Abstract
AbstractAimTo evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high‐quality absences from OR based on questionnaires sent to observers.LocationBoth datasets cover the same large area (Sweden) and time period (2000–2013).MethodsSpecies distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data was assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR.ResultsFor all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species–environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found.Main conclusionsRelatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist.
Highlights
Habitat suitability models are important tools to predict species' distributions (Elith & Leathwick, 2009), and for conservation and management (Franklin, 2013; Lawler, Wiersma, & Huettmann, 2011)
For the rarest species, the model based on data from opportunistic reporting (OR) was even better than the one based on systematic protocol (SP) at predicting presences and absences in the SP data (Figure S6.1)
Our study provides evidence that habitat suitability models from OR can provide similar predictions of habitat suitability as models from SP, for multiple species with varied characteristics, ecological requirements and observation biases
Summary
Habitat suitability models are important tools to predict species' distributions (Elith & Leathwick, 2009), and for conservation and management (Franklin, 2013; Lawler, Wiersma, & Huettmann, 2011) These models require occurrence data collected across a variety of habitat types and covering a broad spatial extent (Bonney et al, 2009). Citizen science data have been successfully used to improve knowledge in many research areas such as mapping the distribution of invasive species (Delaney, Sperling, Adams, & Leung, 2008), predicting seasonal dynamics of pathogens (Altizer, Hochachka, & Dhondt, 2004) or assessing the effect of the environment on breeding success (Rosenberg, Lowe, & Dhondt, 1999) These data can be used to model species’ habitat suitability or to describe population trends and range change (Kery et al, 2010; Mair, Harrison, Räty, et al, 2017; Snäll, Kindvall, Nilsson, & Pärt, 2011). We investigated whether the observed differences between models based on OR and SP can be explained by potential sampling bias between habitat types
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