Temporal degradation of data limits biodiversity research

Abstract

Spatial and/or temporal biases in biodiversity data can directly influence the utility, comparability, and reliability of ecological and evolutionary studies. While the effects of biased spatial coverage of biodiversity data are relatively well known, temporal variation in data quality (i.e., the congruence between recorded and actual information) has received much less attention. Here, we develop a conceptual framework for understanding the influence of time on biodiversity data quality based on three main processes: (1) the natural dynamics of ecological systems—such as species turnover or local extinction; (2) periodic taxonomic revisions, and; (3) the loss of physical and metadata due to inefficient curation, accidents, or funding shortfalls. Temporal decay in data quality driven by these three processes has fundamental consequences for the usage and comparability of data collected in different time periods. Data decay can be partly ameliorated by adopting standard protocols for generation, storage, and sharing data and metadata. However, some data degradation is unavoidable due to natural variations in ecological systems. Consequently, changes in biodiversity data quality over time need be carefully assessed and, if possible, taken into account when analyzing aging datasets.

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.…”

Historic herbarium specimens as biocultural assets: An examination of herbarium specimens and their in situ plant communities of the Agulhas National Park, South Africa

“…Herbarium specimens are well recognized in the academic literature as scientific records in their own right (Dargavel, Evans, & Dadswell, 2014; Dosmann, 2006). However, the use of historical data do have a number of limitations and should be used with caution when undertaking contemporary research (Tessarolo, Ladle, Rangel, & Hortal, 2017). Historic herbarium records are presence only records, often with minimal descriptive notes to provide more information of habitat and associated species.…”

Accounting for uncertainty in assessing the impact of climate change on biodiversity hotspots in Spain

“…However, other sources of uncertainty in mapping species distributions have seldom been studied (Rocchini et al, 2011). Few studies have assessed the effects of taxonomic uncertainty (Lozier et al, 2009;Romero et al, 2013;McInerny and Purves, 2011;Tessarolo et al, 2017), diversity of sources for climate data (Fernández et al, 2013;García-López and Real, 2014), behavioural plasticity of species in their response to climate change (Muñoz et al, 2015), correlations between climate, and other environmental factors (Real et al, 2013). These causes of uncertainty can affect model accuracy more than the availability of GCMs and GESs.…”