Areas of high diversity with threatened species

Training-package
Publications
Species profiles
- 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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Source: Prydatko V., Shtepa Yu., Ischuk A. and Petrochenko O. Areas of high diversity with threatened species/BINU Ukraine Project Extract: National ‘State of Ecosystem’ Report (Agrobiodiversity)…IFS Examples/Agrobiodiversity of Ukraine: Theory, Methodology, Indicators, Examples. Book 2 (Ukr). Kyiv: ‘Nichlava’. 2005.- pp.576-579 (Eng). Use and interpretation Users and key questions for which the indicator is intended (if appropriate, comment on whether it is a pressure, state or response indicator, recognizing that one indicator may fill several roles in this respect) Potential users of this indicator include authorized bodies of executive power on ecology and natural resources, the National Commission on the Red Data Book of Ukraine, hunting and fish management institutions and their local representatives, local state administrations; the beneficiary and the recipients of the UNEP-GEF Biodiversity Indicators for National Use (BINU) Project, educational institutions where ecology is studied, etc. The indicator helps answer the following key questions: “What lands could be returned to a natural state in the near future?”, “What are the main factors that cause agrobiodiversity loss or increase, and how do the changes in land use practices influence agrobiodiversity loss or increase?”, and “How can scenarios of agrobiodiversity change be created and how can biodiversity loss be stopped in the near future?” The indicator refers to the indicators of state (S) and response (R). Scale of appropriate use and potential for aggregation The entire territory of Ukraine, as well as oblasts, districts, natural and agricultural zones, etc. Meaning and potential causes of upward and downward trends Theoretically, the less identified “hot spot” territories, the better the state of biodiversity (agrobiodiversity) is. GIS and a modern map of agro-landscapes of Ukraine were used with the help of RS data to combine “point” habitats of the Red Data Book listed species with agricultural surfaces [1]. According to these updated data, up to 45% of the Red Data Book listed plant habitats and 47% of animal habitats (profiles of which can be found in 1994-1996 issues of the Red Data Book of Ukraine) intersect with today’s active agricultural surfaces – see Annex 1. At the same time, nowadays, it is still difficult to say how the situation changes for several reasons. Some publications say that the number of Red Data Book listed species in Ukraine will continue to increase – see our materials concerning the indicator Antropogenic Pressure Value: The Red Data Book Species Response to the Antropogenic Pressure (or RDB-Response Index)[2]. At the same time, there are a number of state programs that may improve the situation (i.e. ecological network development programs, land conservation programs, etc.). These approaches will ensure better planning of nature protection measures related to agro-landscapes as well as provide modern mapping. Implications for biodiversity management This topic is constantly being discussed and studied in Ukraine. The critical question is how to work with these “hot” territories on a regular basis and to indicate them on the map effectively. The BINU Project for the first time used remote sensing data to develop a relevant thematic map [1,3]. The indicator and its presentation in the form of a map will, certainly, be further taken into account while implementing the Concept for Perspectives of Use, Conservation and Reproduction of Agrobiodiversity in Ukraine. The main provisions of this Concept were published in 2003 [4]. The given indicator is not included into the list of indicators compiled by the Subsidiary Body on Scientific-Technical and Technological Assistance (SBSTTA) within the Convention on Biological Diversity (CBD). However, many countries use it to provide information about habitats of threatened species [5]. Units in which it is expressed (for example, km2, number of individuals, % change) In our case, a thematic map generated with the help of GIS was used to generalize the information. Here, an ArcMap option called “completely contain” was applied to identify the groups of “point” objects within the polygonal object. As shown above, the unit of measurement, in this case, can be a fraction and/or percentage of species or their “point” habitats, depending on the choice of the l user. Description of source data (origins, units, sample size and extent, custodians) The indicator is developed using data from the Red Data Book of Ukraine, other scientific publications, the annual state statistical reporting (Form 1- reserve), and modern RS and GIS materials. Calculation procedure (including appropriate methods and constraints for aggregation) GIS methods were applied, and a modern map of agro-landscapes of Ukraine was generated with the help of RS data [1]. Such approach was first initiated by the BINU Project [1,3]. Most effective forms of presentation (graph types, maps, narratives, etc. – give examples if possible) See Figure 1 and Annex 1. Data source and process for updating The indicator is developed with data from the Red Data Book of Ukraine, other scientific publications, the annual state statistical reporting (Form 1- reserve), and modern RS and GIS materials. Closely related indicators These are the following indicators: Territories for Monitoring of Wild Species Associated with Agro-landscapes and Antropogenic Pressure Value: The Red Data Book Species Response to the Antropogenic Pressure (or RDB-Response Index). For more details, please see the BINU web-page at http://www.ulrmc.org.ua/services/binu/is/Board.asp?quest=2&TextName=〈=EN. Additional information and important comments The obtained examples were demonstrated to the participants of the conference Understanding the Linkages Between Poverty and Biodiversity, which was held in Ecuador on November 24-26, 2004, as well as to the participants of the 3rd Working Group Meeting of the BINU Project in Kyiv on December 17, 2004. Based on observations and combinations of maps, there was also found out that distribution of the given “point” habitats and density of land cover classes (LCC) are closely correlated: the habitats are more widely distributed in the western part of Ukraine, where the density of LCCD is greater (fig. 1). This important information can be further applied, for example, in mapping nature protection measures and designing elements of the ecological network of Ukraine. __ The compilers express sincere gratitude to everyone who at different times provided assistance to ULRMC in search and processing of various materials concerning “point” habitats of the Red Data Book of Ukraine species, including the information related to reserve territories. We are grateful to A. Grachov, O. Dudkin, A. Kozlova, Y. Sapiton, R. Burda, and many others. Additional sources of information 1. Созинов А.А., Штепа Ю.Н, Придатко В.И. Агросфера как объект целевого исследования с помощью ДЗЗ и ГИС для улучшения управления территориальным развитием и сохранения природного биоразнообразия / Ученые записки Таврического национального университета им. В.И.Вернадского, Серия «География», Том 17 (56), №2 (2004), с. 72-87 2. Буравльов та ін. Сила антропогенного тиску: відповідь червонокнижних видів на антропогенний тиск (або RDB-індекс)/Агробіорізноманіття України: теорія, методологія, індикатори, приклади. Книга 2. —Київ: ЗАТ “Нічлава”. 2005.-С.235-240. 3. BINU Ukraine National Report on Project Experience (2002-2004): Agro-biodiversity Indicators Development and Use. December 2004. v. 1. Kyiv (Prepared by Dr. Vasyl Prydatko and Yulia Apetova (ULRMC)). 4. Перспективи використання, збереження та відтворення агробіорізноманіття в Україні. (Відповідальні редактори акад. УААН, проф. Патика В.П., д.б.н., проф. Соломаха В.А.). Київ: “Хімджест”, 2003, 254 с. 5. Indicators and Environmental Impact Assessment. UNEP/CBD/SBSTTA/7/12, September 20, 2001. P.21. 6. Червона книга України: тваринний світ. К.: “Українська енциклопедія”, 1994, 457 с. 7. Червона книга України: рослинний світ. К.: “Українська енциклопедія”, 1996, 603 с. MSA-LCCD comparison (Adobe flash) [new window is required] Prepared by: G.Kolomytsev

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