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RESEARCH PRODUCT

Curvilinear interspecific density-range size relationship in small mammals in Finland

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ABSTRACTAim Macroecological patterns have mainly been depicted as atemporal, withexisting research covering only short time periods. One fundamental pattern inmacroecology is the interspecific relationship between local abundance andregional range size, which is generally considered to be positively linear. Here,we examine structural details of the relationship between abundance and rangesize in cyclic populations of small mammals and its long-term temporal varia-tion.Location Finland.Methods We analysed 39 years of trapping data of Rodentia and Soricomor-pha collected in field and forest habitats across Finland. Abundance was mea-sured as the mean population density of individuals, and range size as thenumber of grid cells occupied and sample occupancy. Data were analysed usinglinear mixed models.Results The relationship between mean local density and sample occupancywas generally linear, whereas the relationship between density and the numberof grid cells occupied was generally curvilinear (U-shaped) for both habitats,being negative for species with small range sizes and positive for species withlarger ranges. The curvilinearity was temporally consistent in both habitats.Main conclusions The interspecific relationship between abundance andrange size varies depending on how range size is measured. Interestingly, thecurvilinear relationship between density and range size was found in an assem-blage in which many of the species have fluctuating (cyclic) populations.Future research on abundance–range size relationships should focus on long-term temporal variation to better understand the underlying mechanisms andto develop macroecological theory.KeywordsAbundance–distribution relationship, macroecology, population cyclicity, Rod-entia, Soricomorpha, synchrony, temporal variation.INTRODUCTIONThe interspecific relationship between local population den-sity and range size is a fundamental pattern in macroecology.Although this relationship varies considerably in sign, slopeand shape across realms, biogeographical regions, habitats,taxa and spatial scales, a positive log-linear relationshipbetween the two is generally observed (Blackburn et al.,2006; Borregaard & Rahbek, 2010).Macroecology can be defined as the analysis of emergentstatistical properties of large data sets (Brown, 1995) or asthe analysis of data from large geographical areas and longtemporal scales (Gaston et al., 2000; Fisher et al., 2010).Macroecological patterns have predominantly been depictedas atemporal, which may potentially obscure their dynamicsand inadvertently conceal the identity of underlying pro-cesses (Fisher et al., 2010). For example, one-year snapshotdata cannot reveal temporal consistency, and pooled datamay only represent emergent, average patterns (Turner et al.,1987; Kerr et al., 2007). Particularly regarding abundance–range size relationships over large geographical scales, tempo-ral analyses are in their infancy (but see Collins & Glenn,1194