Characterisation of false-positive observations in botanical surveys

Abstract

Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. They were told beforehand that their final score would be the sum of the correct species they listed, but false-positive errors counted against their overall grade. The number of errors varied considerably between people, some people create a high proportion of false-positive errors, but these are scattered across all skill levels. Therefore, a person’s ability to correctly identify a large number of species is not a safeguard against the generation of false-positive errors. There was no phylogenetic pattern to falsely observed species; however, rare species are more likely to be false-positive as are species from species rich genera. Raising the threshold for the acceptance of an observation reduced false-positive observations dramatically, but at the expense of more false negative errors. False-positive errors are higher in field surveying of plants than many people may appreciate. Greater stringency is required before accepting species as present at a site, particularly for rare species. Combining multiple surveys resolves the problem, but requires a considerable increase in effort to achieve the same sensitivity as a single survey. Therefore, other methods should be used to raise the threshold for the acceptance of a species. For example, digital data input systems that can verify, feedback and inform the user are likely to reduce false-positive errors significantly.

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

“…For instance, Farmer et al ( 2012 ) found a tendency for more false positives of rare species to be recorded by participants with higher expertise. In contrast, Groom and Whild ( 2017 ) found false positives to be uniformly distributed among observers of different expertise, yet both studies reported higher frequencies of false positive detections for rarer species when compared to more common species. Increasing participants’ observational skills, in the aim of reducing false negative and false positive detections, may be directly addressed by providing training and feedback (but see Feldman et al 2018 ), although such an option is often not feasible for many community science projects.…”

“…The reason is that false positives cannot be distinguished from true positives from reported detections alone. However, false positives are common in community science data, particularly for studies that aim at detecting recently introduced and hence rare alien species that are therefore easily misidentified (Groom and Whild 2017 ). To learn false positive rates in an occupancy setting, additional information must be available, either in the form of ground-truth at a subset of locations, or confirmed detections (e.g., by requesting to upload pictures of the observed individual(s) (Chambert et al 2015 ; Vantieghem et al 2017 ).…”

Personalised ecology and detection functions

“…Thus far, I have focussed simply on the probability that an individual organism will be detected by an observer if it is available to be detected. In the context of estimating occurrences and abundances it is important not just if an individual that is present is not detected (a false negative) but also if an individual is detected when it is not actually there (a false positive), and the frequency with which these errors occur has been a focus of some research attention (Groom & Whild, 2017; Miller et al., 2012). One might argue that while false negatives reduce human–nature interactions, false positives matter a good deal less, as there may nevertheless be benefits to be obtained by a person from positive human–nature interactions that did not actually occur but which they perceived to do so (although this would obviously not extend to false‐positive interactions with species that cause undue anxiety).…”

How to predict fine resolution occupancy from coarse occupancy data

“…This might be a property of the spatial distribution of rare species, but it may also reflect an over-sampling of rare species. For example, if observers are more interested in observing and recording rare species than common ones or if rare species are more likely to be false-positive observations (Groom & Whild, 2017). Biogeographic atlas data are rarely, if ever, a completely systematic sample and often contain spatial and taxonomic sampling biases (Dennis & Thomas, 2000;Rich & Woodruff, 1992).…”