Using balanced acceptance sampling as a master sample for environmental surveys

Abstract

Well‐designed environmental monitoring programmes for management organisations are important for evidence‐based decision making. However, many environmental problems are not single agency issues that require intervention or monitoring at one spatial scale. A master sample can be used to coordinate and scale monitoring designs to ensure consistency in information gathered and robustness of estimators at the different spatial scales. We propose using balanced acceptance sampling (BAS) to generate a master sample. In this context, practical applications and justification of BAS as a master sample are addressed. These include sample generation, stratification, unequal probability sampling, rotating panel designs, and regional intensification. A method for incorporating legacy sites is also provided. Using BAS as a master sample is conceptually simple, gives good spatial balance over different spatial scales, and is computationally efficient to generate. An example for terrestrial biodiversity monitoring in New Zealand is provided. Environmental monitoring can benefit from increased coordination between agencies. A master sample is an excellent way to incorporate coordination directly into the sample design. BAS improves on methods previously described and provides an effective method to monitor populations at multiple spatial scales.

Phylogenetic restriction of plant invasion in drought‐stressed environments: Implications for insect‐pollinated plant communities in water‐limited ecosystems

“…To sample floral resource availability (FRA), we randomly distributed 6-8 (2 × 15 m) belt transects throughout each site, using balanced acceptance sampling methods to ensure a random yet spatially balanced distribution of transects (van Dam- Bates et al, 2018).…”

Stocktaking the environmental coverage of a continental ecosystem observation network

“…GRTS provides a compromise between spatial evenness and independence (Stevens and Olsen 2004). A master sample can be prepared in advance from which field sites are measured (van Dam-Bates et al 2018).…”

“…The strategic placement of a limited number of field plots could be implemented in many different ways, ranging between the extremes of incautious practicality and statistical purism (Roleček et al 2007, Hoffman et al 2013). Survey and monitoring networks have been established, for example, using systematic grid (Messer et al 1991, Goring et al 2016), stratified random (Michaelsen et al 1994, Danz et al 2005, Carvalho et al 2016, Hoekman et al 2017, van Etten and Fox 2017), gradsect (Austin and Heyligers 1989, Wessels et al 1998), and generalized random‐tessellation stratified (GRTS; Larsen et al 2008, McCord et al 2017, van Dam‐Bates et al 2018) designs. While purely random surveys are more statistically robust, they require a larger sample size and have been shown to be less effective at capturing ecological diversity, which is typically structured along multiple environmental gradients (Austin and Heyligers 1989, Wessels et al 1998, Roleček et al 2007, Michalcová et al 2011, Carvalho et al 2016, Caddy‐Retalic et al 2017).…”

Spatially Balanced Sampling with Local Ranking

“…Another group of methods apply a local repulsion strategy to ensure well-spread samples are drawn (Grafström 2011;Grafström et al 2012;Grafström and Tillé 2013;Grafström and Matei 2018). SB designs based on the Halton sequence (Halton 1960) have been proposed (Robertson et al 2013(Robertson et al , 2017van Dam-Bates et al 2018;Robertson et al 2018. The Halton sequence is quasi-random and generates evenly spread points with similar spatial properties to a regular lattice; however, unlike a regular lattice, points can be added incrementally with no clumping of points.…”