The Modelling Leaflet (MSA, LCCD, etc.) | BioModel.info: Biodiversity modelling, Species distribution models (SDMs), Spatial analysis, Education

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- BeechBeech: model of expected distribution in 2050 by GLM (forced by climate) . current potential distribution Differences in current distribution map and distribution (see please picture above), predicted by model (based on climatic requirements, modelled by GLM) are relatively significant. . The map shows expected changes in Beech (Fagus sylvatica) distribution to 2050 due to climate changes. Map visualize appearance of the climatic suitable territories for beech on east to 2050 (which is actually steppe and potentially can’t be acceptable for Fagus sylvatica survival by other options). Note please that not all of the predicted “potentially suitable” (by climate parameters) territory contain suitable habitats. Model is based on GLM methodology. Input climate data derived from IMAGE model, distribution map compiled based on Biodat maps. . . Map authors: G.Kolomytsev, V.Prydatko Last update: June 09, 2008. . Beech (Fagus sylvatica L.) habitat changes modelling result demonstrates: i) possible enlargement (restoration) of the species natural habitats – Scenario #1, and ii) possible artificial enlargement of the habitats to the East ‘for cost’ of settlements i.e. its green zones (gardens, parks etc.) – in the GLOBIO Ukraine Region. See full animated story about trends: Fagus sylvatica L. areal changes were modelled with usage…
- Brown bearBrown Bear (Ursus arctos) habitats change modelling in EEBIO Region in scope of the IMAGE climate change data: GLM-scenarios by 2050 Ursus arctos L.: natural habitat changes and accidental GSF-response by settlements based on open Internet sources, GSF software and RS- and GIS-analyze (1970, 2007) GSF is an additional searchable test-program made in ULRMC for the project purposes [1], and which used available Internet sources. GSF calculates number of causes, when Latin names of selected species were connected with a GIS-name of a settlement plus a year (for a concrete territory): ‘Ursus arctos 2007’, Ursus arctos 1970’ etc. A basic authors’ hypothesis was that series of respective settlements may adjoin ‘natural’ or ‘past-natural’ habitats of the species or respective species migratory areas. There were 20891 settlement names in the GSF-GIS test and that was overlapped with modeled U.arctos natural habitat based on RS data and special GIS analyze [2]. The map in GIS showed that during 1970-2007 number of cases of the responses increased (against a background of slowly destroyed original habitats!) Authors suppose that it is not only result of increasing of awareness of local people or mass media, but also result of the species movement, and which populations…
- Eurasian ElkAlces alces: model of expected distribution in 2050 by GLM (forced by climate) . current potential distribution (note that for lagre mammal besides climate options spacious habitats presence are very important, so even in 50×50 km pixel elk can be absent even if climate is acceptable) Additionally, supported populations of elk can be found in Ukraine, which do not reflect natural distribution (and do not included into model). . The map shows expected changes in elk (Alces alces) distribution to 2050 due to climate changes. Distribution data from 1970s to 2000 based on EEBIO project materials compiled from different sources. Prediction in elk distribution to 2050 modelled by IMAGE climate predictions using GLM approach. . . Map authors: G.Kolomytsev, V.Prydatko Last update: June 10, 2008. The map reflects key data based on: BioDAT (in Russian) – www.biodat.ru, prepared by A. Puzachenko; Russian Federation Mammals (in Russian) – www.sevin.ru, prepared by B.Sheftel; and the data of Ukraine State Statistics Committee The integrated map shows probable changes of the species area (expansion to the North and to the South) during last 25 years may be bacause of serious forest area changes and infrasrtucture increasing. Alces alces (elk) climate envelope building (continuing) in…
- Pedunculate OakPedunculate Oak (Quercus robur): Climate Change and Cartographical and GLM-distribution Modelling by 2030 and 2050 Case-research #1 The model is published with the assistance of ULRMC, funded by ‘Projection of Species- and Species-Climate Based Models on to the GLOBIO Ukraine Region, and Scenarios Development’ Project according to the Contract Е/555050/01/МО (2006) between The Netherlands Environmental Assessment Agency (MNP, Bilthoven) and International Association ‘Ukrainian Land and Resource Management Center’ (ULRMC, Kyiv). Quercus robur (Fagaceae) – current and predicted distribution modelling The table shows a difference: ‘current distribution’ contrary to ‘predicted distribution’ by the model (based on climatу change data i.e. GLM-scenario). The map shows expected changes in distribution of Q. robur by 2050 due to well known climate changes. Map visualizes a shift of expected potentially-suitable-habitats of the species to the north. The model is based on GLM method and R-statistics. The basic climate change data is derived from an IMAGE model package. The integrated distribution map was compiled with usage of data from local sources of the information as well as the Flora Europaea Atlas map. Map authorы: G.Kolomytsev (IZ NASU), V.Prydatko (ULRMC. Published on: May 12, 2008. Last updated on: May 1, 2009. Case-research #2 Quercus robur (Fagaceae): overlap…
- Red squirrelRed squirrel (Sciurus vulgaris) habitats change modelling in EEBIO Region in scope of the IMAGE climate change data: GLM-scenario by 2050 V.Makarenko, V.Prydatko, G.Kolomytsev Sciurus vulgaris Linnaeus, 1758 red squirrel, eurasian red squirrel ardilla roja (spanish), ecureuil roux (french) vyvirka, vyvirka lisova (ukr) Red Squirrel is most abundant species in large tracts of coniferous forest and also occurs in deciduous woods, mixed forest, parks, gardens, and small stands of conifers. Its diet is mainly vegetarian, consisting of seeds, acorns, fungus, bark, and sapwood, although it occasionally takes animal prey (young birds and eggs). The main expected threats to this species in EEBIO (and GLOBIO Ukraine) region are habitat loss and fragmentation. Simple comparison of basic ‘1980s’ and ‘1990s’ maps in GIS environment demonstrated probable changes of species areal. Official local statistics in Ukraine state that the trend looks stable. Based on our polynomial approximation test of 3th-degree (R2=0.97) we could argue that it would not be stable by 2050. GLM scenario shows that the species habitats have migrate to the northern areas (N) and partly to Crimea; finally, the areal will be smaller in GLOBIO Ukraine Region. See more with a summarized table on BioModel web-page. The model is published…
- Stone morokoStone moroko Pseudorasbora parva Temminck & Schlegel, 1846 (Cypriniformes, Cyprinidae) SYNONYMS Fundulus virescens, Leuciscus parvus, Micraspius mianowskii, Pseudorasbora altipinna, Pseudorasbora depressirostris, Pseudorasbora fowleri, Pseudorasbora monstrosa, Pseudorasbora parva parvula, Pseudorasbora parva tenuis, Pseudorasbora parvus Common names: Stone moroco, topmouth gudgeon, false razbora (GB), Blaubandbarbling, Amurbarbling, Pseudokeilfleckbarbe, Asiatischer Grundling (DE), Bandgrundling (DK), Ebarasboora (EE), Rytinis gruzlelis (LT), Amuras cebaceks (LV), czebaczek amurski (PL), Tschebatchek, Cebacok amurskij (RU). Stone moroko is a fish belonging to the Cyprinid family, native to Asia, but introduced and now considered an invasive species in Europe. The fish’s size is rarely above 8cm and usually 2 to 7.5cm long. Minimum temperature for reproduction is 15-19oC. Native range The native range of P. parva is the East Asian subregion including the basins of the rivers Amur, Yang-tze, Huang-ho, Japanese islands (Kiusiu, Sikoku and the southern and central parts of Honsiu), western and southern parts of the Korean Peninsula and Taiwan (Minkiang river system). In Europe it was first recorded in 1961 from southern Romania and Albania. In 1972 the species was recorded from the European part of the former USSR – the Danube delta and Dniester. In slightly over 40 years it has almost entirely colonised Europe, proceeding rapidly…
- All profilesThis BioModel-web section includes some separate description (examples) of species profiles, which reflects unique details and outcome of the modelling as well as other regional and local supportive details i.e. alternative scenarios. (Note: see more updated GLM-species-scenarios as for May 2009 here as well as modelling summary and/or data for downloads). The expected examples will take into account two key causes of change i.e. ‘land use’ [LU] and ‘climate change’ [CC]. Some previous regionally sound research have demonstrated that LU-pressure could be much more intensive contrary to that ‘CC-pressure’ or it would be a combination of these both factors. On the other hand that distribution shifts are more distinct on regional or global scale due to climate change. Proposed profiles will content respective examples and descriptions. For the EEBIO Project BioModel team produced and incorporated more local scientific data into updated ‘EcoProfile 2.1′ data base of MNP (RIVM). Now the package contents 167 species profiles including 90 new species that were entered during February-May (mammals – 27, vascular plants – 25, invertebrates – 12). Population trends were lighted in points (i.e. from 0 to 100) and in scope of the natural zones: Carpathian Mountainous, Crimea Mountainous, Ukrainian Steppe, Ukrainian Forest-Steppe and Polissya…
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There is the Leaflet on BioModelling Experience in Ukraine leaflet.pdf

The MSA is an indicator of the remaining mean species abundance of original species, relative to their abundance in primary vegetation. It can be interpreted as a measure of “naturalness” or “intactness”. MSA is calculated based on the following pressures: land use change, infrastructure impact, fragmentation level, nitrogen deposition and climate change. In the GLOBIO-3 model the MSA is calculated according to following formula MSA_Xi= MSA_LUi*MSA_Ni*MSA_Ii*MSA_Fi*MSA_CCi, where i is the index of the grid-cell, MSA_Xi is the relative mean species abundance corresponding to the following drivers and indices: LU (land-cover/use), N (atmospheric N deposition), I (infrastructure change), F (fragmentation) and CC (climate change). More details: Prydatko V., Kolomytsev G., Burda R., Chumachenko S. Landscape ecology: textbook on application of pressure based biodiversity modelling for national and regional educational purposes. Part 1 and Part 2.– Kyiv: NAU, 2008. — 100 pp.

Note. In a convenient format for self-study and self-testing for the first time for Ukrainian higher educational institutions the bilingual textbook (English-Ukrainian) provides the history of the issue, approaches and examples of thematic mapping-oriented modelling of the expected biodiversity taking into account the impacts on it within the context of GLOBIO Ukraine Region. It is based on rich groundwork of both national and foreign schools of modern landscape ecology, in their comparison. GLOBIO approaches are revealed for Ukrainian ecologists for the first time. The textbook also provides such approaches to biodiversity modelling as MSA, CLUE, EEBIO, LCCD_Bio, RS-GIS in terms of the region that are still little known to Ukrainian ecologists. After each unit there are questions for progress testing (in total 100). In annexes, there are 30 testing assignments and answer keys as well as examples of topics for independent, bachelor’s and master’s degree papers. The issue has a list of 169 sources of information, which will ease the use of testing assignments and will be a good resource for further self-study of future ecologists. The textbook is also a contribution to the development of bioticgeoinformation science as a new trend in ecology. Ukrainian Land and Resource Management Center (ULRMC) 13 Chokolivsky Boulevard, Kyiv 03186, Ukraine Tel.: (+38-044) 230-2266; Fax: (+38-044) 230-2267. ULRMC Web: http://www.ulrmc.org.ua/