Selecting site characteristics at different spatial and thematic scales for shrubby cinquefoil (Potentilla fruticosa L.) distribution mapping

Kalle Remm 1
  • 1 Institute of Ecology and Earth Sciences, University of Tartu, 46 Vanemuise St, 51014 Tartu, Estonia


The largest natural population of shrubby cinquefoil (Potentilla fruticosa) in the Baltic States was observed in the field to reveal the scale-dependent explanatory value of site characteristics for subsequent spatial distribution modelling of the species. About 700 km was crossed during field observations in 2008–2014. Thinning of the raw field records to ensure a distance of at least 50 metres between each point yielded 1459 presences and 7327 absences. These occurrence data were related to present and historical land cover, soil, elevation, human population density, the proportion of presence sites, and P. fruticosa mean coverage in the neighbourhood. Boosted classification tree models were used to compare the value of 60 individual site features at thematically and spatially different levels of generalization as indicators of the species’ presence or absence. P. fruticosa presence is significantly non-random regarding most of the studied site features but only a few of these are valuable predictors. The proportion of presences in the neighbourhood had the highest indicative value. P. fruticosa occurrence also coincides with moist thin calcareous soils according to the soil map, with larger scrubland patches according to the topographical database, and with tussock areas according to a topographical map from the 1930s. The explanatory value of nominal site characteristics primarily drops when the most indicative category is merged with other classes to form a more general category. Site characteristics calculated at the observation point are not always the most effective predictors for P. fruticosa occurrence – features of the neighbourhood are related to the occurrence as well. The study area was classified into: confirmed absence area, unclear presence/absence area and probable presence area. Subsequent distribution modelling in the unclear area should be targeted on a species presence/absence, while abundance could be the priority within the probable presence area.

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