Exploitation rates by humans and natural predators on marine and terrestrial prey species

A data file used in the majority of the figures and analyses that appear in the paper. It contains estimates of exploitation rate (n=2125) by humans and natural predators on prey species from marine and terrestrial ecosystems globally. Exploitation rates are expressed as proportion of the biomass removed from marine systems and as proportion of individuals removed from terrestrial systems. Please see the ReadMe file for more information

Proportion of mortality data for terrestrial systems

A data file with proportion of mortality data for terrestrial systems, used for generating Fig 1B. All cases include paired mortality estimates of humans and non-human predators on shared prey. This dataset includes a total of 99 cases, including cases from the larger predrate file (n=51) that have proportion of biomass estimates as well as cases (n=48) with only proportion of mortality estimates. This dataset is formatted for paired comparisons (wide format) and is included as a separate data file for convenience. Please see the ReadMe file for more information

R Code for human predator data analyses

An R file that includes code for generating the figures and analyses in the manuscrip

Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1. Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, has acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos contain valuable information for faunal and environmental monitoring, and are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2. In this study, we compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task in the context of an ichthyology class. Results were validated against counts obtained from a scientific expert. 3. All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4. As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques, as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development

expert_data

Contains all the data acquired by the PhD student referred as the expert. Details of the columns are as follows: Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

crowd_data

Contains all the data acquired by Citizen Scientists using the Digital Fisher crowdsourcing platform. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

students_data

Contains all the data acquired by the group of students. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

algorithm_data

Contains all the data acquired using the computer vision algorithm. Details of the columns are as follows: Video: Name of the video in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group