Model‐based integration of citizen science data from disparate sources increases the precision of bird population trends

Abstract

Aim: Timely and accurate information on population trends is a prerequisite for effective biodiversity conservation. Structured biodiversity monitoring programmes have been shown to track population trends reliably, but require large financial and time investment. The data assembled in a large and growing number of online databases are less structured and suffer from bias, but the number of observations is much higher compared to structured monitoring programmes. Model-based integration of data from these disparate sources could capitalize on their respective strengths. Location: Germany.Methods: Abundance data for 26 farmland bird species were gathered from the standardized Common Breeding Bird Survey (CBBS) and three online databases that varied with regard to their degree of survey standardization. Population trends were estimated with a benchmark model that included only CBBS data, and five Bayesian hierarchical models integrating all data sources in different combinations. Across models, we compared consistency and precision of the predicted population trends and the accuracy of the models. Bird species body mass, prevalence in the dataset and abundance were tested as potential predictors of the explored quantities.Results: Consistency in predicted annual abundance indices was generally high especially when comparing the benchmark models to the integrated models without unstructured data. The accuracy of the estimated population changes was higher in the hierarchical models compared to the benchmark model but this was not related to data integration. Precision of the predicted population trends increased as more data sources were integrated. Main conclusions:Model-based integration of data from different sources can lead to improved precision of bird population trend estimates. This opens up new opportunities for conservation managers to identify declining populations earlier. Integrating data from online databases could substantially increase sample size and thus allowing to derive trends for currently not well-monitored species, especially at sub-national scales.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Effects of input data sources on species distribution model predictions across species with different distributional ranges

“…Although both are valuable for addressing a wide range of socio-ecological research questions and biological modelling and monitoring approaches (Chandler et al, 2017;Neate-Clegg et al, 2020), doubts still exist about data quality from citizen science surveys (Aubry et al, 2017;Jiménez et al, 2019). The data combination from different sources also proved to be a useful tool in ecological modelling (Miller et al, 2019;Suhaimi et al, 2021), specifically to improve estimates of species distributions (Isaac et al, 2020;Robinson et al, 2020), abundance and population trends (Boersch-Supan et al, 2019;Hertzog et al, 2021). However, there is still much to explore on the data quality (Johnston et al, 2019;Van Eupen et al, 2021) and usefulness of each input data source through the modelling process (Kobori et al, 2016;Kosmala et al, 2016), as well as the uncertainty hosted in each modelling step should also be controlled more exhaustively.…”

The Decline of Common Birds Exemplified by the Western Jackdaw Warns on Strong Environmental Degradation

“…Absolute abundance surveys are especially informative for species where the number of breeding pairs can be adequately accounted for or individuals gather at communal roosts, provided that counts can be done over a large enough area to be representative. However, although a combination of sampling methods may be the most appropriate for assessing population trends, simultaneous information on the relative and absolute abundance of a species is rarely available [31,35,38].…”

Integrating presence-only and presence-absence data to model changes in species geographic ranges: An example of yaguarundí in Latin America

“…Despite the great promise of IDMs, their applications are still limited. Studies have used IDMs to address a wide range of data integration problems (e.g., (Martino et al, 2021;Rose et al, 2020;Schank et al, 2017;Zulian et al, 2021), but they have mostly been used over local (Farr et al, 2021) or nationwide (Hertzog et al, 2021) extents, and at fine grains, but not to model entire geographic ranges of species over coarse grains. As an exception, Zulian et al, (2021) used data integration to model the full geographic distribution of a parrot species endemic to the tropical South American Atlantic Forest.…”

“…As an exception, Zulian et al, (2021) used data integration to model the full geographic distribution of a parrot species endemic to the tropical South American Atlantic Forest. Further, with some exceptions (Doser et al, 2022;Hertzog et al, 2021;Pagel et al, 2014), IDMs have not been used to model temporal change of distributions, although this could be their obvious application, given the scarcity of temporally replicated data. Finally, IDMs can appear complex, with a lack of user-friendly tools available; thus, their implementation can be challenging, particularly for inexperienced users.…”