The use of opportunistic data for IUCN Red List assessments

IUCN Red Lists are recognized worldwide as powerful instruments for the conservation of species. Quantitative criteria to standardize approaches for estimating population trends, geographic ranges and population sizes have been developed at global and sub-global levels. Little attention has been given to the data needed to estimate species trends and range sizes for IUCN Red List assessments. Few regions collect monitoring data in a structured way and usually only for a limited number of taxa. Therefore, opportunistic data are increasingly used for estimating trends and geographic range sizes. Trend calculations use a range of proxies: (i) monitoring sentinel populations, (ii) estimating changes in available habitat, or (iii) statistical models of change based on opportunistic records. Geographic ranges have been determined using: (i) marginal occurrences, (ii) habitat distributions, (iii) range-wide occurrences, (iv) species distribution modelling (including site-occupancy models), and (v) process-based modelling. Red List assessments differ strongly among regions (Europe, Britain and Flanders, north Belgium). Across different taxonomic groups, in European Red Lists IUCN criteria B and D resulted in the highest level of threat. In Britain, this was the case for criterion D and criterion A, while in Flanders criterion B and criterion A resulted in the highest threat level. Among taxonomic groups, however, large differences in the use of IUCN criteria were revealed. We give examples from Europe, Britain and Flemish Red List assessments using opportunistic data and give recommendations for a more uniform use of IUCN criteria among regions and among taxonomic groups

Statistics for citizen science: extracting signals of change from noisy ecological data

1. Policy-makers increasingly demand robust measures of biodiversity change over short time periods. Long-term monitoring schemes provide high-quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy-relevant timescales. 2. We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method-scenario combination, as well as the power to detect genuine trends. 3. We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful. 4. We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably Frescalo and Occupancy-Detection models. 5. The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection

A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes

1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales

Over a century of data reveal more than 80% decline in butterflies in the Netherlands

Opportunistic butterfly records from 1890 to 2017 were analysed to quantitatively estimate the overall long-term change in occurrence of butterfly species in the Netherlands. For 71 species, we assessed trends in the number of occupied 5 km × 5 km sites by applying a modified List Length method, which takes into account changes in observation effort. We summarised the species trends in a Multi-Species Indicator (MSI) by taking the geometric mean of the species indices. Between 1890–1930 and 1981–1990, the MSI decreased by 67%; downward trends were detected for 42 species, many of which have disappeared completely from the Netherlands. Monitoring count data available from 1992 showed a further 50% decline in MSI. Combined, this yields an estimated decline of 84% in 1890–2017. We argue that in reality the loss is likely even higher. We also assessed separate MSIs for three major butterfly habitat types in the Netherlands: grassland, woodland and heathland. Butterflies strongly declined in all three habitats alike. The trend has stabilised over recent decades in grassland and woodland, but the decline continues in heathland

Declines in common, widespread butterflies in a landscape under intense human use.

Analyses of species' population losses typically show a dichotomy between strongly affected, rare, and localized species and apparently unaffected, common, and widespread species. We analyzed 16 years (1992-2007) of butterfly transect count data from The Netherlands in a reevaluation of the trends of common, widespread species. Fifty-five percent (11 of 20 species) of these species suffered severe declines in distribution and abundance. Overall, cumulative butterfly abundance declined by around 30%. Some of the species in decline used to be omnipresent in gardens and parks, and 2 of the species were previously considered agricultural pests. Based on their declines over the last 16 years, 2 of the 20 species (Lasiommata megera and Gonepteryx rhamni) reached endangered status in The Netherlands under the IUCN (International Union for Conservation of Nature) population-decline criterion, and 2 species (Inachis io and Thymelicus lineola) met vulnerable criterion. Butterflies in farmland, urban, and particularly woodland areas showed the largest decline in species abundance. The abundance of species associated with vegetation types found mainly in nature reserves (dunes, heathland, and, to a lesser extent, seminatural grassland) increased or remained stable. The decline of widespread species requires additional conservation strategies in the wider landscape

Over a century of data reveal more than 80% decline in butterflies in the Netherlands

Opportunistic butterfly records from 1890 to 2017 were analysed to quantitatively estimate the overall long-term change in occurrence of butterfly species in the Netherlands. For 71 species, we assessed trends in the number of occupied 5 km × 5 km sites by applying a modified List Length method, which takes into account changes in observation effort. We summarised the species trends in a Multi-Species Indicator (MSI) by taking the geometric mean of the species indices. Between 1890–1930 and 1981–1990, the MSI decreased by 67%; downward trends were detected for 42 species, many of which have disappeared completely from the Netherlands. Monitoring count data available from 1992 showed a further 50% decline in MSI. Combined, this yields an estimated decline of 84% in 1890–2017. We argue that in reality the loss is likely even higher. We also assessed separate MSIs for three major butterfly habitat types in the Netherlands: grassland, woodland and heathland. Butterflies strongly declined in all three habitats alike. The trend has stabilised over recent decades in grassland and woodland, but the decline continues in heathland.</p

Appendix A. Figures showing number of detection/nondetection observations per year at all 566 5 × 5 km sites within the Dutch range of Hipparchia semele derived from single-records data, derived from short daily species lists, and derived from comprehensive daily species lists.

Figures showing number of detection/nondetection observations per year at all 566 5 × 5 km sites within the Dutch range of Hipparchia semele derived from single-records data, derived from short daily species lists, and derived from comprehensive daily species lists