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In Finland, the distribution area of the taiga tick, Ixodes persulcatus (Schulze, 1930), is nested within a broader area of distribution of a congeneric species, the sheep tick, Ixodes ricinus (Linnaeus, 1758) (Acari: Ixodidae). We assess whether distinct environmental adaptations or dispersal history provides a more parsimonious explanation for the differences in the distributions of the two common and medically important ixodids in Finland. We used an innovative spatially constrained randomization procedure to analyze whether crowdsourced occurrence data points of the two tick species had statistically different associations with any of the 28 environmental variables. Using points of presence in a region of species co-occurrence, we built Maxent models to examine whether environmental factors or dispersal history could explain the absence of I. persulcatus in a part of the range of I. ricinus in Finland. Five environmental variables-number of inhabitants, road length, elevation above sea level, proportion of barren bedrock and boulders, and proportion of unsorted glacial deposits-were significant at p  ≤ .05, indicating greater between-species difference in original than in the randomized data. Of these variables, only the optimum value for unsorted glacial deposits was higher for I. persulcatus than for I. ricinus . Maxent models also predicted high relative habitat suitability (suitability >80%) for I. persulcatus south of its current, sharply bounded distribution range, suggesting that the species has not fulfilled its distribution potential in Finland. The two most common and medically relevant ixodids in Finland may colonize habitats with different environmental conditions. On the contrary, the recent establishment and ongoing dispersion of I. persulcatus in Fennoscandia rather than environmental conditions cause the southernmost distribution limit of the species in Finland., Competing Interests: The authors declare that the research was conducted in the absence of any commercial, financial, or other relationships that could be construed as a potential conflict of interest., (© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Amazonia combines semi-continental size with difficult access, so both current ranges of species and their ability to cope with environmental change have to be inferred from sparse field data. Although efficient techniques for modeling species distributions on the basis of a small number of species occurrences exist, their success depends on the availability of relevant environmental data layers. Soil data are important in this context, because soil properties have been found to determine plant occurrence patterns in Amazonian lowlands at all spatial scales. Here we evaluate the potential for this purpose of three digital soil maps that are freely available online: SOTERLAC, HWSD, and SoilGrids. We first tested how well they reflect local soil cation concentration as documented with 1,500 widely distributed soil samples. We found that measured soil cation concentration differed by up to two orders of magnitude between sites mapped into the same soil class. The best map-based predictor of local soil cation concentration was obtained with a regression model combining soil classes from HWSD with cation exchange capacity (CEC) from SoilGrids. Next, we evaluated to what degree the known edaphic affinities of thirteen plant species (as documented with field data from 1,200 of the soil sample sites) can be inferred from the soil maps. The species segregated clearly along the soil cation concentration gradient in the field, but only partially along the model-estimated cation concentration gradient, and hardly at all along the mapped CEC gradient. The main problems reducing the predictive ability of the soil maps were insufficient spatial resolution and/or georeferencing errors combined with thematic inaccuracy and absence of the most relevant edaphic variables. Addressing these problems would provide better models of the edaphic environment for ecological studies in Amazonia.