution models (SDMs) provide insights into species’ ecology
 and distributions and are frequently used to guide conservation
 priorities. However, many uses of SDMs require model transferability,
 which refers to the degree to which a model built in one place or time can
 successfully predict distributions in a different place or time. If a
 species’ model has high spatial transferability, the relationship between
 abundance and predictor variables should be consistent across a
 geographical distribution. We used Breeding Bird Surveys, climate and
 remote sensing data, and a novel method for quantifying model
 transferability to test whether SDMs can be transferred across the
 geographic ranges of 129 species of North American birds. We also assessed
 whether species’ traits are correlated with model transferability. We
 expected that prediction accuracy between modeled regions should decrease
 with 1) geographical distance, 2) degree of extrapolation, and 3) were
 affected by a ‘core-boundary’ effect, which assesses distances to the
 boundary of a distribution. Our results suggest that very few species have
 a high model transferability index (MTI). Species with large
 distributions, with distributions located in areas with low topographic
 relief, and with short lifespans are more likely to exhibit low
 transferability. Transferability between modeled regions also decreased
 with geographical distance and degree of extrapolation. We expect that low
 transferability in SDMs potentially resulted from both ecological
 non-stationarity (i.e., biological differences within a species across its
 range) and over-extrapolation. Accounting for non-stationarity and
 extrapolation should substantially increase prediction success of species
 distribution models, therefore enhancing the success of conservation
 efforts.
description: <strong>Data – Bird species and
 abundance</strong> We used data from the USGS
 Breeding Bird Survey (BBS) to extract abundances of bird species in Canada
 and USA (Pardieck et al. 2019). BBS consists of routes surveyed once a
 year during the breeding season (typically June). A BBS route includes 50
 three-minute point counts, separated by a distance of at least 0.5 miles
 (Sauer et al. 2003). We selected bird species (n = 138)
 using three criteria. First, a species had to be detected in at least 30
 different routes per year. This provided for a minimum sample size in the
 number of routes where a species is present and helped ensure our models
 would predict reasonably well (Hernandez et al. 2006, Wisz et al. 2008).
 Second, we selected species with a prevalence (i.e., the percentage of
 routes where the species was present) of at least 20%. We also excluded
 highly common species (prevalence &gt;75%). These prevalence values
 are recommended to improve the fit of SDMs (McPherson et al. 2004). Third,
 a minimum of 80% of the breeding distribution of each species had to be
 within the area covered by the BBS routes. We used breeding bird
 distributions from BirdLife International (2018). The area covered by the
 BBS routes was determined using a minimum convex polygon surrounding all
 BBS routes.  For each BBS route and species, we used
 the mean abundance for the years of 2013 to 2017 (inclusively; Howard et
 al., 2014). This range of years represents the latest five years available
 at the time of download. Using the mean abundance across a short time
 frame enabled us to reduce the noise caused by yearly changes in
 detections, while limiting the impact caused by long-term changes in
 habitat and climate on bird abundance (Gutiérrez-Illán et al. 2014, Betts
 et al. 2019). <strong>Data – Environmental
 covariates</strong> We used climatic and land
 cover covariates known to be correlated with bird abundance (Austin 2002,
 Shirley et al. 2013, Howard et al. 2015). Data were obtained from Google
 Earth Engine (Gorelick et al. 2017) and were summarized for each BBS route
 and year (2013 to 2017), using a 400 m buffer (Bahn and McGill 2013).
 Datasets were selected based on their availability across North America.
 Climatic covariates were obtained from Daymet V3 (Thornton et al. 2017)
 and included summer precipitation (prcpSummer), winter precipitation
 (prcpWinter), maximum summer temperature (tMax), and minimum winter
 temperature (tMin). We used the equivalent of the band 3 (B3) and 4 (B4)
 of Landsat 7, from Landsat 5, 7, and 8 as land cover variables. These land
 cover data were summarized using the LandTrendr tools (Kennedy et al.
 2018). LandTrendr includes pre-processing of the images including
 geometric rectification and cloud and shadow screening. It creates a
 yearly surface reflectance composite which we used to summarize data for
 each BBS route. We used B3 to discriminate between built-up environments
 and vegetation, and B4 to compare rates of chlorophyll absorption which is
 useful to distinguish between conifer and broadleaf as well as young
 versus senescent vegetation (Cohen and Goward 2004). The climatic and land
 cover covariates used in the analysis are summarized in Table
 1 of paper. To be consistent with bird data and to increase
 model transferability (Tuanmu et al. 2011), covariates for each BBS route
 were then averaged over the period from 2013 to 2017.