Hide‐and‐seek in vegetation: time‐to‐detection is an efficient design for estimating detectability and occurrence

Abstract

Summary1. Ecology and conservation require reliable data on the occurrence of animals and plants. A major source of bias is imperfect detection, which, however, can be corrected for by estimation of detectability. In traditional occupancy models, this requires repeat or multi-observer surveys. Recently, time-to-detection models have been developed as a cost-effective alternative, which requires no repeat surveys and hence costs could be halved. 2. We compared the efficiency and reliability of time-to-detection and traditional occupancy models under varying survey effort. Two observers independently searched for 17 plant species in 44 100 m 2 Swiss grassland quadrats and recorded the time-to-detection for each species, enabling detectability to be estimated with both time-todetection and traditional occupancy models. In addition, we gauged the relative influence on detectability of species, observer, plant height and two measures of abundance (cover and frequency). 3. Estimates of detectability and occupancy under both models were very similar. Rare species were more likely to be overlooked; detectability was strongly affected by abundance. As a measure of abundance, frequency outperformed cover in its predictive power. The two observers differed significantly in their detection ability. 4. Time-to-detection models were as accurate as traditional occupancy models, but their data easier to obtain; thus they provide a cost-effective alternative to traditional occupancy models for detection-corrected estimation of occurrence.

Research design considerations to ensure detection of all species in an avian community

“…Similarly, time‐to‐detection analyses have shown that the effort required for detecting inconspicuous or rare species of plants is nearly four times greater than what experts predicted, and detectability should be explicitly modelled to ensure that adequate survey effort is applied (Bornand et al . ).…”

Factors influencing the detection of beach plastic debris

“…The assumption of perfect detection has been widely criticized in the monitoring of biological populations, and 50 numerous approaches have been developed to account for imperfect detection (Buckland et al, 2008;Kéry and Schaub, 2012;Nichols et al, 2009). For example, counts of mobile birds M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 4 and lizards depend on the observer, weather, habitat, and several other factors (Alldredge et al, 2007;Kéry et al, 2009;Schmidt et al, 2013), and even counts of sessile plants are 54 generally considered to be less than perfect and vary with substrate and observer experience (Bornand et al, 2014;Burg et al, 2015;Dufrêne et al, 2015). However, such effects have, to 56 our knowledge, not been considered in the majority of beach plastic studies (but see HidalgoRuz and Thiel, 2013).…”

“…Such data could be easily generated by at least 3-10 independent repeat counts from at least 25-50 distinct sites.258 While these approaches require a more stringent monitoring design and greater monitoring effort, the statistical framework is applied increasingly to large-scale citizen science datasetsIsaac et al, 2014; Tulloch et al, 2013; van Strien et al, 2013) and we envision that results from beach surveys could be analysed in a similar fashion to account for the imperfect 262 detection of plastic. Alternatively, more efficient monitoring designs that use the time to detection to estimate detection probability have proven useful in botanical surveys and may 264 reduce the number of observers required for robust monitoring(Bornand et al, 2014).However, an important consideration for the design of such surveys is the interval between 266 repeat surveys and between surveys that are used to estimate changes over time: the abundance of plastic on a beach is a function of accumulation over time, hence the interval 268 between sampling events will influence the abundance of plastic that is collected(Moreira et al, 2016; Ryan et al, 2014; Smith and Markic, 2013).270 Existing beach surveys and clean-up programmes that do not account for imperfect detection 272 underestimate the amount of plastic on beaches. For these existing datasets, or for monitoring programmes where designs or analyses accounting for imperfect detection are logistically274 impractical, the true amount of plastic could be coarsely extrapolated by using the detection probabilities estimated here.…”

Demographic modeling of citizen science data informs habitat preferences and population dynamics of recovering fishes

“…On one hand, the diversity of and participation in citizen science programs has grown rapidly over the last few decades (Miller-Rushing et al 2012). We also encourage further comparison of intensive (e.g., tagresighting) and opportunistic (e.g., citizen science) data types (Bornand et al 2014, Zipkin et al 2014a. 3.…”