references_ebird.bib

@article{arasumani2019,
  title = {Invasion Compounds an Ecosystem-Wide Loss to Afforestation in the Tropical Grasslands of the {{Shola Sky Islands}}},
  author = {Arasumani, M. and Khan, Danish and Vishnudas, C.K. and Muthukumar, M. and Bunyan, Milind and Robin, V.V.},
  year = {2019},
  month = feb,
  volume = {230},
  pages = {141--150},
  issn = {00063207},
  doi = {10.1016/j.biocon.2018.12.019},
  journal = {Biological Conservation},
  language = {en}
}

@book{burnham2002a,
  title = {Model {{Selection}} and {{Multimodel Inference}}: {{A Practical Information}}-{{Theoretic Approach}}},
  shorttitle = {Model {{Selection}} and {{Multimodel Inference}}},
  author = {Burnham, Kenneth P. and Anderson, David R.},
  year = {2002},
  edition = {Second},
  publisher = {{Springer-Verlag}},
  address = {{New York}},
  doi = {10.1007/b97636},
  abstract = {We wrote this book to introduce graduate students and research workers in various scienti?c disciplines to the use of information-theoretic approaches in the analysis of empirical data. These methods allow the data-based selection of a ``best'' model and a ranking and weighting of the remaining models in a pre-de?ned set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book. The second edition was prepared with three goals in mind. First, we have tried to improve the presentation of the material. Boxes now highlight ess- tial expressions and points. Some reorganization has been done to improve the ?ow of concepts, and a new chapter has been added. Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6. Third, new technical material has been added to Chapters 5 and 6. Well over 100 new references to the technical literature are given. These changes result primarily from our experiences while giving several seminars, workshops, and graduate courses on material in the ?rst e- tion.},
  file = {/home/pratik/Zotero/storage/3K4W2NXY/Burnham and Anderson - 2002 - Model Selection and Multimodel Inference A Practi.pdf;/home/pratik/Zotero/storage/KX23EVIB/9780387953649.html},
  isbn = {978-0-387-95364-9},
  language = {en}
}

@article{burnham2011,
  title = {{{AIC}} Model Selection and Multimodel Inference in Behavioral Ecology: Some Background, Observations, and Comparisons},
  shorttitle = {{{AIC}} Model Selection and Multimodel Inference in Behavioral Ecology},
  author = {Burnham, Kenneth P. and Anderson, David R. and Huyvaert, Kathryn P.},
  year = {2011},
  month = jan,
  volume = {65},
  pages = {23--35},
  issn = {0340-5443, 1432-0762},
  doi = {10.1007/s00265-010-1029-6},
  journal = {Behavioral Ecology and Sociobiology},
  language = {en},
  number = {1}
}

@article{elsen2017,
  title = {The Role of Competition, Ecotones, and Temperature in the Elevational Distribution of {{Himalayan}} Birds},
  author = {Elsen, Paul R. and Tingley, Morgan W. and Kalyanaraman, Ramnarayan and Ramesh, Krishnamurthy and Wilcove, David S.},
  year = {2017},
  month = feb,
  volume = {98},
  pages = {337--348},
  issn = {00129658},
  doi = {10.1002/ecy.1669},
  file = {/home/pratik/Zotero/storage/T8SNRK46/Elsen et al. - 2017 - The role of competition, ecotones, and temperature.pdf},
  journal = {Ecology},
  language = {en},
  number = {2}
}

@article{fiske2011,
  title = {{\textbf{Unmarked}} : {{An}} {{{\emph{R}}}} {{Package}} for {{Fitting Hierarchical Models}} of {{Wildlife Occurrence}} and {{Abundance}}},
  shorttitle = {{\textbf{Unmarked}}},
  author = {Fiske, Ian and Chandler, Richard},
  year = {2011},
  volume = {43},
  issn = {1548-7660},
  doi = {10.18637/jss.v043.i10},
  file = {/home/pratik/Zotero/storage/68GAMCZE/Fiske and Chandler - 2011 - unmarked  An R Package for Fitting .pdf},
  journal = {Journal of Statistical Software},
  language = {en},
  number = {10}
}

@article{johnston2018,
  title = {Estimates of Observer Expertise Improve Species Distributions from Citizen Science Data},
  author = {Johnston, Alison and Fink, Daniel and Hochachka, Wesley M. and Kelling, Steve},
  editor = {Isaac, Nick},
  year = {2018},
  month = jan,
  volume = {9},
  pages = {88--97},
  issn = {2041-210X, 2041-210X},
  doi = {10.1111/2041-210X.12838},
  file = {/home/pratik/Zotero/storage/MBJW9DDZ/Johnston et al. - 2018 - Estimates of observer expertise improve species di.pdf},
  journal = {Methods in Ecology and Evolution},
  language = {en},
  number = {1}
}

@techreport{johnston2019a,
  title = {Analytical Guidelines to Increase the Value of Citizen Science Data: Using {{eBird}} Data to Estimate Species Occurrence},
  shorttitle = {Analytical Guidelines to Increase the Value of Citizen Science Data},
  author = {Johnston, A and Hochachka, Wm and {Strimas-Mackey}, Me and Ruiz Gutierrez, V and Robinson, Oj and Miller, Et and Auer, T and Kelling, St and Fink, D},
  year = {2019},
  month = mar,
  institution = {{Ecology}},
  doi = {10.1101/574392},
  abstract = {Abstract           Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.           To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.           Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.           Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.},
  file = {/home/pratik/Zotero/storage/3KRGJNK3/Johnston et al. - 2019 - Analytical guidelines to increase the value of cit.pdf},
  language = {en},
  type = {Preprint}
}

@article{kelling2015a,
  title = {Can {{Observation Skills}} of {{Citizen Scientists Be Estimated Using Species Accumulation Curves}}?},
  author = {Kelling, Steve and Johnston, Alison and Hochachka, Wesley M. and Iliff, Marshall and Fink, Daniel and Gerbracht, Jeff and Lagoze, Carl and La Sorte, Frank A. and Moore, Travis and Wiggins, Andrea and Wong, Weng-Keen and Wood, Chris and Yu, Jun},
  editor = {Goffredo, Stefano},
  year = {2015},
  month = oct,
  volume = {10},
  pages = {e0139600},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0139600},
  file = {/home/pratik/Zotero/storage/CUJG7N96/Kelling et al. - 2015 - Can Observation Skills of Citizen Scientists Be Es.pdf},
  journal = {PLOS ONE},
  language = {en},
  number = {10}
}

@article{mackenzie2002,
  title = {Estimating {{Site Occupancy Rates When Detection Probabilities Are Less Than One}}},
  author = {MacKenzie, Darryl I. and Nichols, James D. and Lachman, Gideon B. and Droege, Sam and Royle, J. Andrew and Langtimm, Catherine A.},
  year = {2002},
  volume = {83},
  pages = {2248--2255},
  issn = {1939-9170},
  doi = {10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2},
  abstract = {Nondetection of a species at a site does not imply that the species is absent unless the probability of detection is 1. We propose a model and likelihood-based method for estimating site occupancy rates when detection probabilities are {$<$}1. The model provides a flexible framework enabling covariate information to be included and allowing for missing observations. Via computer simulation, we found that the model provides good estimates of the occupancy rates, generally unbiased for moderate detection probabilities ({$>$}0.3). We estimated site occupancy rates for two anuran species at 32 wetland sites in Maryland, USA, from data collected during 2000 as part of an amphibian monitoring program, Frogwatch USA. Site occupancy rates were estimated as 0.49 for American toads (Bufo americanus), a 44\% increase over the proportion of sites at which they were actually observed, and as 0.85 for spring peepers (Pseudacris crucifer), slightly above the observed proportion of 0.83.},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1890/0012-9658\%282002\%29083\%5B2248\%3AESORWD\%5D2.0.CO\%3B2},
  copyright = {\textcopyright{} 2002 by the Ecological Society of America},
  file = {/home/pratik/Zotero/storage/CPCX48ZH/MacKenzie et al. - 2002 - Estimating Site Occupancy Rates When Detection Pro.pdf;/home/pratik/Zotero/storage/UUP7BKHF/0012-9658(2002)083[2248ESORWD]2.0.html},
  journal = {Ecology},
  keywords = {anurans,bootstrap,Bufo americanus,detection probability,maximum likelihood,metapopulation,monitoring,patch occupancy,Pseudacris crucifer,site occupancy},
  language = {en},
  number = {8}
}

@book{mackenzie2017,
  title = {Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence},
  author = {MacKenzie, Darryl I and Nichols, James D and Royle, J Andrew and Pollock, Kenneth H and Bailey, Larissa and Hines, James E},
  year = {2017},
  publisher = {{Elsevier}},
  isbn = {0-12-407245-3}
}

@manual{MuMIn,
  title = {{{MuMIn}}: {{Multi}}-Model Inference},
  author = {Barto{\'n}, Kamil},
  year = {2020},
  type = {Manual}
}

@article{OpenStreetMap,
  title = {Planet Dump Retrieved from {{https://planet.osm.org}}},
  author = {{OpenStreetMap contributors}},
  year = {2019}
}

@article{praveenj.2017,
  title = {On the Geo-Precision of Data for Modelling Home Range of a Species \textendash{} {{A}} Commentary on {{Ramesh}} et al. (2017)},
  author = {Praveen J.},
  year = {2017},
  month = sep,
  volume = {213},
  pages = {245--246},
  issn = {0006-3207},
  doi = {10.1016/j.biocon.2017.07.017},
  file = {/home/pratik/Zotero/storage/TI2HN6MQ/S0006320717309552.html},
  journal = {Biological Conservation},
  language = {en}
}

@article{sullivan2014,
  title = {The {{eBird}} Enterprise: {{An}} Integrated Approach to Development and Application of Citizen Science},
  shorttitle = {The {{eBird}} Enterprise},
  author = {Sullivan, Brian L. and Aycrigg, Jocelyn L. and Barry, Jessie H. and Bonney, Rick E. and Bruns, Nicholas and Cooper, Caren B. and Damoulas, Theo and Dhondt, Andr{\'e} A. and Dietterich, Tom and Farnsworth, Andrew and Fink, Daniel and Fitzpatrick, John W. and Fredericks, Thomas and Gerbracht, Jeff and Gomes, Carla and Hochachka, Wesley M. and Iliff, Marshall J. and Lagoze, Carl and La Sorte, Frank A. and Merrifield, Matthew and Morris, Will and Phillips, Tina B. and Reynolds, Mark and Rodewald, Amanda D. and Rosenberg, Kenneth V. and Trautmann, Nancy M. and Wiggins, Andrea and Winkler, David W. and Wong, Weng-Keen and Wood, Christopher L. and Yu, Jun and Kelling, Steve},
  year = {2014},
  month = jan,
  volume = {169},
  pages = {31--40},
  issn = {00063207},
  doi = {10.1016/j.biocon.2013.11.003},
  journal = {Biological Conservation},
  language = {en}
}

@article{vanstrien2013,
  title = {Opportunistic Citizen Science Data of Animal Species Produce Reliable Estimates of Distribution Trends If Analysed with Occupancy Models},
  author = {{van Strien}, Arco J. and {van Swaay}, Chris A.M. and Termaat, Tim},
  editor = {Devictor, Vincent},
  year = {2013},
  month = dec,
  volume = {50},
  pages = {1450--1458},
  issn = {00218901},
  doi = {10.1111/1365-2664.12158},
  file = {/home/pratik/Zotero/storage/MDFRJWFE/van Strien et al. - 2013 - Opportunistic citizen science data of animal speci.pdf},
  journal = {Journal of Applied Ecology},
  language = {en},
  number = {6}
}

@manual{viswanathan2020,
  title = {State of India's Birds 2020: {{Background}} and Methodology.},
  author = {Viswanathan, A. and Reddy, A. and Deomurari, A. and Suryawanshi, K. and Madhusudan, M. D. and Kaushik, M. and J, P. and Jayapal, R. and \& Quader, S.},
  year = {2020},
  type = {Manual}
}