The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review
Abstract
:Simple Summary
Abstract
1. Forensic Entomology, an Inferential Science
2. Issues Associated with Successional Data
2.1. Data Measured Repetitively from a Small Number of Sampling Units and Field Sites
2.2. Non-Linearity
2.3. Datasets with a Relatively Large Proportion of Unexplained Variance
2.4. Data Affected by Temporal and Spatial Effects
2.5. Datasets Including Many Independent and Dependent Variables
3. Possible Remedies to the Issues Associated with Successional Data
3.1. How to Solve Problems Related to Low Statistical Power as well as to Low Internal and External Validity
3.2. How to Solve Problems Related to Interdependence between Records, Auto-Regressive Covariance Structure, Non-Linear Effects and Non-Gaussian Distributions
3.3. How to Solve Problems Related to Autocorrelation, Multicollinearity, Overfitting and Alpha Inflation
3.4. How to Solve Problems Related to Having Data Interrelated in Time or Space
3.5. How to Solve Problems Related to Having a Large Amount of Systematic Variance
4. Advice to Scientific Editors, Reviewers and Academic Supervisors
- The study is devoid of experimental errors. Scientific editors and reviewers should not be afraid to require from authors a detailed description and a map of the layout of the study. Regardless of the nature of the study, the experimental unit should always be clearly identified. To learn how to recognize the experimental unit and main experimental errors, read [12‒14,62]. Pseudoreplicated studies should never be published, even as “preliminary studies”.
- If the nature of the study allows for it, an inferential statistical test that permits extrapolation of the results to case scenarios is presented. The statistical procedures should be described in detail and an estimate of the experimental error should be evident in the tables and figures of the manuscript. If successional data are involved, the statistical test should comply with elements presented in Table 2.
- If the nature of the study does not allow for it, no inferential statistical test is presented. In a widely cited article, Hurlbert [62] suggested that good articles that refrain from using inferential statistics when these cannot be applied are worth publishing. However, the authors should explicitly recognize that the study is descriptive, thus not allowing for transposition of the results to other situations or use in court.
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Schoenly, K.G.; Haskell, N.H.; Hall, R.D.; Gbur, J.R. Comparative performance and complementarity of four sampling methods and arthropod preference tests from human and porcine remains at the Forensic Anthropology Center in Knoxville, Tennessee. J. Med. Entomol. 2007, 44, 881–894. [Google Scholar] [CrossRef] [PubMed]
- Dawson, B.M.; Barton, P.S.; Wallman, J.F. Contrasting insect activity and decomposition of pigs and humans in an Australian environment: A preliminary study. Forensic Sci. Int. 2020, 316, 110515. [Google Scholar] [CrossRef] [PubMed]
- Payne, J.A. A summer carrion study of the baby pig Sus scrofa Linnaeus. Ecology 1965, 46, 592–602. [Google Scholar] [CrossRef]
- VanLaerhoven, S.L.; Anderson, G.S. Insect succession on buried carrion in two biogeoclimatic zones of British Columbia. J. Forensic Sci. 1999, 44, 32–43. [Google Scholar] [CrossRef]
- LeBlanc, K.; Boudreau, D.; Moreau, G. Small bait traps may not accurately reflect the composition of necrophagous Diptera associated to remains. Insects.
- Faigman, D.L.; Monahan, J.; Slobogin, C. Group to individual (G2i) inference in scientific expert testimony. Univ. Chic. Law Rev. 2014, 81, 417–480. [Google Scholar] [CrossRef] [Green Version]
- Peirce, C.S. A theory of probable inference. In Studies in Logic by Members of the Johns Hopkins University; Peirce, C.S., Ed.; Little, Brown, and Company: Boston, MA, USA, 1883; pp. 126–181. [Google Scholar]
- Fisher, R.A. The arrangement of field experiments. J. Min. Agric. 1926, 33, 503–513. [Google Scholar]
- Fisher, R.A. The Design of Experiments; Oliver and Boyd: Edinburgh, UK.; London, UK, 1935. [Google Scholar]
- Cochran, W.G.; Cox, G.M. Experimental Designs; Wiley: New York, NY, USA, 1950. [Google Scholar]
- Amendt, J.; Campobasso, C.P.; Gaudry, E.; Reiter, C.; LeBlanc, H.N.; Hall, M.J. Best practice in forensic entomology—Standards and guidelines. Int. J. Legal. Med. 2007, 121, 90–104. [Google Scholar] [CrossRef]
- Michaud, J.-P.; Schoenly, K.G.; Moreau, G. Sampling flies or sampling flaws? Experimental design and inference strength in forensic entomology. J. Med. Entomol. 2012, 49, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moreau, G.; Michaud, J.-P.; Schoenly, K.G. Experimental design, inferential statistics, and computer modeling. In Forensic Entomology: International Dimensions and Frontiers; Tomberlin, J.K., Benbow, M.E., Eds.; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2015; pp. 205–230. [Google Scholar]
- Schoenly, K.G.; Michaud, J.-P.; Moreau, G. Design and analysis of field studies in carrion ecology. In Carrion Ecology, Evolution and Their Applications; Benbow, M.E., Tomberlin, J.K., Tarone, A.M., Eds.; CRC Press: Boca Raton, FL, USA, 2015; pp. 129–148. [Google Scholar]
- Imwinkelried, E.J. The best insurance against miscarriages of justice caused by junk science: An admissibility test that is scientifically and legally sound. Albany Law Rev. 2017, 81, 851–875. [Google Scholar] [CrossRef]
- Robertson, B.; Vignaux, G.A. Probability—The logic of the law. Oxf. J. Leg. Stud. 1993, 13, 457–478. [Google Scholar] [CrossRef]
- Aitken, C.G.G.; Taroni, F. Statistics and the Evaluation of Evidence for Forensic Scientists, 2nd ed.; John Wiley & Sons Ltd: Chichester, UK, 2004. [Google Scholar] [CrossRef]
- O’Flynn, M.A. The succession and rate of development of blowflies in carrion in southern Queensland and the application of these data to forensic entomology. Aust. J. Entomol. 1983, 22, 137–148. [Google Scholar] [CrossRef]
- Tabor, K.L.; Fell, R.D.; Brewster, C.C.; Pelzer, K.; Behonick, G.S. Effects of antemortem ingestion of ethanol on insect successional patterns and development of Phormia regina (Diptera: Calliphoridae). J. Med. Entomol. 2005, 42, 481–489. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lutz, L.; Amendt, J.; Moreau, G. Carcass concealment alters assemblages and reproduction of forensically important beetles. Forensic Sci. Int. 2018, 291, 124–132. [Google Scholar] [CrossRef]
- Pechal, J.L.; Crippen, T.L.; Benbow, M.E.; Tarone, A.M.; Dowd, S.; Tomberlin, J.K. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int. J. Legal Med. 2014, 128, 193–205. [Google Scholar] [CrossRef]
- Fu, X.; Guo, J.; Finkelbergs, D.; He, J.; Zha, L.; Guo, Y.; Cai, J. Fungal succession during mammalian cadaver decomposition and potential forensic implications. Sci. Rep. 2019, 9, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vass, A.; Smith, R.; Thompson, C.; Burnett, M.; Wolf, D.; Synstelien, J.; Dulgerian, N.; Eckenrode, B. Decomposition odor analysis database. J. Forensic Sci. 2004, 49, 1–10. [Google Scholar] [CrossRef]
- Tumram, N.K.; Bardale, R.V.; Dongre, A.P. Postmortem analysis of synovial fluid and vitreous humour for determination of death interval: A comparative study. Forensic Sci. Int. 2011, 204, 186–190. [Google Scholar] [CrossRef]
- Megyesi, M.S.; Nawrocki, S.P.; Haskell, N.H. Using accumulated degree-days to estimate the postmortem interval from decomposed human remains. J. Forensic Sci. 2005, 50, 1–9. [Google Scholar] [CrossRef]
- Michaud, J.-P.; Moreau, G. A statistical approach based on accumulated degree-days to predict decomposition-related processes. J. Forensic Sci. 2011, 56, 229–232. [Google Scholar] [CrossRef]
- Moffatt, C.; Simmons, T.; Lynch-Aird, J. An improved equation for TBS and ADD: Establishing a reliable postmortem interval framework for casework and experimental studies. J. Forensic Sci. 2016, 61, S201–S207. [Google Scholar] [CrossRef] [PubMed]
- Matuszewski, S.; Hall, M.J.; Moreau, G.; Schoenly, K.G.; Tarone, A.M.; Villet, M.H. Pigs vs. people: The use of pigs as analogues for humans in forensic entomology and taphonomy research. Int. J. Legal Med. 2020, 134, 793–810. [Google Scholar] [CrossRef] [Green Version]
- Schoenly, K.G.; Shahid, S.A.; Haskell, N.H.; Hall, R.D. Does carcass enrichment alter community structure of predaceous and parasitic arthropods? A second test of the arthropod saturation hypothesis at the anthropology research facility in Knoxville, Tennessee. J. Forensic Sci. 2005, 50, 134–142. [Google Scholar] [CrossRef]
- Damann, F.E.; Tanittaisong, A.; Carter, D.O. Potential carcass enrichment of the University of Tennessee Anthropology Research Facility: A baseline survey of edaphic features. Forensic Sci. Int. 2012, 222, 4–10. [Google Scholar] [CrossRef] [PubMed]
- Michaud, J.-P.; Moreau, G. Predicting the visitation of carcasses by carrion-related insects under different rates of degree-day accumulation. Forensic Sci. Int. 2009, 185, 78–83. [Google Scholar] [CrossRef] [PubMed]
- Lever, J.; Krywinski, M.; Altman, N. Model selection and overfitting. Nat. Methods 2016, 13, 703–704. [Google Scholar] [CrossRef]
- Michaud, J.-P.; Moreau, G. Effect of variable rates of daily sampling of fly larvae on decomposition and carrion-insect community assembly: Implications for forensic entomology field study protocols. J. Med. Entomol. 2013, 50, 890–897. [Google Scholar] [CrossRef]
- Leibold, M.A.; Holyoak, M.; Mouquet, N.; Amarasekare, P.; Chase, J.M.; Hoopes, M.F.; Holt, R.D.; Shurin, J.B.; Law, R.; Tilman, D.; et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 2004, 7, 601–613. [Google Scholar] [CrossRef]
- Lichstein, J.W.; Simons, T.R.; Shriner, S.A.; Franzreb, K.E. Spatial autocorrelation and autoregressive models in ecology. Ecol. Monogr. 2002, 72, 445–463. [Google Scholar] [CrossRef]
- Sachs, L. Angewandte Statistik; Springer: Berlin/Heidelberg, Germany, 1978. [Google Scholar]
- Farrar, D.E.; Glauber, R.R. Multicollinearity in regression analysis: The problem revisited. Rev. Econ. Stat. 1967, 49, 92–107. [Google Scholar] [CrossRef]
- Horgan, F.G. Dung beetles in pasture landscapes of Central America: Proliferation of synanthropogenic species and decline of forest specialists. Biodivers. Conserv. 2007, 16, 2149–2165. [Google Scholar] [CrossRef]
- Jonsell, M.; Abrahamsson, M.; Widenfalk, L.; Lindbladh, M. Increasing influence of the surrounding landscape on saproxylic beetle communities over 10 years succession in dead wood. For. Ecol. Manag. 2019, 440, 267–284. [Google Scholar] [CrossRef]
- Michaud, J.-P.; Moreau, G. Facilitation may not be an adequate mechanism of community succession on carrion. Oecologia 2017, 183, 1143–1153. [Google Scholar] [CrossRef]
- Aubernon, C.; Hedouin, V.; Charabidze, D. The maggot, the ethologist and the forensic entomologist: Sociality and thermoregulation in necrophagous larvae. J. Adv. Res. 2019, 16, 67–73. [Google Scholar] [CrossRef]
- Tarone, A.M.; Foran, D.R. Generalized additive models and Lucilia sericata growth: Assessing confidence intervals and error rates in forensic entomology. J. Forensic Sci. 2008, 53, 942–948. [Google Scholar] [CrossRef]
- Moreau, G.; Lutz, L.; Amendt, J. Honey, can you take out the garbage can? Modeling weather data for cadavers found within containers. Pure Appl. Geophys. 2019. [Google Scholar] [CrossRef]
- Cumming, J.A.; Wooff, D.A. Dimension reduction via principal variables. Comput. Stat. Data Anal. 2007, 52, 550–565. [Google Scholar] [CrossRef] [Green Version]
- De Souza, M.S.; Pepinelli, M.; de Almeida, E.C.; Ochoa-Quintero, J.M.; Roque, F.O. Blow flies from forest fragments embedded in different land uses: Implications for selecting indicators in forensic entomology. J. Forensic Sci. 2016, 61, 93–98. [Google Scholar] [CrossRef] [PubMed]
- Moore, H.E.; Pechal, J.L.; Benbow, M.E.; Drijfhout, F.P. The potential use of cuticular hydrocarbons and multivariate analysis to age empty puparial cases of Calliphora vicina and Lucilia sericata. Sci. Rep. 2017, 7, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Boudreau, D.R.; Hammami, N.; Moreau, G. Environmental and evolutionary factors favoring the coexistence of sarcosaprophagous Calliphoridae species competing for animal necromass. Ecol. Entomol.
- Gandiaga, F.; Moreau, G. How long are thinning-induced resource pulses maintained in plantation forests? For. Ecol. Manag. 2019, 440, 113–121. [Google Scholar] [CrossRef]
- Chiasson, B.; Moreau, G. Assessing the lifeboat effect of retention forestry using flying beetle assemblages. For. Ecol. Manag. 2021, 483, 118784. [Google Scholar] [CrossRef]
- Vogel, S.; Bussler, H.; Finnberg, S.; Müller, J.; Stengel, E.; Thorn, S. Diversity and conservation of saproxylic beetles in 42 European tree species: An experimental approach using early successional stages of branches. Insect Conserv. Divers. 2021, 14, 132–143. [Google Scholar] [CrossRef]
- Jugovic, J.; Koprivnikar, N. Rolling in the deep: Morphological variation as an adaptation to different nesting behaviours of coprophagous Scarabaeoidea. Biologia 2020, 1–13. [Google Scholar] [CrossRef]
- Pechal, J.L.; Moore, H.; Drijfhout, F.; Benbow, M.E. Hydrocarbon profiles throughout adult Calliphoridae aging: A promising tool for forensic entomology. Forensic Sci. Int. 2014, 245, 65–71. [Google Scholar] [CrossRef] [PubMed]
- McIntosh, C.S.; Dadour, I.R.; Voss, S.C. A comparison of carcass decomposition and associated insect succession onto burnt and unburnt pig carcasses. Int. J. Legal Med. 2017, 131, 835–845. [Google Scholar] [CrossRef] [PubMed]
- Andow, D.A.; Kiritani, K. Density-dependent population regulation detected in short time series of saproxylic beetles. Popul. Ecol. 2016, 58, 493–505. [Google Scholar] [CrossRef]
- Kaila, L.; Martikainen, P.; Punttila, P. Dead trees left in clear-cuts benefit saproxylic Coleoptera adapted to natural disturbances in boreal forest. Biodivers. Conserv. 1997, 6, 1–18. [Google Scholar] [CrossRef]
- Louzada, J.; Lima, A.P.; Matavelli, R.; Zambaldi, L.; Barlow, J. Community structure of dung beetles in Amazonian savannas: Role of fire disturbance, vegetation and landscape structure. Landsc. Ecol. 2010, 25, 631–641. [Google Scholar] [CrossRef]
- McGeoch, M.A.; Schroeder, M.; Ekbom, B.; Larsson, S. Saproxylic beetle diversity in a managed boreal forest: Importance of stand characteristics and forestry conservation measures. Divers. Distrib. 2007, 13, 418–429. [Google Scholar] [CrossRef]
- Mourant, A.; Lecomte, N.; Moreau, G. Indirect effects of an ecosystem engineer: How the Canadian beaver can drive the reproduction of saproxylic beetles. J. Zool. 2018, 304, 90–97. [Google Scholar] [CrossRef]
- Campobasso, C.P.; Di Vella, G.; Introna, F. Factors affecting decomposition and Diptera colonization. Forensic Sci. Int. 2001, 120, 18–27. [Google Scholar] [CrossRef]
- Smaldino, P.E.; McElreath, R. The natural selection of bad science. R Soc. Open Sci. 2016, 3, 160384. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ioannidis, J.P. Why most published research findings are false. PLoS Med. 2005, 2, e124. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hurlbert, S.H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 1984, 54, 187–211. [Google Scholar] [CrossRef] [Green Version]
Issues | Consequences |
---|---|
1. Data measured repetitively from a small number of sampling units and field sites | Data interdependence Autoregressive covariance structure Low statistical power Low internal validity |
2. Data presenting non-linear trends | Non-linear effects Overfitting Non-Gaussian distribution |
3. Datasets with a relatively large proportion of unexplained variance | High proportion of systematic variance Low external validity |
4. Data affected by temporal and spatial effects | Data interrelated in time Data interrelated in space |
5. Datasets including many independent and dependent variables | Autocorrelation Multicollinearity Overfitting Alpha inflation |
Consequences of Successional Data | Remedies |
---|---|
1. Low statistical power Low internal validity Low external validity | Increase the sample size Increase the number of locations, times, and conditions |
2. Data interdependence Autoregressive covariance structure Non-linear effects Non-Gaussian distribution | Generalized linear models (GLMs), generalized linear mixed models (GLMMs), generalized additive models (GAMs), generalized additive mixed models (GAMMs) |
3. Autocorrelation Multicollinearity Overfitting Alpha inflation | Multivariate statistics |
4. Data interrelated in time Data interrelated in space | Time series analysis Spatial statistics Repeated measures and/or spatially explicit GLMs, GLMMs, GAMs, GAMMs |
5. High proportion of systematic variance | Ensure that all influential variables have been accounted for Use a model that is better suited to data Acknowledge this variability |
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Moreau, G. The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review. Insects 2021, 12, 240. https://doi.org/10.3390/insects12030240
Moreau G. The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review. Insects. 2021; 12(3):240. https://doi.org/10.3390/insects12030240
Chicago/Turabian StyleMoreau, Gaétan. 2021. "The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review" Insects 12, no. 3: 240. https://doi.org/10.3390/insects12030240
APA StyleMoreau, G. (2021). The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review. Insects, 12(3), 240. https://doi.org/10.3390/insects12030240