Machine learning for tracking illegal wildlife trade on social media

Minin, Enrico Di; Fink, Christoph; Tenkanen, Henrikki; Hiippala, Tuomo

doi:10.1038/s41559-018-0466-x

Cited by 138 publications

(69 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…And in order to reduce illegal trafficking, it has been suggested to use deep learning algorithms to monitor such activities on social media to automatically detect pictures of illegal wildlife products 60 .…”

Section: Ecosystem Management and Conservationmentioning

confidence: 99%

Applications for deep learning in ecology

Christin

Hervet

Lecomte

2018

Preprint

View full text Add to dashboard Cite

A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

show abstract

Section: Ecosystem Management and Conservationmentioning

confidence: 99%

Applications for deep learning in ecology

Christin

Hervet

Lecomte

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…However, from this research it must be clear that digit-based approaches, like the Benford’s distribution, are not meant to replace more refined techniques, such as machine learning or the comparison of data with complex and realistic population models (DiMininin 2018 a,b; Tulloch et al, 2018). Indeed, we believe that they could be very useful as early detection tools, like a warning light that lights up whenever the data do not look convincing.…”

Section: Discussionmentioning

confidence: 99%

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

Cerri

2018

Preprint

View full text Add to dashboard Cite

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 ...

show abstract

“…Machine learning techniques are also becoming incorporated to investigate illegal wildlife trade on social media using the three-step idea of mining, filtering, and identifying data. However, this approach is still in its early stages, and frameworks still need to be explored particularly related to data security and privacy requirements where researchers are based (Di Minin et al 2018).…”

Section: Ethical and Legal Considerations Of Online Surveysmentioning

confidence: 99%

“…The link between the growth of social media and exploitation of threatened species for pet purposes is somewhat subjective but highlighted for the trade of raptors and owls (Kitson and Nekaris 2017;Panter et al 2018). Recently, more research is being dedicated to investigate the role and impact of social media on legal and illegal trade of birds (Iqbal 2016;Siriwat et al 2020) and other species (Martin et al 2018, Di Minin et al 2018. The combination of increased monitoring trade on these novel platforms, with continued surveying of traditional markets to ensure no increase in numbers are offered for sale Q24 , will be essential in helping inform wildlife regulations and policies, not only of raptors but of all wildlife species targeted for trade.…”

Section: Challengesmentioning

confidence: 99%

Wildlife trade shifts from brick-and-mortar markets to virtual marketplaces: A case study of birds of prey trade in Thailand

Siriwat

Nijman

2020

Journal of Asia-Pacific Biodiversity

View full text Add to dashboard Cite

a b s t r a c tThe shift of wildlife trade from brick-and-mortar markets to virtual online marketplaces is reported using the trade of birds of prey (raptors) in Thailand from 1968 to 2019 as a case study. We analyzed data obtained from physical market survey data for 2,782 individuals from 27 species, with 2,420 individuals reported in early surveys and 362 individuals reported in later surveys (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) and online surveys (February 2017 to January 2019). We compared information on asking prices and what species were traded using two comparative approaches to analyze how species composition may have changed over time (physical markets and online platforms; pre-Internet and Internet era). In comparison with the five previous market surveys, we did not find a statistically significant difference between species and availability of species offered for sale when comparing physical bird markets and online markets. In all data sets, biological factors such as wingspan were significant factors in explaining price variation. We conclude that sustained monitoring is needed to make direct comparisons between the trade platforms. With a continued increase of wildlife trade on online platforms, we recommend increased regulation and enforcement of wildlife trade laws.

show abstract

Machine learning for tracking illegal wildlife trade on social media

Cited by 138 publications

References 3 publications

Applications for deep learning in ecology

Applications for deep learning in ecology

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

Wildlife trade shifts from brick-and-mortar markets to virtual marketplaces: A case study of birds of prey trade in Thailand

Contact Info

Product

Resources

About