To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 5, we selected ‘Statistics for citizen science: extracting signals of change from noisy ecological data‘ by Isaac et al. (2014) and the authors looked back on their article and how the field of citizen science has changed since.
In this Editor’s Choice, Res Altwegg, our Associate Editor with expertise in citizen science, shares his favourite MEE papers in the field of citizen science and beyond.
Res Altwegg, University of Cape Town
Citizen science data play an important role in research and monitoring. Citizen scientists can typically collect large amounts of data over large spatial and temporal extents. Citizen science data challenge data analysts and inspire statisticians. One challenge with analysing citizen science data is that variable observer skills often introduce heterogeneity into the data. Ratnieks et al. (2016) demonstrated the effectiveness of observer training in reducing such heterogeneity and Johnston et al. (2018) showed how observer experience can be directly incorporated into the analysis. Citizen scientists tend to sample preferentially at certain locations resulting in spatial sampling biases. Conn et al. (2017) present a particularly clear overview of the issue and offer some analytical solutions.
Citizen science data are often used for modelling species’ distributions and MEE has published some landmark papers about species distribution models, e.g. Elith et al.’s (2010) classic “The art of modelling range-shifting species” paper and Pagel et al’s (2014) paper on quantifying population trends from local surveys and widespread opportunistic data. Another milestone was the insight that species distributions can be modelled using the framework of spatial point processes (Fithian et al., 2015; Renner et al., 2015), which opened the door for exciting new methods that combine information from different sources into a single analysis (Koshkina et al., 2017; Miller et al., 2019; Renner et al., 2019).
One popular method for analysing distribution data is MAXENT and MEE hosted a lively debate about the use and misuse of this method (Yackulic et al., 2013; Guillera-Arroita et al., 2014; Merow & Silander, 2014) which offered practical guidelines (Merow, Smith, & Silander, 2013) and alternatives (Royle et al. 2012).
Citizen science data have also been critical for studies of phenology, i.e. the timing of ecological seasonal phenomena, and MEE has published some important statistical methods (Bishop et al., 2013; Dennis et al., 2013; Chambert et al., 2015).
When designing citizen science programmes or analysing data from such programmes, one needs to think about the relative information content of different data collection schemes (Munson et al., 2010). One of my favourite MEE papers is Canessa et al.’s (2015) paper on calculating the value of information.
Find out about the Methods in Ecology and Evolution articles selected to celebrate Volumes 1-6:
10th Anniversary Volume 1: The Art of Modelling Range-Shifting Species
10th Anniversary Volume 3: paleotree: A Retrospective
10th Anniversary Volume 3: Editor’s Choice
10th Anniversary Volume 5: Extracting Signals of Change from Noisy Ecological Data
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