ednastudies A Statistician s Perspective Mary Lesperance, Ph.D., P.Stat. Dept Mathematics & Statistics
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1 ednastudies A Statistician s Perspective Mary Lesperance, Ph.D., P.Stat. Dept Mathematics & Statistics
2 Outline 1. Purpose of study 2. Sampling Plan 3. Statistical Methodology - Assumptions 4. Validation
3 1. Purpose of study Detection presence/absence of species develop a test for presence/absence estimate the probabilities of presence/detection with confidence intervals Species abundance/density estimation
4 What decisions/policies will be informed by the study? What type of information is needed to inform the above? What are the costs associated with being wrong? e.g. + present, -absent Detection True + True - Test + Test - False Negative False Positive
5 Characteristics required for the test/estimates? Sensitivity, specificity, accuracy, AUC How accurately must abundance be measured? Etc. Answers inform sampling plan and statistical methodology
6 2. Sampling Plan Sample size issues number of sites, samples, technical replicates, sampling times Closed sites? Heterogeneity of detection probs Pool samples at a site? Covariates measured? Degradation due to transport samples Detection Include negative/positive controls Abundance Models for standard curves
7 3. Statistical Methodology Detection ROC (Receiver Operating Characteristic) curves diagnostic ability of binary classifier Mixed models Site occupancy models (hierarchical) Abundance Standard curve models
8 ASSUMPTIONS! All statistical methods involve assumptions/requirements Examples: Closed populations Constant probabilities of detection Adequate sample sizes Binomial sampling Bayesian models require prior assumptions Sensitivity to assumptions
9 4. Validation!! How will your model/test be validated? Require some gold standard data, i.e. data where presence/absence is known; or abundance is known External data sets for validation
10 Summary There are many factors to consider when designing and edna tool/study!!
11 References: Chambert, T., D. S. Pilliod, C. S. Goldberg, H. Doi, and T. Takahara An analytical framework for estimating aquatic species density from environmental DNA. Ecology and Evolution 8: Dorazio, R. M., and R. A. Erickson ednaoccupancy: An r package for multiscale occupancy modelling of environmental DNA data. Molecular Ecology Resources 18: Ficetola, G. F., C. Miaud, F. Pompanon, and P. Taberlet Species detection using environmental DNA from water samples. Biology Letters 4: Ficetola, G. F., J. Pansu, A. Bonin, E. Coissac, C. Giguet-Covex, M. De Barba, L. Gielly, C. M. Lopes, F. Boyer, F. Pompanon, G. Raye, and P. Taberlet Replication levels, false presences and the estimation of the presence/absence from edna metabarcoding data. Molecular Ecology Resources 15: Ficetola, G. F., P. Taberlet, and E. Coissac How to limit false positives in environmental DNA and metabarcoding? Molecular Ecology Resources 16: Hunter, M. E., S. J. Oyler-McCance, R. M. Dorazio, J. A. Fike, B. J. Smith, C. T. Hunter, R. N. Reed, and K. M. Hart Environmental DNA (edna) Sampling Improves Occurrence and Detection Estimates of Invasive Burmese Pythons. Plos One 10. Lahoz-Monfort, J. J., G. Guillera-Arroita, and R. Tingley Statistical approaches to account for false-positive errors in environmental DNA samples. Molecular Ecology Resources 16: MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: Miller, D. A., J. D. Nichols, B. T. McClintock, E. H. C. Grant, L. L. Bailey, and L. A. Weir Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification. Ecology 92: Schmidt, B. R., M. Kery, S. Ursenbacher, O. J. Hyman, and J. P. Collins Site occupancy models in the analysis of environmental DNA presence/absence surveys: a case study of an emerging amphibian pathogen. Methods in Ecology and Evolution 4: Thomsen, P. F., J. Kielgast, L. L. Iversen, C. Wiuf, M. Rasmussen, M. T. P. Gilbert, L. Orlando, and E. Willerslev Monitoring endangered freshwater biodiversity using environmental DNA. Molecular Ecology 21: Veldhoen, N., J. Hobbs, G. Ikonomou, M. Hii, M. Lesperance, and C. C. Helbing Implementation of Novel Design Features for qpcr-based edna Assessment. Plos One 11. Yoccoz, N. G., K. A. Brathen, L. Gielly, J. Haile, M. E. Edwards, T. Goslar, H. von Stedingk, A. K. Brysting, E. Coissac, F. Pompanon, J. H. Sonstebo, C. Miquel, A. Valentini, F. de Bello, J. Chave, W. Thuiller, P. Wincker, C. Cruaud, F. Gavory, M. Rasmussen, M. T. P. Gilbert, L. Orlando, C. Brochmann, E. Willerslev, and P. Taberlet DNA from soil mirrors plant taxonomic and growth form diversity. Molecular Ecology 21: PRESENCE: ML estimation for occupancy models
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