DOI: 10.5061/dryad.8cz8w9gnp
title: Avian point-counts from Rhode Island and Connecticut used to test species distribution models

description: Spatial-biases are a common feature of presence-absence data from citizen
 scientists. Spatial thinning can mitigate errors in species distribution
 models (SDMs) that use these data. When detections or non-detections are
 rare, however, SDMs may suffer from class imbalance or low sample size of
 the minority (i.e. rarer) class. Poor predictions can result, the severity
 of which may vary by modeling technique. To explore the consequences of
 spatial bias and class imbalance in presence-absence data, we used eBird
 citizen science data for 102 bird species from the northeastern USA to
 compare spatial thinning, class balancing, and majority-only thinning
 (i.e., retaining all samples of the minority class). We created SDMs using
 two parametric or semi-parametric techniques (generalized linear models
 and generalized additive models) and two machine-learning techniques
 (random forest and boosted regression trees). We tested the predictive
 abilities of these SDMs using an independent and systematically collected
 reference dataset with a combination of discrimination (area under the
 receiver operator characteristic curve; true skill statistic; area under
 the precision-recall curve) and calibration (Brier score; Cohen’s kappa)
 metrics. We found large variation in SDM performance depending on thinning
 and balancing decisions. Across all species, there was no single best
 approach, with the optimal choice of thinning and/or balancing depending
 on modeling technique, performance metric, and the baseline sample
 prevalence of species in the data. Spatially thinning all the data was
 often a poor approach, especially for species with baseline sample
 prevalence &lt; 0.1. For most of these rare species, balancing classes
 improved model discrimination between presence and absence classes, but
 hindered model calibration. Baseline sample prevalence, sample
 size, modeling approach, and the intended application of SDM output –
 whether discrimination or calibration – should guide decisions about how
 to thin or balance data, given the considerable influence of these
 methodological choices on SDM performance.  For prognostic
 applications requiring good model calibration (vis-à-vis discrimination),
 the match between sample prevalence and true species prevalence may be the
 overriding feature and warrants further investigation.
description: This test dataset includes
 presence/absence data for 102 bird species based on point-counts conducted
 throughout Connecticut and Rhode Island from three different studies. One
 study covered interior forests habitats primarily in eastern Connecticut,
 where at each of 32 forested sites, seven point-count stations were spaced
 a minimum of 160 m apart. A total of 358 surveys were conducted in 2018 at
 220 locations, with 87 locations surveyed once, 128 surveyed twice, and
 five surveyed three times. In another study, point-count stations were
 randomly stratified by habitat throughout Connecticut with a maximum of 10
 point-counts spaced at least 500 m apart within 200 randomly-selected 5 x
 5 km grid cells. A total of 932 surveys were conducted in 2018 at 690
 locations with 450 locations surveyed once, 238 surveyed twice, and two
 surveyed three times. In the remaining study, point-counts were conducted
 throughout Rhode Island at 3,664 randomly selected locations spaced a
 minimum of 250 m apart. Each location was surveyed once in 2015.
 Point-counts were conducted between May 19<sup>th</sup> – July
 5<sup>th</sup> to capture the peak breeding period for most
 species in the dataset. This dataset is presented with associated
 observation and environmental covariates. See the manuscript and Table S2
 for additional details.
description: Environmental covariate sources: National Landcover
 Database (NLCD), the National Wetlands Inventory (NWI), NOAA's
 Coastal Change Analysis Program 2010 Forest Fragmentation Data (C-CAP),
 and the USGS National Elevation Dataset (NED). Environmental covariates
 are summarized as proportional coverage (NLCD, NWI, and C-CAP) or
 mean values (NED) based on a 250-m radius circle surrounding the point.
 See ColumnHeaderTable txt file for column descriptions.