Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions

[1]  Steve Kelling,et al.  Estimates of observer expertise improve species distributions from citizen science data , 2018 .

[2]  Jane Elith,et al.  blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models , 2018, bioRxiv.

[3]  Stephen R. Baillie,et al.  Estimating species distributions from spatially biased citizen science data , 2020 .

[4]  Helen E Roy,et al.  The diversity and evolution of ecological and environmental citizen science , 2017, PloS one.

[5]  Ayesha I. T. Tulloch,et al.  A behavioural ecology approach to understand volunteer surveying for citizen science datasets , 2012 .

[6]  Alejandro Ruete,et al.  Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa , 2016, PloS one.

[7]  R. T. Brumfield,et al.  Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae) , 2017, Evolution; international journal of organic evolution.

[8]  Arco J. van Strien,et al.  Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models , 2013 .

[9]  J. K. Legind,et al.  Contribution of citizen science towards international biodiversity monitoring , 2017 .

[10]  R. Meentemeyer,et al.  Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions? , 2009 .

[11]  S. Newson,et al.  A novel citizen science approach for large-scale standardised monitoring of bat activity and distribution, evaluated in eastern England , 2015 .

[12]  Duane R. Diefenbach,et al.  INCORPORATING AVAILABILITY FOR DETECTION IN ESTIMATES OF BIRD ABUNDANCE , 2007 .

[13]  F. Olmos,et al.  Observation of Diurnal Soaring Raptors In Northeastern Brazil Depends On Weather Conditions and Time of Day , 2018, Journal of Raptor Research.

[14]  Kevin Crowston,et al.  From Conservation to Crowdsourcing: A Typology of Citizen Science , 2011, 2011 44th Hawaii International Conference on System Sciences.

[15]  David B. Roy,et al.  Statistics for citizen science: extracting signals of change from noisy ecological data , 2014 .

[16]  Simon N. Wood,et al.  Shape constrained additive models , 2015, Stat. Comput..

[17]  Wesley M. Hochachka,et al.  Sources of Variation in Singing Probability of Florida Grasshopper Sparrows, and Implications for Design and Analysis of Auditory Surveys , 2009 .

[18]  Marshall J. Iliff,et al.  Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves? , 2015, PloS one.

[19]  Wolfgang Schwanghart,et al.  Spatial bias in the GBIF database and its effect on modeling species' geographic distributions , 2014, Ecol. Informatics.

[20]  W. Hochachka,et al.  Regional variation in the impacts of the COVID-19 pandemic on the quantity and quality of data collected by the project eBird , 2021, Biological Conservation.

[21]  B. Erasmus,et al.  Geographic sampling bias in the South African Frog Atlas Project: implications for conservation planning , 2010, Biodiversity and Conservation.

[22]  James E. Hines,et al.  Accounting for false positives improves estimates of occupancy from key informant interviews , 2014 .

[23]  Johannes Kamp,et al.  Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark , 2016 .

[24]  R. Fuller,et al.  Estimating numbers of birds by point counts: how long should counts last? , 1984 .

[25]  Tomas J. Bird,et al.  Statistical solutions for error and bias in global citizen science datasets , 2014 .

[26]  Richard B. Chandler,et al.  unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance , 2011 .

[27]  R. Kadmon,et al.  EFFECT OF ROADSIDE BIAS ON THE ACCURACY OF PREDICTIVE MAPS PRODUCED BY BIOCLIMATIC MODELS , 2004 .

[28]  Thomas G. Dietterich,et al.  The eBird enterprise: An integrated approach to development and application of citizen science , 2014 .

[29]  R. Dennis,et al.  Bias in Butterfly Distribution Maps: The Influence of Hot Spots and Recorder's Home Range , 2000, Journal of Insect Conservation.

[30]  Z. Huaman,et al.  Assessing the Geographic Representativeness of Genebank Collections: the Case of Bolivian Wild Potatoes , 2000, Conservation biology : the journal of the Society for Conservation Biology.

[31]  Murray Ellis,et al.  Effects of weather, time of day, and survey effort on estimates of species richness in temperate woodlands , 2018 .

[32]  Damien R Farine,et al.  Temporal activity patterns of predators and prey across broad geographic scales , 2018, Behavioral Ecology.

[33]  M. Kéry,et al.  A resampling-based method for effort correction in abundance trend analyses from opportunistic biological records , 2014 .

[34]  Steve Kelling,et al.  Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity , 2019, Bioscience.

[35]  P. Grandcolas,et al.  Taxonomic bias in biodiversity data and societal preferences , 2017, Scientific Reports.

[36]  W. Link,et al.  The first 50 years of the North American Breeding Bird Survey , 2017, The Condor.

[37]  James D Nichols,et al.  Modeling false positive detections in species occurrence data under different study designs. , 2015, Ecology.

[38]  Mark Hill,et al.  Local frequency as a key to interpreting species occurrence data when recording effort is not known , 2012 .

[39]  Heather J. Lynch,et al.  Using citizen science to estimate lichen diversity , 2014 .

[40]  Krishna Pacifici,et al.  The recent past and promising future for data integration methods to estimate species’ distributions , 2019, Methods in Ecology and Evolution.

[41]  Mark G. Meekan,et al.  Acoustic Telemetry Validates a Citizen Science Approach for Monitoring Sharks on Coral Reefs , 2014, PloS one.

[42]  J. Lobo,et al.  How well does presence‐only‐based species distribution modelling predict assemblage diversity? A case study of the Tenerife flora , 2011 .

[43]  M. T. Murphy,et al.  Follow the rain? Environmental drivers of Tyrannus migration across the New World , 2018, The Auk.

[44]  David A. W. Miller,et al.  Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification. , 2011, Ecology.

[45]  E. Howard,et al.  Citizen Science Observations of Monarch Butterfly Overwintering in the Southern United States , 2010 .

[46]  I. Koizumi,et al.  Mapping large‐scale bird distributions using occupancy models and citizen data with spatially biased sampling effort , 2015 .

[47]  Krishna Pacifici,et al.  Integrating multiple data sources in species distribution modeling: a framework for data fusion. , 2017, Ecology.

[48]  James D. Nichols,et al.  Occupancy models for citizen‐science data , 2019, Methods in Ecology and Evolution.

[49]  Laura López-Hoffman,et al.  Recreation economics to inform migratory species conservation: Case study of the northern pintail. , 2018, Journal of environmental management.

[50]  Javier Otegui,et al.  Increasing phenological asynchrony between spring green-up and arrival of migratory birds , 2017, Scientific Reports.

[51]  Andreas Ziegler,et al.  ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.

[52]  Hugh P. Possingham,et al.  Realising the full potential of citizen science monitoring programs , 2013 .