Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

Aim: Soil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. Location: Island of Ireland. Methods: We tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. Results: Using spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main Conclusions: Spatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.

Mercury (Hg) researches in Brazilian biomes: A scientometric analysis between the years 1991 and 2018

Mercury is a metal present in natural sources and its concentration have been allocated by human activities, posing impacts in environmental matrices and organisms. In this study, we highlight the panorama of Hg researches in Brazilian biomes in the scientific literature published between 1991 and 2018. Although the appreciated attention to the mercury researches in Amazon context, there has not been an assessment of Hg research within other Brazilian biomes. With this in mind, we searched for articles in the periodic database the Thomson’s ISI Web of Science database and observed an oscillated trend of the number of publications throughout this period. The low percentage of papers performed in Pantanal (8%), Caatinga (4%), Atlantic Rainforest (4%), Pampa (3%) and Cerrado (0.36%) was contrasted to majority academic works performed in Amazon regions (83%), indicating the asymmetrical geographical distribution in Hg researches. The five top keywords of research topics were mercury, Amazon, Brazil, river, Brazilian and fish. The top authors formed 19 different clusters, with different research directions and strengths. Our data shows that Hg studies have been geographically biased. The lack of Hg studies on other Brazilian biomes may mask the extent and intensity of the impacts related to the Hg mobilization in these areas. Concerning Hg concentration is context-dependent, we emphasize the need for investment in Hg research in different tropical biomes in order to overcome spatial bias of Hg knowledge, particularly with organisms from different environmental matrices. Keywords: Scientometry; Mercury; Brazilian biomes, Web of science (WoS)