A fish rots from the head down: how to use the leading digits of ecological data to detect their falsification

Abstract

8Managing wildlife populations requires good data. Researchers and policy makers need 9 reliable population estimates and, in case of commercial or recreational harvesting, also 10 trustworthy information about the number of removed individuals. However, auditing 11 schemes are often weak and political or economic pressure could lead to data fabrication 12 or falsification. Time-series data and population models are crucial to detect anomalies, 13 but they are not always available nor feasible. Therefore, researchers need other tools 14 to identify suspicious patterns in ecological and environmental data, to prioritize their 15 controls. We showed how the Benford's law might be used to identify anomalies and 16 potential manipulation in ecological data, by testing for the goodness-of-fit of the lead-17 ing digits with the Benford's distribution. For this task, we inspected two datasets that 18 were found to be falsified, containing data about estimated large carnivore populations 19 in Romania and Soviet commercial whale catches in the Pacific Ocean. In both the two 20 datasets, the first and second digits numerical series deviated from the expected Ben-21 ford's distribution. In data about large carnivores, the first too digits, taken together, 22 also deviated from the expected Benford's distribution and were characterized by a high 23 Mean Absolute Deviation. In Soviet whale catches, while the single digits deviated from 24 the Benford's distribution and the Mean Absolute Deviation was high, the first two digits 25 were not anomalous. This controversy invites researchers to combine multiple measures 26 of nonconformity and to be cautious in analyzing mixtures of data. Testing the distribu-27 tion of the leading digits might be a very useful tool to inspect ecological datasets and 28 to detect potential falsifications, with great implications for policymakers and researchers 29 as well. For example, if policymakers revealed anomalies in harvesting data or popula-30 tion estimates, commercial or recreational harvesting could be suspended and controls 31 1 strengthened. On the other hand, revealing falsification in ecological research would be 32 crucial for evidence-based conservation, as well as for research evaluation.33 Introduction 34 Successful management of animal and plant populations requires informed decision-making. 35 Information about populations and their geographical distribution is crucial for design-36 ing effective networks of protected areas, identifying threats and integrating conservation 37 in policy making. Furthermore, as many animals and plants are traded, environmental 38 managers also need trustworthy information about the number and qualities of these indi-39 viduals which are removed from nature. During the last 20 years, conservation biology was 40 flooded with information. Digitalization enabled conservationists and agencies to store 41 and share their data (Hampton et al., 2013; Page et al., 2015). Advances in informatics 42 and the computational power of computers, allowed for an ...

Aplicación de la ley de Benford a la pesquería argentina de corvina rubia (Micropogonias furnieri)

“…Los valores críticos estimados en este trabajo para d 1 , d 2 y d 1 -d 2, presentaron rangos que incluyeron a los valores de referencia establecidos por Nigrini (2012), lo que sugiere que la metodología aplicada es consistente con los estándares previamente establecidos. Resultaría importante complementar el análisis de la conformidad con la ley de Benford con otros métodos estadísticos para una evaluación más robusta de la calidad de los datos (Cerri 2018). La aplicación de un enfoque multidimensional que incluya pruebas adicionales podría proporcionar una perspectiva más completa y mitigar las limitaciones inherentes a cualquier método individual.…”

“…A pesar de que la distribución de frecuencias de Benford no es intuitiva debido a su naturaleza no uniforme, ha sido observada en una amplia variedad de conjuntos de datos, incluyendo áreas como finanzas, auditorías, dimensiones geográficas, fraudes electorales, reportes oficiales de casos de COVID-19 y declaraciones de impuestos, entre otros (Diekmann 2007;Mebane 2008;Silva y Filho 2021). En el ámbito de la biología pesquera, la implementación de técnicas estadísticas basadas en la ley de Benford han sido utilizadas para la verificación de los reportes de capturas de la langosta del Atlántico canadiense (Homarus americanus, Graham et al 2010), de ballenas en el océano Pacífico (Cerri 2018), atunes en el Golfo de Guinea (Tsagbey et al 2017) y, recientemente, para validar la información de captura de la pesca artesanal en el noreste de Brasil (Noleto-Filho et al 2022). Sin embargo, la utilización de este tipo de herramientas no es ajena a controversias concernientes con la magnitud de las desviaciones observadas a la ley y el tamaño de la muestra (eventos cry wolf o exceso de potencia).…”

Número de casos confirmados de la covid-19 en Ecuador identificados por provincia de atención, se ajustan a la distribución de Ea ley de newcomb-benford

“…En los años 70 se aplicó la LNB en ingeniería informática y en el desarrollo de modelos matemáticos 21,22 . En general la LNB se aplica para la detección de fraude 15 o sesgo en datos financieros 19,20 , electorales 14,23 , en Biología [24][25][26][27] , en datos de Física atómica 28 , en datos científicos 29 y patrones de música clásica 30 .…”

Forensic Accounting

“…Researchers have used Benford's law to predict misreporting even outside the financial context (Varian 1972). For example, researchers used Benford's law to identify misreporting in ecological data (Cerri 2018), hydrology data (Nigrini & Miller 2007), countries' economic data (Michalski 13 Using a critical value of 5%, untabulated analysis shows that more than 80% of funds' returns conform to the distribution but that Madoff's returns deviate significantly. To measure deviations at the fund level, I use the Kolmogorov-Smirnov statistic, which relies on the maximum deviation from the expected distribution (for a discussion of measuring conformance to Benford's law, see Morrow 2014, Pike 2008.…”