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Habitat suitability models based on opportunistic citizen science data: Evaluating forecasts from alternative methods versus an individual-based model

Bradter, Ute and Ozgul, Arpat and Griesser, Michael and Layton-Matthews, Kate and Eggers, Jeannette and Singer, Alexander and Sandercock, Brett K. and Haverkamp, Paul J. and Snäll, Tord (2021). Habitat suitability models based on opportunistic citizen science data: Evaluating forecasts from alternative methods versus an individual-based model. Diversity and Distributions. 27 , 2397-2411
[Research article]

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Abstract

Aim To evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individual-based population dynamics. Location Sweden. Methods We evaluated whether alternative methods for building habitat suitability models (HSMs) based on opportunistic data from citizen science programmes produced forecasts that were consistent with forecasts from two benchmark models: (1) a HSM based on data from systematic monitoring and (2) an individual-based model for spatially explicit population dynamics based on empirical demographic and movement data. We forecasted population numbers and habitat suitability for three realistic, future forest landscapes for a forest bird, the Siberian jay (Perisoreus infaustus). We ranked simulated forest landscapes with respect to their benefits to Siberian jays for each modelling method and compared the agreement of the rankings among methods. Results Forecasts based on our two benchmark models were consistent with each other and with expectations based on the species' ecology. Forecasts from logistic regression models based on opportunistic data were consistent with the benchmark models if species detections were combined with high-quality inferred absences derived via retrospective interviews with experienced "super-reporters." In contrast, forecasts with three other widely used methods were inconsistent with the benchmark models, sometimes with misleading rankings of future scenarios. Main conclusions Our critical evaluation of alternative HSMs against a spatially explicit IBM demonstrates that information on species absences critically improves forecasts of species distributions using opportunistic data from citizen science programmes. Moreover, high-quality information on species absences can be retrospectively inferred from surveys of the consistency of reporting of individual species and the identification skills of participating reporters. We recommend that citizen science projects incorporate procedures to evaluate reporting behaviour. Inferred absences may be especially useful for improving forecasts for species and regions poorly covered by systematic monitoring schemes.

Authors/Creators:Bradter, Ute and Ozgul, Arpat and Griesser, Michael and Layton-Matthews, Kate and Eggers, Jeannette and Singer, Alexander and Sandercock, Brett K. and Haverkamp, Paul J. and Snäll, Tord
Title:Habitat suitability models based on opportunistic citizen science data: Evaluating forecasts from alternative methods versus an individual-based model
Series Name/Journal:Diversity and Distributions
Year of publishing :2021
Volume:27
Page range:2397-2411
Number of Pages:15
Publisher:WILEY
ISSN:1366-9516
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Ecology
Keywords:citizen science, forecast, habitat suitability, individual-based model, inferred absence, opportunistically collected, presence-only, Siberian jay
URN:NBN:urn:nbn:se:slu:epsilon-p-113678
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-113678
Additional ID:
Type of IDID
DOI10.1111/ddi.13409
Web of Science (WoS)000695990100001
ID Code:26271
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
S - Faculty of Forest Sciences
Department:(NL, NJ) > Swedish Species Information Centre
(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:06 Dec 2021 10:25
Metadata Last Modified:23 Feb 2022 21:20

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