Tiago, P. | Search bioRxiv

bioRxiv2024

Drivers of temporal bias in biodiversity recording by citizen scientists

O_LICitizen science data is increasingly important for ecological research, biodiversity conservation and monitoring. However, these data often suffer from biases due to uneven recording efforts by citizen scientists. Biases caused by intra-annual differences in recording activity can be particularly severe, hindering the use of citizen science data in research areas such as population dynamics and phenology. Therefore, understanding the factors driving recording activity is essential. C_LIO_LIIn this study, we provide a detailed assessment of how weather and calendar-related factors influence biodiversity recording activity by citizen scientists at a daily resolution. To perform this, we analyse the recording patterns for six tree species in the Iberian Peninsula, which maintain a fairly consistent appearance throughout the year. Observation data were collected from iNaturalist, a leading platform for citizen-science data. We used boosted regression trees (BRT) to compare observed recording activity patterns with those expected by chance. Our analysis included a comprehensive set of explanatory variables, such as day of the week, month, holidays, temperature, accumulated precipitation, wind intensity, and snow depth. C_LIO_LIThe BRT models effectively identified the drivers of recording activity, with the correlation between predicted and observed temporal patterns (left out of model training) ranging from 0.47 to 0.96, depending on the species. The day of the week, daily temperature, and month of the year consistently emerged as the main drivers. Recording activity was higher on weekends, to some extent on Fridays, and during the spring months. Extreme low and high temperatures generally correlated with lower recording activity, although there were exceptions. Wind speed and precipitation had a moderate influence, with higher wind intensity and accumulated precipitation leading to decreased activity. Holidays and accumulated snow had very minor relevance across species. C_LIO_LIOur findings show that citizen scientists record more frequently on weekends, during mild weather, and in spring. By addressing these biases, we can maximize the utility of citizen-collected data for research and applied purposes, ensuring robust and reliable conclusions that enhance ecological understanding and conservation efforts. C_LI

ecology↗

bioRxiv2023

Predicting the timing of ecological phenomena across regions using citizen science data

Spatial predictions of intra-annual ecological variation enhance ecological understanding and inform decision-making. Unfortunately, it is often challenging to use statistical or machine learning techniques to make such predictions, due to the scarcity of systematic, long-term observational data. Conversely, opportunistic time-stamped observation records, supported by highly informative data such as photographs, are increasingly available for diverse ecological phenomena in many regions. However, a general framework for predicting such phenomena using opportunistic data remains elusive. Here, we introduce a novel framework that leverages the concept of relative phenological niche to model observation records as a sample of temporal environmental conditions in which the represented ecological phenomenon occurs. We demonstrate its application using two distinct, management-relevant, ecological events: the emergence of the adult stage of the invasive Japanese beetle (Popillia japonica), and of fruiting bodies of the winter chanterelle mushroom (Craterellus tubaeformis). The framework accounts for spatial and temporal biases in observation data, and it contrasts the temporal environmental conditions (e.g., in temperature, precipitation, wind speed, etc.) associated with the observation of these events to those available in their occurrence locations. To discriminate between the two sets of conditions, we employ machine-learning algorithms (boosted regression trees and random forests). The proposed approach can accurately predict the temporal dynamics of ecological events across large geographical scales. Specifically, it successfully predicted the intra-annual timing of occurrence of adult Japanese beetles and of winter chanterelle mushrooms across Europe and North America. We further validate the approach by successfully predicting the timing of occurrence of adult Japanese beetles in Northern Italy, a recent hotspot of invasion in continental Europe, and the winter chanterelle mushroom in Denmark, a country with a high number of records of this mushroom. These results were also largely insensitive to temporal bias in recording effort. Our results highlight the potential of opportunistic observation data to predict the temporal variation of a wide range of ecological phenomena in near real-time. Furthermore, the conceptual and methodological framework is intuitive and easily applicable for the large number of ecologists already using machine-learning and statistical-based predictive approaches.

ecology↗