We analyze through a climatic model the influence of regional warming on the geographical spreading and potential risk of infection of human dirofilariosis in Russia, Ukraine, and other post-Soviet states from 1981 to 2011 and estimate the situation by 2030. The model correctly predicts the spatiotemporal location of 97.10% of 2154 clinical cases reported in the area during the studied period, identified by a retrospective review of the literature. There exists also a significant correlation between annual predicted Dirofilaria generations and calculated morbidity. The model states the progressive increase of 14.8% in the potential transmission area, up to latitude 64°N, and 14.7% in population exposure. By 2030 an increase of 18.5% in transmission area and 10.8% in population exposure is expected. These findings strongly suggest the influence of global warming in both geographical spreading and increase in the number of Dirofilaria generations. The results should alert about the epidemiological behavior of dirofilariosis and other mosquito-borne diseases in these and other countries with similar climatic characteristics.

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A common strategy for sampling intraspecific genetic diversity is to maximize the sampling of geographically distinct populations. The objective of this study was to illustrate how geographic information coupled with geographic information systems (GIS) analysis can provide a new level of precision for establishing frameworks for sampling germplasm occurring in ecogeographically diverse sites. An exploration to collect forage germplasm in the western Caucasus Mountains in southern Russia, carried out jointly by the National Plant Germplasm System and the N.I. Vavilov Institute of Plant Industry in 1995, was used as a case study. The development of a GIs database and resulting map products and how these products were used to guide the sampling of geographically‐distinct areas is discussed. Information sources included Russian maps, orbital satellite imagery, remotely‐sensed elevation data and long‐term climate data from weather stations. A GIS database was developed and used to produce the following maps: soil classification, roads and trails, vegetation, and topography. Climate modeling techniques were used to develop maps reflecting climate zones. During the collecting trip, the strengths and weaknesses of the various map products became evident. The satellite imagery could be effectively used to identify potential meadow sites, but did not reflect more recent anthropogenic disturbance. Soil maps comprised of original Russian agricultural soil maps failed to reflect the extreme heterogeneity of soil types. Assessment of map‐based and site‐specific geographic features during the collection trip provided collectors with an increased understanding of how the physical features of the collection landscape may have influenced the geographic differentiation of 75% of the germplasm accessions collected.

Without committing significant resources in the decision‐making process, curators are challenged to identify among newly collected germplasm, exceptional accessions that merit inclusion in ex situ collections. The objectives of this paper were to illustrate how geographic information can be used to infer (i) site uniqueness relative to other sites sampled, (ii) the likelihood that an accession reflects adaptation to the site, and (iii) uniqueness of a given accession relative to other accessions of the same taxon collected. Forage legume germplasm was collected from the western Caucasus Mountains in southern Russia in 1995. Prior to the trip, a database was developed from geographic information system software that included data from Russian maps, orbital satellite imagery, remotely sensed elevation data, and long. term climate data from weather stations. Data were collected characterizing the collection sites. A map was developed that partitioned the collection area into standard landscape units from the GIS‐derived climate data. The cross‐classification map was used to assess collection coverage and identify sites that had similar ecogeographic characteristics. Germplasm was collected from 41% of the possible moisture and temperature zone combinations. Site redundancy was also identified. Local passport data, GIS‐derived data and the cross‐classification map were used to identify accessions of Trifolium pratense L. adapted to acid soil by inferring the influence of micro evolutionary forces such as selection and gene flow that could lead to geographic differentiation. Of 36 accessions, six met the test of being collected from undisturbed sites with acidic soils, being the sole representatives of unique climate classes and having been isolated from other T. prutense accessions. The post‐collection analysis provided a cost‐effective way of determining the accessions that warranted inclusion into the NPGS T. pratense germplasm collection.

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