Challenges for leveraging citizen science to support statistically robust monitoring programs-Reference-Cited by-同舟云学术

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Challenges for leveraging citizen science to support statistically robust monitoring programs

Published:2020-02 Issue: Volume:242 Page:108411
ISSN:0006-3207
Container-title:Biological Conservation
language:en
Short-container-title:Biological Conservation

Author:

Weiser Emily L.,Diffendorfer Jay E.,Lopez-Hoffman Laura,Semmens Darius,Thogmartin Wayne E.

Funder

U.S. Geological Survey

Publisher

Elsevier BV

Subject

Nature and Landscape Conservation,Ecology, Evolution, Behavior and Systematics

Reference59 articles.

1. Fitting linear mixed-effects models using lme4;Bates;J. Stat. Softw.,2015

2. Statistical solutions for error and bias in global citizen science datasets;Bird;Biol. Conserv.,2014

3. A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables;Brus;Environ. Monit. Assess.,2003

4. The Integrated Monarch Monitoring Program: from design to implementation;Cariveau;Front. Ecol. Evol.,2019

5. Confronting preferential sampling when analysing population distributions: diagnosis and model-based triage;Conn;Methods Ecol. Evol.,2017

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1. Successful citizen science tools to monitor animal populations require innovation and communication: SealSpotter as a case study;Frontiers in Conservation Science;2024-09-06

2. Differences in fisher demographics and fishing behaviour from concurrent phone‐recall and smartphone app surveys of recreational angling in south‐western Australia;Fisheries Management and Ecology;2024-08-28

3. Drivers of temporal bias in biodiversity recording by citizen scientists;2024-07-23

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