Networks of citizen scientists (CS) have the potential to observe biodiversity and species distributions at global scales. Yet the adoption of such datasets in conservation science may be hindered by a perception that the data are of low quality. This perception likely stems from the propensity of data generated by CS to contain greater levels of variability (e.g., measurement error) or bias (e.g., spatio-temporal clustering) in comparison to data collected by scientists or instruments. Modern analytical approaches can account for many types of error and bias typical of CS datasets. It is now possible to (1) describe how the sampling process influences the overall variability in response data using mixed-effects modeling, (2) integrate data to explicitly model the sampling process and account for bias using a hierarchical modeling framework, and (3) examine the relative influence of many different or related explanatory factors using machine learning tools. Information from these modeling approaches can further be incorporated into predictions of species distributions and estimates of biodiversity. By detailing how CS data are generated, patterns can be discerned from complex datasets that are unevenly distributed and collected by many observers with varying skill levels. Even so, gaining the full potential from even the best designed CS projects requires meta-data describing the sampling process, reference data to allow for standardization, and insightful modeling suitable to the type of response data of interest.

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International audienceSatellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide-ranging meso- and top-predator species: Adélie Pygoscelisadeliae and emperor Aptenodytes forsteri penguins, light-mantled albatross Phoebetria palpebrata , Antarctic fur seals Arctocephalus gazella , southern elephant seals Mirounga leonina , and Weddell seals Leptony-chotes weddellii . The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea-ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. Th e results emphasize the importance of on-shore and sea-ice processes to Antarctic marine ecosystems. Our study provides ocean-basin-scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts

These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations.Scientific Data | (2020) 7:94 | https://doi.org/10.1038/s41597-020-0406-x www.nature.com/scientificdata www.nature.com/scientificdata/ circum-Antarctic synthesis yet exists that crosses species boundaries. This deficiency prompted the Expert Group on Birds and Marine Mammals (EG-BAMM) and the Expert Group on Antarctic Biodiversity Informatics (EGABI) of the Scientific Committee on Antarctic Research (SCAR; www.scar.org) to initiate in 2010 the Retrospective Analysis of Antarctic Tracking Data (RAATD). RAATD aims to advance our understanding of fundamental and applied questions in a data-driven way, matching research priorities already identified by the SCAR Horizon Scan 9,21 and key questions in animal movement ecology 22 . For these reasons, we worked on the collation, validation and preparation of tracking data collected south of 45 °S. Data from over seventy contributors (Data Contacts and Citations 23 ) were collated. This database includes information from seventeen predator species, 4,060 individuals and over 2.9 million at-sea locations. To exploit this unique dataset, RAATD is undertaking a multi-species assessment of habitat use for higher predators in the Southern Ocean 24 .RAATD will provide a greater understanding of predator distributions under varying climate regimes, and provide outputs that can inform spatial management and planning decisions by management authorities such as the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR; www.ccamlr.org). Our synopsis and analysis of multi-predator tracking data will also highlight regional or seasonal data-gaps.Scientific Data | (2020) 7:94 | https://doi.

Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly using technologies, such as remotely piloted aircraft (RPA; commonly known as “drones,” unmanned aerial systems or unmanned aerial vehicles), for wildlife monitoring applications. Although RPA are widely touted as a cost‐effective way to collect high‐quality wildlife population data, the validity of these claims is unclear. Using life‐sized, replica seabird colonies containing a known number of fake birds, we assessed the accuracy of RPA‐facilitated wildlife population monitoring compared to the traditional ground‐based counting method. The task for both approaches was to count the number of fake birds in each of 10 replica seabird colonies. We show that RPA‐derived data are, on average, between 43% and 96% more accurate than the traditional ground‐based data collection method. We also demonstrate that counts from this remotely sensed imagery can be semi‐automated with a high degree of accuracy. The increased accuracy and increased precision of RPA‐derived wildlife monitoring data provides greater statistical power to detect fine‐scale population fluctuations allowing for more informed and proactive ecological management.

The reliable estimation of animal location, and its associated error is fundamental to animal ecology. There are many existing techniques for handling location error, but these are often ad hoc or are used in isolation from each other. In this study we present a Bayesian framework for determining location that uses all the data available, is flexible to all tagging techniques, and provides location estimates with built-in measures of uncertainty. Bayesian methods allow the contributions of multiple data sources to be decomposed into manageable components. We illustrate with two examples for two different location methods: satellite tracking and light level geo-location. We show that many of the problems with uncertainty involved are reduced and quantified by our approach. This approach can use any available information, such as existing knowledge of the animal's potential range, light levels or direct location estimates, auxiliary data, and movement models. The approach provides a substantial contribution to the handling uncertainty in archival tag and satellite tracking data using readily available tools.

1. Light-level geolocator tags use ambient light recordings to estimate the whereabouts of an individual over the time it carried the device. Over the past decade, these tags have emerged as an important tool and have been used extensively for tracking animal migrations, most commonly small birds.

1Ecologists are increasingly using technology to improve the quality of data collected on wildlife, 2 particularly for assessing the environmental impacts of human activities. Remotely Piloted 3 Aircraft Systems (RPAS; commonly known as 'drones') are widely touted as a cost-effective 4 way to collect high quality wildlife population data, however, the validity of these claims is 5 unclear. Using life-sized seabird colonies containing a known number of replica birds, we show 6 that RPAS-derived data are, on average, between 43% and 96% more accurate than data from 7 the traditional ground-based collection method. We also demonstrate that counts from this 8 remotely sensed imagery can be semi-automated with a high degree of accuracy. The 9 increased accuracy and precision of RPAS-derived wildlife monitoring data provides greater 10 statistical power to detect fine-scale population fluctuations allowing for more informed and 11 proactive ecological management. 12