Plants stand still but hide: imperfect and heterogeneous detection is the rule when counting plants

Abstract

1. The estimation of population size and its variation largely relies on counts of individuals, generally carried out within spatial units such as quadrats or sites. Missing individuals during counting (i.e. imperfect detection) results in biased estimates of population size and trends. Imperfect detection has been shown to be the rule in animal studies, and most studies now correct for this bias by estimating detection probability. Yet this correction remains exceptional in plant studies, suggesting that most plant ecologists implicitly assume that all individuals are always detected. 2. To assess if this assumption is valid, we conducted a field experiment to estimate individual detection probability in plant counts conducted in 1x1 m quadrats. We selected 30 herbaceous plant species along a gradient of conspicuousness at 24 sites along a gradient of habitat closure, and asked groups of observers to count individuals in 10 quadrats using three counting methods requiring progressively increasing times to complete (quick count, unlimited count and cell count). In total, 158 participants took part in the experiment, allowing an analysis of the results of 5,024 counts. 3. Over all field sessions, no observer succeeded in detecting all the individuals in the 10 quadrats. The mean detection rate was 0.44 (ranging from 0.11 to 0.82) for the quick count, 0.59 for the unlimited count (range 0.18–0.87) and 0.74 for the cell count (range 0.46–0.94). 4. Detection probability increased with the conspicuousness of the target species and decreased with the density of individuals and habitat closure. The observer′s experience in botany had little effect on detection probability, whereas detection was strongly affected by the time observers spent counting. Yet although the more time–consuming methods increased detection probability, none achieved perfect detection, nor did they reduce the effect on detection probability of the variables we measured. 5. Synthesis and applications. Given that plant detection was always imperfect and highly heterogeneous, rather than rely on raw counts, plant ecologists should use methods that estimate the detection probability of individuals to assess the size, temporal or spatial trends of plant populations.

Occupancy model reveals limited detectability of lichens in a standardised large‐scale monitoring

“…Occupancy models, by contrast, do both, which is why they have become so popular (Guillera-Arroita, 2017). Like other authors (Chen et al, 2009(Chen et al, , 2013Casanovas et al, 2014;Middleton & Vining, 2022;Perret et al, 2023),…”

“…Occupancy models, by contrast, do both, which is why they have become so popular (Guillera‐Arroita, 2017). Like other authors (Chen et al., 2009, 2013; Casanovas et al., 2014; Middleton & Vining, 2022; Perret et al., 2023), we believe that we do not yet realise the full potential of occupancy models in studies of sessile species. We acknowledge that occupancy models require slightly more data than other species distribution models (at least two visits on at least a subset of the sites) and some experience in model fitting.…”

“…Although the sampled area per site was relatively large (500 m 2 ), we had hoped that the generous allowance of survey time would have counter‐balanced this less‐favourable aspect. Instead, the estimated detection probabilities in this study lie very much within reported detection probabilities of mobile organisms like birds (0.26–0.83 in Tyre et al., 2003; 0.03–0.99 in Kéry & Schmidt, 2008; 0.48–0.60 in Bailey et al., 2009) or amphibians (0.34–0.51 in Tyre et al., 2003; 0.33–0.62 in Bailey et al., 2009) and sessile organisms like plants (0.26–0.57 in Al‐Chokhachy et al., 2013; 0.44–0.74 in Perret et al., 2023), and they were lower than those reported for wood‐dwelling fungi from sites of similar size (0.79–0.94 in Moor et al., 2020).…”

“…This study adds another piece to the growing body of evidence that also sessile organisms are far from being perfectly detected (Kéry & Gregg, 2003; Cáceres et al., 2008; Garrard et al., 2008, 2013; Chen et al., 2009, 2013; Vittoz et al., 2010; Casanovas et al., 2014; Berberich et al., 2016; Morrison, 2016; Vondrák et al., 2016; Brown et al., 2017; Dennett et al., 2018; Dennett & Nielsen, 2019; Iles et al., 2019; Middleton & Vining, 2022; Perret et al., 2023). Yet, detection probability is not commonly accounted for in analyses of occurrence or abundance data from plants, lichens and other sessile organisms.…”

Rapid on‐ground assessment after the 2019–2020 megafires reveals new information on rare and threatened plants in northern New South Wales, Australia

“…Plants sometimes lack key taxonomic features needed for confident in situ identification, making field experience valuable, particularly for recognizing non‐reproductive individuals, such as seedlings or adults outside their flowering seasons. However, the ‘botanist effect’, where experts are assumed to detect more species than non‐experts (Ahrends et al., 2011), may be context‐dependent and could be more influenced by observation time or a surveyor's familiarity with the ecosystem (Morrison, 2016; Perret et al., 2023). To enhance detection rates and efficiency, future post‐disturbance surveys would ideally require participation by expert botanists, either as survey leaders or as trainers for other observers and there is scope for further work on exploring how expertise influences detection rates in these unique systems (e.g., McCarthy et al., 2013).…”