Non-Gaussian Analysis of Herbarium Specimen Damage to Optimize Specimen Collection Management

Aris Yaman; Yulia Aris Kartika; Ariani Indrawati; Zaenal Akbar; Lindung Parningotan Malik; Wita Wardani; Tutie Djarwaningsih; Taufik Mahendra; Dadan Ridwan Saleh

doi:10.17977/um018v5i12022p1-16

Non-Gaussian Analysis of Herbarium Specimen Damage to Optimize Specimen Collection Management

Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung Parningotan Malik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan Ridwan Saleh

Abstract

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, location, storage, and remounting status on the level of damage to herbarium specimens, especially those in the genus Excoecaria. Gaussian modeling is not good enough to model the counted data phenomenon (the amount of damage to herbarium specimens). Negative binomial regression (NBR) provides a better model when compared to generalized Poisson regression and ordinary Gaussian regression approaches. NBR detects non-uniformity in the storage process, causing damage to herbarium specimens. Natural damage to herbarium specimens is caused by differences in species and the origin of specimens.

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N. O. Kin, N. Y. Demchenko, and S. N. Ryabtsov, “Rare plants of the Voronezh region in ecosystems of Khrenovsky pine forest,” IOP Conf. Ser. Earth Environ. Sci., vol. 817, no. 1, 2021.

A. A. Pinto, J. J. C. Mont, D. E. M. Jiménez, A. G. Noriega, J. J. Barrios, and A. C. McCormick, “Characterization of riparian tree communities along a river basin in the pacific slope of guatemala,” Forests, vol. 12, no. 7, pp. 1–12, 2021.

T. K. Miller, A. S. Gallinat, L. C. Smith, and R. B. Primack, “Comparing fruiting phenology across two historical datasets: Thoreau’s observations and herbarium specimens,” Ann. Bot., vol. 128, no. 2, pp. 159–170, 2021.

A. G. Auffret, “Historical floras reflect broad shifts in flowering phenology in response to a warming climate,” Ecosphere, vol. 12, no. 7, 2021.

L. A. Jenny, L. R. Shapiro, C. C. Davis, J. Davies, N. E. Pierce, and E. K. Meineke, “Herbarium specimens reveal herbivory patterns across the genus Cucurbita,” Bioarvix, 2021.

E. K. Meineke, A. T. Classen, N. J. Sanders, and T. Jonathan Davies, “Herbarium specimens reveal increasing herbivory over the past century,” J. Ecol., vol. 107, no. 1, pp. 105–117, 2019.

W. Milliken, B. E. Walker, M. J. R. Howes, F. Forest, and E. Nic Lughadha, “Plants used traditionally as antimalarials in Latin America: Mining the tree of life for potential new medicines,” J. Ethnopharmacol., vol. 279, no. March, 2021.

P. Maher et al., “The Value of Herbarium Collections to the Discovery of Novel Treatments for Alzheimer’s Disease, a Case Made With the Genus Eriodictyon,” Front. Pharmacol., vol. 11, no. March, 2020.

S. Acha, A. Linan, J. MacDougal, and C. Edwards, “The evolutionary history of vines in a neotropical biodiversity hotspot: Phylogenomics and biogeography of a large passion flower clade (Passiflora section Decaloba),” Mol. Phylogenet. Evol., vol. 164, no. July, p. 107260, 2021.

N. Forin, A. Vizzini, F. Fainelli, E. Ercole, and B. Baldan, “Taxonomic re‐examination of nine rosellinia types (Ascomycota, xylariales) stored in the saccardo mycological collection,” Microorganisms, vol. 9, no. 3, 2021.

D. Girmansyah, Y. Santika, Rugayah, and J. S. Rahajoe, Index Herbariorum Indonesianum. 2018.

V. Bestandssituation, “Bärlappe in Thüringen –Verbreitung und Bestandssituation,” Landschaftspfl. und Naturschutz Thüringen, vol. 52, no. 2, pp. 51–54, 2015.

A. Güntsch, W. Berendsohn, and P. Mergen, “The BioCASE Project - a Biological Collections Access Service for Europe,” Ferrantia, vol. 51, no. June 2014, pp. 103–108, 2007.

E. K. Meineke, C. Tomasi, S. Yuan, and K. M. Pryer, “Applying machine learning to investigate long-term insect–plant interactions preserved on digitized herbarium specimens,” Appl. Plant Sci., vol. 8, no. 6, pp. 1–11, 2020.

K. D. Pearson et al., “Machine learning using digitized herbarium specimens to advance phenological research,” Bioscience, vol. 70, no. 7, pp. 610–620, 2020.

I. Koh et al., “Modeling the status, trends, and impacts of wild bee abundance in the United States,” Proc. Natl. Acad. Sci. U. S. A., vol. 113, no. 1, pp. 140–145, 2016.

C. Meyer, P. Weigelt, and H. Kreft, “Multidimensional biases, gaps and uncertainties in global plant occurrence information,” Ecol. Lett., vol. 19, no. 8, pp. 992–1006, 2016.

M. A. Jamieson, A. L. Carper, C. J. Wilson, V. L. Scott, and J. Gibbs, “Geographic biases in bee research limits understanding of species distribution and response to anthropogenic disturbance,” Front. Ecol. Evol., vol. 7, no. JUN, pp. 1–8, 2019.

A. Agresti, C. Franklin, and B. Klingenberg, The art and science of learning from data, Fourth. Ed., vol. 53, no. 95. New York: Pearson, 2012.

P. C. Consul and F. Famoye, “Generalized poisson regression model,” Communications in Statistics - Theory and Methods, vol. 21, no 1. pp. 89–109, 1992.

A. Zeileis, C. Kleiber, and S. Jackman, “Regression models for count data in R,” J. Stat. Softw., vol. 27, no. 8, pp. 1–25, 2008.

J. M. Hilbe, “Modeling count data,” model. Count Data, no. 3, pp. 1–294, 2014.

C. von Linnei, Systema naturae, Editio Dec. Impensis Direct. Laurentii Salvii, 1759.

Flora Fauna Web, “Excoecaria cochinchinensis Lour..,” Singapore National Parks, 2019. https://www.nparks.gov.sg/florafaunaweb/flora/2/0/2010 (accessed Jan. 28, 2021).

T. A. James and G. J. Harden, “Genus Excoecaria,” NEW SOUTH WALES FLORA ONLINE, 2021. https://plantnet.rbgsyd.nsw.gov.au/cgi-bin/NSWfl.pl?page=nswfl&lvl=gn&name=Excoecaria.

E. K. Meineke and B. H. Daru, “Bias assessments to expand research harnessing biological collections,” Trends Ecol. Evol., pp. 1–12, 2021.

A. Hazra, “An Exact Kolmogorov–Smirnov Test for the Negative Binomial Distribution with Unknown Probability of Success,” Res. Rev. J. Stat., vol. 2, no. 1, pp. 1–13, 2013.

M. Jamshidian, R. I. Jennrich, and W. Liu, “A study of partial F tests for multiple linear regression models,” Comput. Stat. Data Anal., vol. 51, no. 12, pp. 6269–6284, 2007.

C. M. Woods, L. Cai, and M. Wang, “The Langer-Improved Wald Test for DIF Testing With Multiple Groups: Evaluation and Comparison to Two-Group IRT,” Educ. Psychol. Meas., vol. 73, no. 3, pp. 532–547, 2013.

DOI: http://dx.doi.org/10.17977/um018v5i12022p1-16