Mapping large‐scale bird distributions using occupancy models and citizen data with spatially biased sampling effort. Issue 1 (11th September 2014)

• Record Type: Journal Article
• Authors: Higa, Motoki; Yamaura, Yuichi; Koizumi, Itsuro; Yabuhara, Yuki; Senzaki, Masayuki; Ono, Satoru
• Editors: Robertson, Mark
• Is Part Of: Diversity & distributions. Volume 21:Issue 1(2015:Jan.)
• Page Start: 46
• Page End: 54
• Publication Date: 2014-09-11
• Subjects: Imperfect detection -- Maxlike -- multispecies occupancy model -- opportunistic citizen data -- pseudo‐absence data -- spatially biased sampling effort; Biodiversity -- Periodicals; Biodiversity conservation -- Periodicals; 577
• DOI: 10.1111/ddi.12255
• Languages: English
• ISSNs: 1366-9516

Abstract

Abstract: Aim: Although data collected by citizen scientists have received a great deal of attention for assessing species distributions over large extents, their sampling efforts are usually spatially biased. We assessed whether the bias of spatially varied sampling effort for opportunistic citizen data can be corrected using occupancy models that incorporate observation processes. Location: Hokkaido Island, northern Japan. Methods: We applied occupancy models for citizen data with spatially biased sampling effort to model and map large‐scale distributions of 52 forest and 23 grassland/wetland bird species. We used estimated species richness (summed occupancy probabilities among the species) as the aggregated distributional patterns of each species group and compared them among two occupancy models (i.e. single‐species and multispecies occupancy models), two conventional logistic regression models and Maxlike, which do not explicitly deal with observation processes. Results: Conventional logistic regression models and Maxlike predicted inappropriate patterns, such as forest species preferring lowland non‐forested areas where most of the data were collected. Occupancy models, however, showed more appropriate results, indicating that forest species preferred lowland forested areas. The prediction by logistic models was somewhat improved by the use of spatially biased non‐detection data as the absence data; however, estimates of species richness were still much lower than those of occupancy models. Differences in model outputs were evident for the forest species but not for grassland/wetland species because citizen data covered virtually all environmental niches for grassland/wetland species. Results of the single‐species and multispecies occupancy models were nearly identical, but in some cases, estimates from the single‐species models were not converged or deviated notably from those of other species compared with estimates by the multispecies model. Main conclusions: We found that citizen data with spatially biased sampling effort can be appropriately utilized for large‐scale biodiversity distribution modelling with the use of occupancy models, which encourages data collection by citizen scientists.