The design, launch and assessment of a new volunteer-based plant monitoring scheme for the United Kingdom

Abstract

Volunteer-based plant monitoring in the UK has focused mainly on distribution mapping; there has been less emphasis on the collection of data on plant communities and habitats. Abundance data provide different insights into ecological pattern and allow for more powerful inference when considering environmental change. Abundance monitoring for other groups of organisms is well-established in the UK, e.g. for birds and butterflies, and conservation agencies have long desired comparable schemes for plants. We describe a new citizen science scheme for the UK (the ‘National Plant Monitoring Scheme’; NPMS), with the primary aim of monitoring the abundance of plants at small scales. Scheme development emphasised volunteer flexibility through scheme co-creation and feedback, whilst retaining a rigorous approach to design. Sampling frameworks, target habitats and species, field methods and power are all described. We also evaluate several outcomes of the scheme design process, including: (i) landscape-context bias in the first two years of the scheme; (ii) the ability of different sets of indicator species to capture the main ecological gradients of UK vegetation; and, (iii) species richness bias in returns relative to a professional survey. Survey rates have been promising (over 60% of squares released have been surveyed), although upland squares are under-represented. Ecological gradients present in an ordination of an independent, unbiased, national survey were well-represented by NPMS indicator species, although further filtering to an entry-level set of easily identifiable species degraded signal in an ordination axis representing succession and disturbance. Comparison with another professional survey indicated that different biases might be present at different levels of participation within the scheme. Understanding the strengths and limitations of the NPMS will guide development, increase trust in outputs, and direct efforts for maintaining volunteer interest, as well as providing a set of ideas for other countries to experiment with.

Effective Biodiversity Monitoring Needs a Culture of Integration

“…BRC has been involved in the development of new monitoring schemes complementing presence-only records, from the establishment of the UK Butterfly Monitoring scheme in 1976 to the National Plant Monitoring Scheme in 2015. 43 The most recent of these new schemes, the Pollinator Monitoring Scheme, was conceived specifically with integration of multiple data sources in mind.…”

Integrated ecological monitoring in Wales: the Glastir Monitoring and Evaluation Programme field survey

“…Survey visits were split into two independent parts: (1) a standardised 2 km transect route through each 1 km survey square, established following the Wider Countryside Butterfly Survey (WCBS) method (Brereton et al, 2011;UKBMS, 2020), which uses Pollard walks (Pollard, 1977), as used in the UK Butterfly Monitoring Scheme (Brereton et al, 2019), and (2) a timed search in a 150 m 2 flower-rich area within the square.…”

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

“…If the PC scores are non-normal, it is simple to substitute the ellipses for the actual data points (see s1 for more details). For similar approaches see Pescott et al (2019b) and Barends et al (2020). Note that this assessment assumes that the spatial resolution of the environmental data are relevant to the responses of the target organism(s) at the spatial scale of the analysis desired.…”

“…The proportion of records identified to species level can be used as a measure of how taxonomic uncertainty has changed over time (Troudet et al, 2018). Multidimensional environmental space can be summarised using principal component analyses (PCAs), or other ordination techniques, allowing one to map the distribution of records in environmental space and scrutinise it for bias relative to the total domain of interest (Pescott et al, 2019b). Whilst these metrics are often presented in studies whose primary aim is to assess datasets for their limitations, we find that they are rarely presented in studies which use such aggregated species occurrence data to investigate actual patterns of species' distributions and how they have changed over time [see Ball-Damerow et al (2019) for a sobering review of the lack of scrutiny where species occurrence data are used across research fields more generally].…”