Non-detection during excursions by citizen scientists modeled as a function of weather, season, list length, and individual preferences
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INTRODUCTION
Citizen science is an increasingly valuable source of information about biodiversity. It is challenging to use this information for analysis of distribution and trends. The lack of a protocol leads to bias in observations and therefore data are not representative. The bias is a consequence of unequal detection probabilities, caused by different preferences and habits of citizen scientists.
METHODS
We propose to incorporate characteristics of these excursions in analyses of data collected by citizen scientists to improve estimates of the probability that a species is not detected and reported, even though it does occur. By limiting these models to areas that are known to be occupied, detection can be modeled separately without considering variation in occupancy. We apply this idea to 150 common species in the Southwest Delta of The Netherlands, and illustrate the data selection, the modeling process and the results using four species.
RESULTS
The strongest features to predict detection are the number of species during a visit (list length), earlier observations of the target species by the same observer, and the day of year. We compare three approaches to predict the total non-detection probability that takes all visits to an area into account. Predictions based on only the number of visits were outperformed by predictions that also take the list length into account. Our predictions based on all features combined consistently beat both other approaches, across all 10 species groups that were compared.
DISCUSSION
We thus show that explicitly modelling the characteristics of all visits to an occupied area results in estimation of non-detection probabilities, while providing insight into the causes of detection and reporting bias. Furthermore, predictions of our model provide a basis for quantifying the sampling effort in each area, which is a promising first step to correct bias in citizen science data when aiming to map a species’ distribution.
