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  • Author: I. I. Kozinenko x
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Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)

Abstract

Based on the maximum entropy modeling algorithm and using 12 environmental variables, we modeled the distribution of the vole twin species Microtus arvalis and M. levis, with particular attention to regions where the species overlap. For both species models performances were considered “excellent” (AUC > 0.9), although some occurrences appeared in areas of low habitat suitability, whereas in some areas of predicted high habitat suitability there were no occurrences. Apparently, both species do not fully occupy areas predicted to be favorable in terms of habitat suitability and persistence. Th e cause for such restriction are not the considered factors (including bioclimatic), but competitive interactions that prevent individuals of one species from expanding within the home range of the other. Contributions of the considered environmental variables for generating the potential distribution prediction were distinguished: for M. arvalis net primary production alone made the largest contribution (42 %), whereas for M. levis there was a cumulative effect of a number of factors.

Open access
Modelling the Bioclimatic Niche and Distribution of the Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), in Ukraine

Abstract

The Steppe mouse, Mus spicilegus, is endemic to Europe and found to be expanding its home range in recent years. In Ukraine there are indications a north- and eastwards expansion and/or reestablishment of M. spicilegus. We suggest that climatic conditions may be the primary factors that foster or limit the range expansion of M. spicilegus in Eastern Europe. Our objective was to complement the knowledge about the distribution of the species with an estimation of the potential distribution of the species in Ukraine using known occurrence sites (in Ukraine and neighbouring areas) and environmental variables in an ecological niche modelling algorithm. After accounting for sampling bias and spatial autocorrelation, we retained 73 occurrence records. The algorithm used in this paper, Maxent (Phillips et al., 2006), is a machine learning algorithm and only needs presence data, besides the environmental layers. Using this approach, we have highlighted the importance and significance of a number of bioclimatic variables, particularly those characterizing wintering conditions, under which higher mean temperatures enhance habitat suitability, whereas increased precipitation leads to an opposite effect. The broadly northwards shift of the home range of the species in Ukraine could generally be due to the increasing (since the 1980s) mean temperature of the winter season. We expect this expansion process will continue together with the changing climate and new records of locations of the species may be used for monitoring such change.

Open access