Why georeferencing matters: Introducing a practical protocol to prepare species occurrence records for spatial analysis

Abstract

Species Distribution Models (SDMs) are widely used to understand environmental controls on species’ ranges and to forecast species range shifts in response to climatic changes. The quality of input data is crucial determinant of the model's accuracy. While museum records can be useful sources of presence data for many species, they do not always include accurate geographic coordinates. Therefore, actual locations must be verified through the process of georeferencing. We present a practical, standardized manual georeferencing method (the Spatial Analysis Georeferencing Accuracy (SAGA) protocol) to classify the spatial resolution of museum records specifically for building improved SDMs. We used the high‐elevation plant Saxifraga austromontana Wiegand (Saxifragaceae) as a case study to test the effect of using this protocol when developing an SDM. In MAXENT, we generated and compared SDMs using a comprehensive occurrence dataset that had undergone three different levels of georeferencing: (1) trained using all publicly available herbarium records of the species, minus outliers (2) trained using herbarium records claimed to be previously georeferenced, and (3) trained using herbarium records that we have manually georeferenced to a ≤ 1‐km resolution using the SAGA protocol. Model predictions of suitable habitat for S. austromontana differed greatly depending on georeferencing level. The SDMs fitted with presence locations georeferenced using SAGA outperformed all others. Differences among models were exacerbated for future distribution predictions. Under rapid climate change, accurately forecasting the response of species becomes increasingly important. Failure to georeference location data and cull inaccurate samples leads to erroneous model output, limiting the utility of spatial analyses. We present a simple, standardized georeferencing method to be adopted by curators, ecologists, and modelers to improve the geographic accuracy of museum records and SDM predictions.

Distributional patterns of Neotropical seasonally dry forest birds: a biogeographical regionalization

“…taxonomic uncertainties, identification errors, or errors on locality information associated with species occurrences). Thus, it is important to promote the constant update of the taxonomic status of specimens deposited in collections, as well as the procedures for cleaning and revising biodiversity data from heterogeneous sources (Navarro‐Sigüenza et al., ; Bloom et al., ).…”

The compounding consequences of wildfire and climate change for a high‐elevation wildflower (Saxifraga austromontana)

“…We compiled a comprehensive dataset of historical herbarium records for S. austromontana (Bloom et al., ; see Appendix S1 in Supplemental Material). The dataset was edited to omit duplicate records, extreme outliers clearly outside the species’ observed range, and misidentified taxa.…”

“…The dataset was edited to omit duplicate records, extreme outliers clearly outside the species’ observed range, and misidentified taxa. We meticulously georeferenced each herbarium record in the edited dataset, following the Spatial Analysis Georeferencing Accuracy (SAGA) protocol (Bloom et al., ). SAGA is a manual georeferencing approach specifically designed to prepare historical records for use in spatial analysis.…”

“…S. bronchialis ssp. austromontana [Weigand] Piper) (DeChaine, ), or the Spotted Saxifrage, which occurs exclusively in high elevation and lower elevation cold micro‐refugium sites (Bloom, Flower, & DeChaine, ). We used historical herbarium records, extensive field observations, species distribution models (SDMs), and simple wildfire predictive models to investigate how the interaction between climate change and wildfire has and may further impact S. austromontana across its range.…”

Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory

“…Historical data require a substantial amount of filtering and/or statistical treatment to account for sampling biases (Vellend et al 2013), but recent advances are improving the potential of these data for understanding ecological community dynamics by minimizing the uncertainty in each datum's spatial, temporal and taxonomic reference. Much progress has occurred with respect to streamlining and improving approaches to georeferencing and digitization of museum specimens (Ellwood et al 2015, Bloom et al 2018) and physical maps derived from historical surveys (Kelly et al 2016(Kelly et al , 2017, integrating taxonomies and tracking taxonomic changes (Pyle and Michel 2008, Pyle 2016, Ytow 2016, and understanding the sources of uncertainty and bias in historical data sources across space (Rocchini et al 2011, Meyer et al 2015, Ruete 2015, Stropp et al 2016, time (Meyer et al 2015, Stropp et al 2016, Tessarolo et al 2017) and taxa (Troudet et al 2017). However, due to their largely opportunistic nature, historical data remain unreliable sources of information on species' relative abundances for many regions and times.…”