![]() The reporting of methodology can also be streamlined and the repeatability and analysis of the research simplified as a body of robust approaches is developed. As use of a standardized methodology increases, the ability to then compare against the “experimental norm” will also increase, making unusual or aberrant results easier to detect. Further, a standard technique facilitates exploration of associated biases without the need to encompass a potentially infinite number of alternate designs. Long‐term or large spatial scale analyses often have considerable “noise” in the first place, so any reduction in this background variation is going to increase the chances of detecting ecological signals and reduce the need for complex analytical approaches (Niemelä et al. For example, collaborative research across larger global or temporal scales than is achievable for a single researcher becomes more straightforward. ![]() RIVPACS allows comparison of freshwater invertebrate assemblages and assessment of river health and relies on a standardized methodology where even details such as the dimension of the nets used to kick sample is controlled.Ī standardized methodology would deliver a number of benefits. One established example is River Invertebrate Prediction and Classification System (RIVPACS), which was designed specifically in response to the lack of comparability in the United Kingdom's National River Survey program during the 1970s (Centre for Ecology and Hydrology, 2015). In other cases, industrial standards are used to ensure large‐scale standardized methodology (e.g., Levan 2015). This has been more rarely achieved, but there do exist collaborative studies where the use of identical methodology has been used in order to tackle research questions at larger spatial scales (Niemelä et al. A second option is to adopt standardized methods for data collection. However, this approach is not without its own share of potential pitfalls (Gotelli and Colwell 2001). One solution to the difficulty of comparing between smaller research projects is to rely on statistical methods to control for between‐researcher idiosyncrasies, and approaches such as rarefaction have been used to allow comparison of species richness when sampling effort differs in this manner (Engemann et al. 2015) and is likely to become an emerging issue more widely. Lack of comparability across studies has recently been highlighted in other fields (Alivisatos et al. However, there exist significant difficulties in the analysis of long‐term and spatially large data, especially where the methodology between researchers differs (Gotelli and Colwell 2001, 2011). The importance of long‐term, standardized data collections has been highlighted in several recent publications (Fischer et al. This is especially relevant when this change concerns taxonomically difficult organisms (Peters et al. “Big data” generated from multiple researchers’ efforts is likely to become ever more important in unveiling the scope of biodiversity change. Ongoing loss of biodiversity is a global issue, necessitating investigation at multiple spatial and temporal scales (Magurran et al. Widespread adoption of more standardized methods and reporting would facilitate more nuanced analysis of biodiversity change. In addition, we provide a table to promote a more standardized reporting of the key methodological variables. We propose a standardized pitfall trap design for the study of ground‐active arthropods. However, our results show that, counterproductive to this goal, over the last 20 years there has little progress in reducing the methodological variation. ![]() ![]() There is a growing need for improved comparability between studies to facilitate the generation of large‐scale, long‐term biodiversity datasets. We report a decline in the completeness of methodological reporting over a 20‐year period, while there has been no clear reduction in the methodological variation between researchers using pitfall traps for arthropod sampling. Here, we present a meta‐analysis and description highlighting this variation in a common, widely used entomological survey technique. The ability to draw meaningful comparison across studies is severely hampered by extensive variation in the design of the sampling equipment and how it is used. To understand change in global biodiversity patterns requires large‐scale, long‐term monitoring.
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