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Emily B Dennis; Byron J T Morgan; Stephen N Freeman; Martin S Ridout; Tom M Brereton; Richard Fox; Gary D Powney; David B Roy
Publisher: Public Library of Science (PLoS)
Journal: PLoS ONE
Languages: English
Types: Unknown
Subjects: Research Article, Earth Sciences, Plant Communities, Mathematics, Geoinformatics, Ecology and Environmental Sciences, Mathematical and Statistical Techniques, Simulation and Modeling, Ecology, QH541, Physical Sciences, Grasslands, Geography, Bayesian Method, Plant Science, Animals, Ecology and Environment, Statistics (Mathematics), Seasons, Biology and Life Sciences, Biodiversity, QA276, Computer and Information Sciences, Research and Analysis Methods, Plant Ecology, Arthropoda, Terrestrial Environments, Spatial Autocorrelation, Medicine, Insects, Q, Moths and Butterflies, R, Science, Organisms, Confidence Intervals, Invertebrates, Seasonal Variations, Zoology
Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Powney GD, Isaac NJB. Beyond maps: a review of the applications of biological records. Biological Journal of the Linnean Society. 2015; 115:532-542. https://doi.org/10.1111/bij.12517
    • 2. Hochachka WM, Fink D, Hutchinson RA, Scheldon D, Wong WK, Kelling S. Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution. 2012; 27:130-137. https://doi. org/10.1016/j.tree.2011.11.006 PMID: 22192976
    • 3. Boakes EH, McGowan PJK, Fuller RA, Chang-qing D, Clark NE, O'Connor K, Mace GM. Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biol. 2010; 8: e1000385. https://doi.org/10.1371/journal.pbio.1000385 PMID: 20532234
    • 4. Isaac NJB, van Strien AJ, August TA, Zeeuq MP, Roy DB. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution. 2014; 5:1052-1060. https://doi.org/10.1111/2041-210X.12254
    • 5. Pocock MJO, Roy HE, Preston CD, Roy DB. The Biological Records Centre: a pioneer of citizen science. Biological Journal of the Linnean Society. 2015; 115:475-493. https://doi.org/10.1111/bij.12548
    • 6. Kelling S, Fink D, La Sorte FA, Johnston A, Bruns NE, Hochacka WM. Taking a'Big Data' approach to data quality in a citizen science project. Ambio. 2015; 44:601-611. https://doi.org/10.1007/s13280-015- 0710-4 PMID: 26508347
    • 7. MacKenzie DI, Nichols JD, Hines JE, Knutson MG, Franklin AB. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology. 2003; 84:2200-2207. https:// doi.org/10.1890/02-3090
    • 8. Bailey LL, MacKenzie DI, Nichols JD. Advances and applications of occupancy models. Methods in Ecology and Evolution. 2014; 5:1269-1279. https://doi.org/10.1111/2041-210X.12100
    • 9. Guillera-Arroita G, Lahoz-Monfort JJ, MacKenzie DI, Wintle BA, McCarthy MA. Ignoring Imperfect Detection in Biological Surveys Is Dangerous: A Response to 'Fitting and Interpreting Occupancy Models'. PloS One. 2014; 7:e99751. https://doi.org/10.1371/journal.pone.0099571
    • 10. Ke´ry M, Royle JA, Schmid H, Schaub M, Volet B, Ha¨fliger G, Zbinden N. Site-Occupancy Distribution Modeling to Correct Population-Trend Estimates Derived from Opportunistic Observations. Conservation Biology. 2010; 24:1388-1397. https://doi.org/10.1111/j.1523-1739.2010.01479.x PMID: 20337672
    • 11. van Strien AJ, van Swaay CAM, Termaat T. Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. Journal of Applied Ecology. 2013; 50:1450-1458. https://doi.org/10.1111/1365-2664.12158
    • 12. van Strien AJ, Termaat T, Groenendijk D, Mensing V, Ke´ry M. Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists. Basic and Applied Ecology. 2010; 11:495-503. https://doi.org/10.1016/j.baae.2010.05.003
    • 13. Defra. UK Biodiversity Indicators 2015. 2016. Published by the Department for Environment, Food and Rural Affairs, London.
    • 14. Woodcock BA, Isaac NJB, Bullock JM, Roy DB, Garthwaite DG, Crowe A, Pywell RF. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nature Communications. 2016; 7:12459. https://doi.org/10.1038/ncomms12459 PMID: 27529661
    • 15. Fox R, Brereton TM, Asher J, August TA, Botham MS, Bourne NAD et al. The state of the UK's Butterflies 2015. 2015. Butterfly Conservation and the Centre for Ecology & Hydrology, Wareham, Dorset.
    • 16. Asher J, Fox R, Warren MS. British butterfly distributions and the 2010 target. Journal of Insect Conservation. 2011; 15:291-299. https://doi.org/10.1007/s10841-010-9346-7
    • 17. Dennis EB, Morgan BJT, Freeman SN, Ridout MS, Brereton T, Fox R, Roy DB. The construction of spatial distribution maps and regional occupancy indices from opportunistic records. 2015. University of Kent, http://kar.kent.ac.uk/id/eprint/54859
    • 18. Gimenez O, Blanc L, Besnard A, Pradel R, Doherty PF Jr, Marboutin E, Choquet R. Fitting occupancy models with E-SURGE: hidden-Markov modelling of presence-absence data. Methods in Ecology and Evolution. 2014; 5:592-597. https://doi.org/10.1111/2041-210X.12191
    • 19. Hayhow DB, Burns F, Eaton MA, Al Fulaij N, August TA, Babey L et al. State of Nature 2016. 2016. The State of Nature Partnership.
    • 20. R Core Team : A Language and Environment for Statistical Computing. 2016. Vienna, Austria. http:// www.R-project.org/.
    • 21. Wood SN. Generalized Additive Models: an introduction with R. Chapman & Hall/CRC, Boca Raton; 2006.
    • 22. Harrison PJ, Buckland ST, Yuan Y, Elston DA, Brewer MJ, Johnston A, Pearce-Higgins JW. Assessing trends in biodiversity over space and time using the example of British breeding birds. Journal of Applied Ecology. 2014; 51:1650-1660. https://doi.org/10.1111/1365-2664.12316
    • 23. Fiske I, Changle RB. : An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. Journal of Statistical Software. 2011; 43:1-23. https://doi.org/10.18637/jss.v043.i10
    • 24. Jackson CH. Multi-State Models for Panel Data: The msm Package for R Journal of Statistical Software. 2011; 38:1-29. https://doi.org/10.18637/jss.v038.i08
    • 25. Winston C, Cheng J, Allair JJ, Xie Y, McPherson J. shiny: Web Application Framework for R. 2016. R package version 0.13.1. http://CRAN.R-project.org/package=shiny.
    • 26. Met Office. UK climate-Historic station data. 2015. http://www.metoffice.gov.uk/public/weather/ climate-historic.
    • 27. Green PJ, Silverman BW. Nonparametric Regression and Generalized Linear Models. Chapman & Hall, London; 1994.
    • 28. Nychka D, Furrer R, Sain S. fields: Tools for spatial data 2014. R package version 7.1. http://CRAN.Rproject.org/package=fields.
    • 29. Morton RD, Rowland CS, Wood CM, Meek L, Marston CG, Smith GM. Land Cover Map 2007 (1km percentage aggregate class, GB) v1.2. 2014. NERC-Environmental Information Data Centre,
    • 30. Ke´ry M, Schaub M. Bayesian Population Analysis using WinBUGS. Academic Press, New York; 2011.
    • 31. Ke´ry M, Guillera-Arroita G, Lahoz-Monfort JJ. Analysing and mapping species range dynamics using occupancy models. Journal of Biogeography. 2013; 40:1463-1474. https://doi.org/10.1111/jbi.12087
    • 32. MacKenzie DI, Nichols JD, Royle JA, Pollack KH, Bailey LL, Hines JE. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Academic Press, New York; 2006.
    • 33. Elith J, Phillips SJ, Hastie TJ, Dud´ık M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 2011; 17:43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
    • 34. Gimenez O, Crainiceanu C, Barbraud C, Jenouvrier S, Morgan BJT. Semiparametric regression in capture-recapture modeling. Biometrics. 2006; 62:691-698. https://doi.org/10.1111/j.1541-0420.2005. 00514.x PMID: 16984309
    • 35. Dennis RLH, Shreeve TG, Arnold HR, Roy DB. Does diet breadth control herbivorous insect distribution size? Life history and resource outlets for specialist butterflies. Journal of Insect Conservation. 2005; 9:187-200. https://doi.org/10.1007/s10841-005-5660-x
    • 36. Bishop TR, Botham MS, Fox R, Leather SR, Chapman DS, Oliver TH. The utility of distribution data in predicting phenology. Methods in Ecology and Evolution. 2013; 4:1024-1032. https://doi.org/10.1111/ 2041-210X.12112
    • 37. Strebel N, Ke´ry M, Schaub M, Schmid H. Studying phenology by flexible modelling of seasonal detectability peaks. Methods in Ecology and Evolution. 2014; 5:483-490. https://doi.org/10.1111/2041-210X. 12175
    • 38. Roth T, Strebel N, Amrhein V. Estimating unbiased phenological trends by adapting site-occupancy models. Ecology. 2014; 95:2144-2154. https://doi.org/10.1890/13-1830.1 PMID: 25230466
    • 39. Chambert T, Kendall WL, Hines JE, Nichols JD, Pedrini P, Waddle JH et al. Testing hypotheses on distribution shifts and changes in phenology of imperfectly detectable species. Methods in Ecology and Evolution. 2015; 6:638-647. https://doi.org/10.1111/2041-210X.12362
    • 40. Schmucki R, Pe'er G, Roy DB, Stefanescu C, van Swaay CAM, Oliver TH et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. Journal of Applied Ecology. 2016; 53:501-510. https://doi.org/10.1111/1365-2664.12561
    • 41. Hill MO. Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution. 2012; 3:195-205. https://doi.org/10.1111/j.2041-210X.2011. 00146.x
    • 42. Johnson DS, Conn P, Hooten M, Ray J, Pond BA. Spatial occupancy models for large data sets. Ecology. 2012; 94:801-808. https://doi.org/10.1890/12-0564.1
    • 43. Bled F, Royle JA, Cam E. Hierarchical modeling of an invasive spread: the Eurasian Collared-Dove Streptopelia decaocto in the United States. Ecological Applications. 2011; 21:290-302. https://doi.org/ 10.1890/09-1877.1 PMID: 21516906
    • 44. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004; 20:289-290. https://doi.org/10.1093/bioinformatics/btg412 PMID: 14734327
    • 45. Moore JE, Swihart RK. Modeling patch occupancy by forest rodents: incorporating detectability and spatial autocorrelation with hierarchically structured data. Journal of Wildlife Management. 2005; 69:933-949. https://doi.org/10.2193/0022-541X(2005)069%5B0933:MPOBFR%5D2.0.CO;2
    • 46. Schaub M, Ke´ry M. Combining information in hierarchical models improves inferences in population ecology and demographic population analyses. Animal Conservation. 2012; 15:125-126. https://doi. org/10.1111/j.1469-1795.2012.00531.x
    • 47. Pagel J, Anderson BJ, O'Hara RB, Cramer W, Fox R, Jeltsch F et al. Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records. Methods in Ecology and Evolution. 2014; 5:751-760. https://doi.org/10.1111/2041-210X.12221
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