Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection

Abstract

1. The availability and quantity of observational species occurrence records have greatly increased due to technological advancements and the rise of online portals, such as the Global Biodiversity Information Facility (GBIF), coalescing occurrence records from multiple datasets. It is well-established that such records are biased in time, space and taxonomy, but whether these datasets differ in relation to origin have not been assessed. If biases are specific to different types of datasets, and the relative contribution from these datasets have changed over time, these shifting biases will have implications for interpretations of results and, consequentially, for management and conservation measures. 2. We examined observational GBIF records from Norway to test potential differences in taxonomic, time and land-cover biases between 10 different datasets, with a focus on red-listed and non-native species. 3. The datasets differ in their taxonomic coverage, with datasets dominated by citizen scientist recorders focusing greatly on birds. The number of records has increased over time; in particular, citizen science datasets have had a sharp increase in recent years. 4. The different datasets (including division of the datasets by conservation status) showed differences in geographical coverage. Anthropogenic land covers have more records than would be expected by chance in the majority of cases. Remote areas have fewer records than would be expected, underlining the prevalence of a roadside bias. 5. Accounting for biases in opportunistic species occurrence records need to be a dynamic rather than static process, as the taxonomic and geographical biases have changed over time and differ between datasets, depending on origin and inherent characteristics. Data-collection programmes should be designed to counteract the biases of the specific datasets, and methods to account for the biases in existing data should be developed. When utilizing compiled, open-source data, care must be taken to ensure complementarity between the datasets, both regarding time and space. Incorporating strengths and accounting for biases between datasets can strengthen the integration This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Constructing ecological indices for urban environments using species distribution models

“…As with previous research using community science observations of species (Petersen et al, 2021), we found geographic clustering in the locations of both our native and non-native indicators (SI: Fig. 5).…”

“…We found that membership in our list of indicator species, particularly those native to Los Angeles, were biased towards birds (Table 1). This re ects a bias in the community science data sets found in GBIF towards observations of birds (Petersen et al, 2021). Despite this bias, birds have been useful as ecological indicators in a variety of studies (Mekonen, 2017), including assessments of urban environments (Callaghan et al, 2021).…”

occAssess: An R package for assessing potential biases in species occurrence data

“…Humans have been accumulating species occurrence data for centuries: historically as preserved specimens in museums and herbaria (Newbold, 2010; Spear et al, 2017), and in written accounts (e.g. Oswald and Preston, 2011); and more recently through recording for distribution atlases (Preston, 2013), and various other structured and unstructured monitoring and citizen science initiatives (Boakes et al, 2010; Pescott et al, 2015; Petersen et al, 2021). Taken together, these data provide an immense resource documenting species’ geographical distributions and opportunities to investigate how they may have changed over time.…”

“…There is a growing literature of studies which take species occurrence datasets and screen them for biases (Barends et al, 2020; Boakes et al, 2010; Meyer et al, 2016; Pescott et al, 2019a; Petersen et al, 2021; Ruete, 2015; Speed et al, 2018; Sumner et al, 2019; Troudet et al, 2018); elsewhere, various approaches to visualising the spatial and temporal coverage of occurrence records across large areas have been commonplace in national species atlases for some time (e.g. Preston et al, 2002).…”

Open Data Practices among Users of Primary Biodiversity Data

“…The open availability of massive modern and historical biodiversity data sets has contributed to a wide range of research areas, including ecology, biogeography, global change, and conservation (James et al 2018 , Ball-Damerow et al 2019 , Heberling et al 2021 ). But the analysis of presence-only data is not without challenges; both historical and modern presence-only data are associated with limitations and biases that are distinct from other data types, both because of the lack of absence data and also because of the opportunistic collection process frequently associated with presence-only data (James et al 2018 , Støa et al 2018 , Gelfand and Shirota 2019 , Grimmett et al 2020 , Sicacha-Parada et al 2020 , Johnston et al 2021 , Petersen et al 2021 ). Further biases, errors, and limitations can be introduced in the processes of data preparation, publishing, and long-term maintenance (Tessarolo et al 2017 , Mesibov 2018 ), including the issues of data leakage (Peterson et al 2018 ) and data obsolescence (Escribano et al 2016 ).…”