A specialist’s audit of aggregated occurrence records

Occurrence records for named, native Australian millipedes from the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA) were compared with the same records from the Millipedes of Australia (MoA) website, compiled independently by the author. The comparison revealed some previously unnoticed errors in MoA, and a much larger number of errors and other problems in the aggregated datasets. Errors have been corrected in MoA and in some data providers’ databases, but will remain in GBIF and ALA until data providers have supplied updates to these aggregators. An audit by a specialist volunteer, as reported here, is not a common occurrence. It is suggested that aggregators should do more, or more effective, data checking and should query data providers when possible errors are detected, rather than simply disclaim responsibility for aggregated content.     

A specialist’s audit of aggregated occurrence records: An ‘aggregator’s’ perspective

A recent ZooKeys’ paper (Mesibov, 2013: http://www.pensoft.net/journal_home_page.php?journal_id=1&page=article&SESID=df7bcb35b02603283dcb83ee0e0af0c9&type=show&article_id=5111) has highlighted data quality issues in aggregated data sets, but did not provide a realistic way to address these issues. This paper provides an aggregator’s perspective including ways that the whole community can help to address data quality issues. The establishment of GBIF and national nodes (national aggregators) such as the Atlas of Living Australia (ALA) have integrated and exposed a huge diversity of biological observations along with many associated issues. Much of the admirable work by Mesibov (2013) was enabled by having the data exposed.Data quality, one of the highest priorities for GBIF, the national nodes and other aggregators, depends on both automatic methods and community experts to detect and correct data issues. Not all issues can however be automatically detected or corrected, so community assistance is needed to help improve the quality of exposed biological data. We do need to improve the infrastructure and associated processes to more easily identify data issues and document all changes to ensure a full record is permanently and publicly available.     

Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision‐making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision‐making and planning.     

Macroecology in the age of Big Data – Where to go from here?

Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this “Big Data” trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (GfÖ). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space- and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data-driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.