Phantom species: adjusting estimates of colonization and extinction for pseudo‐turnover

Abstract

Ecologists rely on field surveys to monitor long‐term ecological change but finite sampling and the prevalence of rare species mean that surveys inevitably miss some species present at a given location. These ‘phantom species’ produce pseudo‐turnover by inflating observed rates of local colonization and extinction in resurvey studies, especially among rare species. In this paper, we quantify the probability that pseudo‐turnover occurs due to imprecise plot relocation and/or shifts in where individuals are located. Using sampling models derived from the binomial distribution, we estimate probabilities of missing species as a function of local abundance and sampling intensity. Spatially explicit simulations confirm that our binomial model is robust to non‐random sampling schemes and clumped species distributions. False absences decline predictably as species abundance and sampling intensity increase allowing us to statistically adjust naïve estimates of colonization and extinction for expected rates of pseudo‐turnover. To illustrate the model's real‐world utility, we analyze apparent colonizations and extinctions for 331 species distributed over 83 sites in southern Wisconsin forests surveyed in both the 1950s and 2000s. Limited sampling in the 1950s means expected rates of pseudo‐colonization are appreciable. Accounting for pseudo‐turnover thus reduces estimated community‐wide colonization rates by 51% compared to naïve (observed) rates. More complete sampling in the 2000s limited overestimates of local extinction to 14%. We distinguish three zones of inference based on sampling intensity and species abundance where: 1) naïve estimates approximate true values of turnover, 2) adjustments are important, and 3) species are too rare relative to sampling to reliably infer turnover. Accounting for phantom species substantially improves our ability to accurately estimate local colonization and extinction rates, enhancing our ability to infer community dynamics and monitor long‐term ecological change.

Climate change aggravates bog species extinctions in the Black Forest (Germany)

“…The assessment of extinction events at the scale of hectares generally involves the difficulty that absences of every species have to be examined across the total area of every bog complex. Thus, even with the extensive data on which our study is based, past extinction events always pose a risk that species may have been overlooked (Beck et al., 2018). Thus, it is difficult to assess when exactly species that were absent in our last systematic survey from 2017 to 2020 went extinct.…”

More losses than gains during one century of plant biodiversity change in Germany

“…However, a careful quality control is a key prerequisite for this type of analysis 13 . In particular, aggregating changes across different communities by species rather than aggregating changes per plot requires much more attention for harmonising different taxonomies to prevent pseudo-turnover 21 . With appropriate care taken, plot time series of community data across larger regions should form a crucial backbone in future biodiversity monitoring.…”

“…While both studies detected an imbalance between losers and winners, it is difficult to ascertain biodiversity changes at regional large-grains, as resurveys at that spatial resolution often differ in sampling intensity from the initial surveys. In 5 kmgrid cells, species are easily overlooked, resulting in pseudo-turnover with erroneous gains or losses 21 . In contrast, small-grain vegetation-plot records, ranging from a few to several hundred square metres 22 , are usually thoroughly checked not only for species presences but also for absences.…”

sPlotOpen – An environmentally balanced, open‐access, global dataset of vegetation plots

“…Being spatially explicit, vegetation plots can be resurveyed through time to assess potential changes in plant species composition relative to a baseline (Perring et al., 2018; Staude et al., 2020; Steinbauer et al., 2018). As they normally contain information on the relative cover or abundance of each species, vegetation plots are also more appropriate for detecting biodiversity changes than data representing only the occurrence of individual species (Beck et al., 2018; Jandt et al., 2011).…”