References
- Aden, C, Kleppin, L, Schmidt, G and Schröder, W 2008.
WaldIS – a web-based reference data system for the forests in Germany . In: Car, A, Griesebner, G and Strobl, J (Hrsg.), Geospatial Crossroads @ GI_Forum ‘08: Proceedings of the Geoinformatics Forum Salzburg, 1–10. – Wichmann, Heidelberg. - Aden, C, Kleppin, L, Schmidt, G and Schröder, W. 2009.
Consolidation, visualisation and analysis of forest condition relevant data in the WebGIS WaldIS . In: Strobl, J, Blaschke, Th and Griesebner, G (eds.), Angewandte Geoinformatik 2009, 506–515. Wichmann, Heidelberg. - Aden, C, Schmidt, G, Schönrock, S and Schröder, W. 2010. Data analyses with the WebGIS WaldIS. European Journal of Forest Research, 129: 489–497. DOI: 10.1007/s10342-010-0370-7
- Aden, C, Schmidt, G and Schröder, W. 2007a.
A web-based geographical information system for the monitoring of genetically modified organisms . In: Breckling, B, Dolek, M, Lang, A, Reuter, H and Verhoeven, R (eds.), GMO-Monitoring vor der Umsetzung, 97–112. Conservation and biological diversity. - Aden, C, Schmidt, G and Schröder, W. 2007b. A web-based geo-information system for the monitoring of genetically modified organisms. E-Journal of Informatics in Agriculture 2.
http://www.ezai.org/index.php/eZAI/article/view/26/26 . - Aden, C, Schmidt, G and Schröder, W. 2007c. WebGIS GMO Monitoring. Journal of Consumer Protection and Food Safety, 1: 62–64. DOI: 10.1007/s00003-007-0239-y
- Aden, C, Schmidt, G and Schröder, W. 2007d. A web-based GIS for the monitoring of genetically modified organisms. In: Probst, F and Keßler, C (eds.), GI-Days-Young researchers forum. Posterreader of the 5th Geographic Information Days 10–12, 1–2. September, Münster.
- AdW (Alliance of German Science Organisations). 2010. Principles for dealing with research data. Berlin.
https://www.ratswd.de/download/RatSWD_WP_2010/RatSWD_WP_156.pdf [Last accessed 12 February 2018]. - Aschenbrenner, A and Neuroth, H. 2011.
Research data repositories . In: Handbuch Forschungsdatenmanagement, 101–114. 1st edition, Bad Honnef: Bock und Herchen Verlag. - Backhaus, K, Erichson, B, Plinke, W and Weiber, R. 2000. Multivariate analytical methods. An application-oriented introduction. 9th, revised and extended edition. Springer, Heidelberg, New York. DOI: 10.1007/978-3-662-08893-7
- Brown, DG, Aspinall, R and Bennett, DA. 2006. Landscape models and explanation in landscape ecology – a space for generative landscape science? The Professional Geographer, 58: 369–382. DOI: 10.1111/j.1467-9272.2006.00575.x
- Bühler, K. 1936. The structural model of language. Travaux du Cercle Linguistique de Prague, 6: 3–12.
- Chavan, V and Penev, L. 2011. The data paper: a mechanism to incentivize data publishing in biodiversity science. BMC Bioinformatics, 12(Supl. 15): S2. DOI: 10.1186/1471-2105-12-S15-S2
- Dale, MRT and Fortin, MJ. 2009. Spatial autocorrelation and statistical tests: Some solutions. Journal of Agricultural, Biological, and Environmental Statistics, 14: 188–206. DOI: 10.1198/jabes.2009.0012
- DDGI (Deutscher Dachverband für Geoinformation e.V.). 2007. Model for the description of the quality of geodata products (PAS 1071:2007-10). Beuth Publishing House, Berlin.
- Deppert, W and Theobald, W. 1998.
A Philosophy of Science of Interdisciplinarity. To lay the foundation for integrative environmental research and assessment . In: Daschkeit, A and Schröder, W (eds.), (1998 Perspektiven integrativer Umweltforschung und -lehre. Environmental Natural and Social Sciences, 75–106. Schröder, W, et al. (eds.). Springer, Berlin. DOI: 10.1007/978-3-642-58801-3_5 - DFG (German Research Foundation). 2015. Guidelines for the Handling of Research Data. Bonn Bad Godesberg.
http://www.dfg.de/download/pdf/foerderung/antragstellung/forschungsdaten/richtlinien_forschungsdaten.pdf [Last accessed 12 February 2018]. - Dormann, CF. 2009. Response to comment on ‘Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 32: 379–381. DOI: 10.1111/j.1600-0587.2009.05907.x
- Dormann, CF, McPherson, JM, Araújo, MB, Bivand, R, Bolliger, J, Carl, G, Davies, RG, Hirzel, A, Jetz, W, Kissling, WD, Kühn, I, Ohlemüller, R, Peres-Neto, PR, Reineking, B, Schröder, B, Schurr, FM and Wilson, R. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30: 609–628. DOI: 10.1111/j.2007.0906-7590.05171.x
- Dungan, JL, Perry, JM, Dale, MRT, Legendre, P, Citron-Pousty, S, Fortin, MJ, Jakomulska, A, Miriti, M and Rosenberg, MS. 2002. A balanced view of scale in spatial statistical analysis. Ecography, 25: 626–640. DOI: 10.1034/j.1600-0587.2002.250510.x
- Erdmann, HW and Petersen, J. 1975. Strukturen empirischer Forschungsprozesse – Einführung in die wissenschaftstheoretischen, methodologischen und statistischen Grundlagen empirisch-pädagogischer Forschung (Structures of Empirical Research Processes – Introduction to the Theoretical, Methodological and Statistical Foundations of Empirical-Pedagogical Research). Two volumes. A. Henn, Ratingen.
- Fortin, JM, Dale, MT and ver Hoef, J. 2002.
Spatial analysis in ecology . In: El-Shaarawi, AH and Piegorsch, WW (eds.), Encyclopedia of environmetrics, 2051–2058. John Wiley & Sons, Ltd, Chichester. DOI: 10.1111/j.1600-0587.2009.05907.x - Fortin, MJ, Drapeau, P and Legendre, P. 1989. Spatial autocorrelation and sampling design in plant ecology. Vegetation, 83: 209–222. DOI: 10.1007/BF00031693
- Fränzle, O. 1971. Physical geography as quantitative landscape research. Kieler Geographische Schriften, 37: 297–312.
- Gorelick, R. 2011. What is theory? Ideas in Ecology and Evolution, 4: 1–10. DOI: 10.4033/iee.2011.4.1.c
- Grießer, S. 2016.
Peat replacement substrates for commercial horticulture – A contribution to sustainable land use in Lower Saxony . Thesis (PhD) Vechta 1–143 + Anh., Vechta, 2016. DOI: 10.23660/voado-4 - Harmens, H, Mills, G, Hayes, F, Sharps, K, Frontasyeva, M, Schröder, W, Nickel, S, Meyer, M and the participants of the ICP Vegetation. 2015.
Air Pollution and Vegetation, Annual Report 2014/2015 . Bangor, Gwynedd, UK: ICP Vegetation Programme Coordination Centre, Centre for Ecology and Hydrology, Environment Centre Wales. - Harmens, H, Norris, DA, Steinnes, E, Kubin, E, Piispanen, J, Alber, R, Aleksiayenak, Y, Blum, O, Coşkun, M, Dam, M, De Temmerman, L, Fernández, JA, Frolova, M, Frontasyeva, M, González-Miqueo, L, Grodzińska, K, Jeran, Z, Korzekwa, S, Krmar, M, Kvietkus, K, Leblond, S, Liiv, S, Magnússon, SH, Maňkovská, B, Pesch, R, Rühling, Å, Santamaria, JM, Schröder, W, Spiric, Z, Suchara, I, Thöni, L, Urumov, V, Yurukova, L and Zechmeister, HG. 2010. Mosses as biomonitors of atmospheric heavy metal deposition: spatial and temporal trends, Europe. Environmental Pollution, 158: 3144–3156. DOI: 10.1016/j.envpol.2010.06.039
- HRK (German Rectors’ Conference). 2015. Recommendation of the 19th General Assembly of the German Rectors’ Conference of 10 November 2015: How university management can steer the development of research data management. Orientation paths, options for action, scenarios. Bonn.
https://www.hrk.de/uploads/tx_szconvention/Recommendation_Research Data Management__final_Stand_11.11.2015.pdf [Last accessed 12 February 2018]. - ICP Vegetation (International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops). 2014. Monitoring of atmospheric deposition of heavy metals, nitrogen and POPs in Europe using bryophytes. Monitoring manual 2015 survey. United Nations Economic Commission for Europe Convention on Long-Range Transboundary Air Pollution. Bangor, Wales, UK: ICP Vegetation Moss Survey Coordination Centre, Dubna, Russian Federation, and Programme Coordination Centre.
https://icpvegetation.ceh.ac.uk/publications/documents/MossmonitoringMANUAL-2015-17.07.14.pdf [Last accessed 12 February 2018]. - ISO (International Organization for Standardization). 2005. Geoinformation – Quality Principles (DIN EN ISO 19113), English Version, Edition: 2005-05. Berlin: Beuth Verlag.
- ISO (International Organization for Standardization). 2014a. Geoinformation – Metadata – Part 1: Principles (ISO 19115-1:2014). Berlin: Beuth Publishing House.
- ISO (International Organization for Standardization). 2014b. Geographic information – Data quality (EN ISO 19157), English version, 2014–04. Berlin: Beuth Publishing House.
- ISO (International Organization for Standardization). 2014c. System and software engineering – Quality criteria and evaluation of system and software products (SQuaRE) – Guidelines for SQuaRE (ISO/IEC 25000, 2014-03). Berlin: Beuth Publishing House.
- Kleppin, L, Aden, C, Pesch, R, Schmidt, G and Schröder, W. 2007a.
Development and testing of a metadata and WebGIS application for nationwide Unece moss monitoring . In: Strobl, J, Blaschke, T and Griesebner, G (eds.), Angewandte Geoinformatik 2007, 344–353. Wichmann, Heidelberg: Contributions to the 19th AGIT Symposium. - Kleppin, L, Aden, C, Pesch, R, Schmidt, G and Schröder, W. 2008a.
‘MossMet’ – A WebGIS based on open source software for nationwide moss monitoring 2005 . In: Umweltbundesamt (ed.), Umweltinformationssysteme – Anwendungsbeispiele für den Themenschwerpunkt Wasser. Workshop of the Working Group ‘Environmental Databases’ of the Section ‘Informatics in Environmental Protection’. 21–22 May 2007. Hamburg: UBA-Texte 02/08, published by the Federal Environment Agency. - Kleppin, L, Aden, C, Schmidt, G and Schröder, W. 2008b.
Monitoring of genetically modified maize cultivation by means of WebGIS and Google Maps . In: Car, A, Griesebner, G and Strobl, J (Hrsg.), Geospatial Crossroads @ GI_Forum ‘08: Proceedings of the Geoinformatics Forum Salzburg, 170–179. Heidelberg: Wichmann. - Kleppin, L, Pesch, R, Schmidt, G and Schröder, W. 2007b.
The WebGIS MossMet . In: Wichmann, H-E, Schlipköter, H-W and Füllgraf, G (eds.), Handbuch der Umweltmedizin, 39–41. Landsberg am Lech, Munich, Zurich, Chapter IV-7.1, 37 Erg.Lfg. - Kleppin, L, Schröder, W, Pesch, R and Schmidt, G. 2007c.
Development and application of the WebGIS ‘MossMet’ . In: Probst, F and Keßler, C (eds.), GI-Days-Young researchers forum. Proceedings of the 5th Geographic Information Days, 10–12, 217–227. September, Münster, S. - Kleppin, L, Schröder, W, Pesch, R and Schmidt, G. 2008c. Development and testing of a metadata and WebGIS application for exposure monitoring with mosses in Germany. A contribution to the LTER network. Environmental Sciences and Pollution Research, 20: 38–48. DOI: 10.1065/uwsf2007.10.223
- Kresse, W and Fadaie, K. 2004. ISO Standards for Geographic Information. Heidelberg, New York, Tokyo: Springer.
- Kühn, I. 2007. Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distribution, 13: 66–69.
https://www.ufz.de/export/data/2/92498_Kuehn_spatial_autocorrelation.pdf . - Legendre, P. 1993. Spatial autocorrelation: Trouble or new paradigm? Ecology, 74: 1659–1673.
http://www.jstor.org/stable/1939924 . DOI: 10.2307/1939924 - Legendre, P, Dale, MT, Fortin, MJ, Gurevitch, J, Hohn, M and Myers, D. 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25: 601–615. DOI: 10.1034/j.1600-0587.2002.250508.x
- Legendre, P, Date, MTR, Fortin, MJ, Casgrain, P and Gurevitch, J. 2004. Effects of spatial structures on the results of field experiments. Ecology, 85: 3202–3214. DOI: 10.1890/03-0677
- Legendre, P and Fortin, MJ. 1989. Spatial pattern and ecological analysis. Vegetation, 80: 107–138. DOI: 10.1007/BF00048036
- Liebhold, AM and Sharov, AA. 1998.
Testing for correlation in the presence of spatial autocorrelation in insect count data . In: Baumgartner, J, Brandmayr, P and Manly, BFJ (eds.), Population and community ecology for insect management and conservation, 11–18. Rotterdam: Balkema. - Lünsmann, I, Pesch, R, Schmidt G and Schröder, W. 2004.
Integration of environmental monitoring data with metadata, multivariate statistical natural space classification, geostatistics and GIS . ‘In environmental databases. Environmental Information Act and Environmental Databases. Workshop of the working group ‘Umweltdatenbanken’ of the section ‘Informatik in Umweltschutz’, 83–96. 17–18 May 2004, UBA-Texte 09/05. Berlin: Umweltbundesamt, Darmstadt. - MacNeil, MA. 2008. Making empirical progress in observational ecology. Environmental Conservation, 35: 193–196. DOI: 10.1017/S0376892908004888
- Matejka, H, Busch, W, Gorczyk, J, Mauersberger, F, Nickel, S, Riekeberg, T and Vosen, P. 2005. Metadata concepts to support GIS processing in monitoring mining environmental impacts. In: Seyfert, E (ed.), 25th Scientific-Technical Annual Conference of the DGPF, 65–74. Rostock,
21–23 September 2005 . - Meyer, M. 2017.
Site-specifically differentiated recording of atmospheric nitrogen and heavy metal inputs by means of mosses with consideration of the eaves effect and supplementary investigations on the relationship between nitrogen inputs and accompanying vegetation . Thesis (PhD), University of Vechta: 1–262 + 86 S. Anh. DOI: 10.23660/voado-16 - Neuschäfer, M. 2015. Handbook Digital Humanities. Public Beta.
http://dhd-blog.org/?p=5566 [Last accessed 12 February 2018]. - Nickel, S and Schröder, W. 2017a. Restructuring of the German moss monitoring network 2015, link to research data and scientific software (version v1) [Data set]. ZENODO. DOI: 10.5281/zenodo.1320814 [Last accessed 19 February 2019].
- Nickel, S and Schröder, W. 2017b. Heavy metal inputs into forest areas of Germany determined with modelling and moss monitoring, link to research data and scientific software (version v1) [Data set]. ZENODO. DOI: 10.5281/zenodo.1320780 [Last accessed 19 February 2019].
- Nickel, S and Schröder, W. 2018. GIS-integrated fuzzy-rule based model for calculating ecological soil moisture according to Hofmann. Python-Tool for ArcGIS 10.2 (Version v1). DOI: 10.5281/zenodo.1184252 [Last accessed 19 February 2019].
- Nickel, S, Schröder, W and Jenssen, M. 2015. Changes in German forests due to climate change and nitrogen deposition. Swiss Forest Review, 166(5): 325–334. DOI: 10.3188/szf.2015.0325
- Nickel, S, Schröder, W and Jenssen, M. 2019. Semi-natural forest ecosystem types of Germany and the Kellerwald National Park (Hesse, Germany), link to shape files. PANGAEA.
https://doi.pangaea.de/10.1594/PANGAEA.887305 [Last accessed 19 February 2019]. DOI: 10.1007/s13595-019-0849-4 - Nickel, S, Schröder, W, Jenssen, M and Riediger, J. 2017.
Integrity of Forest Ecosystems under the Influence of Climate Change and Atmospheric Substance Inputs . In: Schröder, W, Fränzle, O and Müller, F (eds.), Handbuch der Umweltwissenschaften, 1–26. Fundamentals and applications of ecosystem research. Chap. V-1.6. 24. Erg.Lfg., Weinheim: Wiley-VCH. DOI: 10.1002/9783527678525.hbuw2014001 - Peres-Neto, PR and Legendre, P. 2010. Estimating and controlling for spatial structure in the study of ecological communities. Global Ecology and Biogeography, 19: 174–184. DOI: 10.1111/j.1466-8238.2009.00506.x
- Perry, JN, Liebhold, AM, Rosenberg, MS, Dungan, J, Miriti, M, Jakomulska, A and Citron-Pousty, S. 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography, 25: 578–600. DOI: 10.1034/j.1600-0587.2002.250507.x
- Pesch, R, Schmidt, G, Schröder, W, Aden, C, Kleppin, L and Holy, M. 2007.
Development, implementation and application of the WebGIS MossMet . In: Tochtermann, K and Scharl, A (Hrsg.), The Geospatial Web. How geo-browsers, social software and the Web 2.0 are shaping the network society, 191–200. London: Springer. DOI: 10.1007/978-1-84628-827-2_18 - RFI (Rat für Informationsinfrastrukturen). 2016. Leistung aus Vielfalt – Empfehlungen zu Strukturen, Prozessen und Finanzierung des Forschungsdatenmanagements in Deutschland. Göttingen.
http://www.rfii.de/?wpdmdl=1998 [Last accessed 12 February 2018]. - Reuter, H, Middelhoff, U, Verhoeven, R, Breckling, B, Filser, J, Batz, T, Bonn, G, Schmidt, G, Schröder, W, Weis, M, Pappenberger, R, Kreft, I, Rösing J and Winter, G. 2007. ISMO – Information System for the Monitoring of Genetically Modified Organisms. BfN-Scripts 215, published by the Federal Agency for Nature Conservation, 1–304 + Appendix, Bonn. DOI: 10.1007/s00003-006-0094-2
- Schmidt, G, Aden, C, Kleppin, L, Pesch, R and Schröder, W. 2010.
Integration of long-term environmental data by the example of the Unece Heavy Metals in Mosses Survey in Germany: Application of a WebGIS-based metadata system . In: Müller, F, Klotz, S and Schubert, H (Hrsg.), Long-Term Ecological Research – Between Theory and Application (Part 5), 299–313. Berlin: Springer. DOI: 10.1007/978-90-481-8782-9_21 - Schmidt, G and Loesewitz, L. 2005. A WebGIS data retrieval system for use in environmental monitoring.” In: EnviroInfo Brno 2005. In: Hrebicek, J and Racek, J (Hrsg.) Proceedings of the 19th international conference ‘Informatics for Environmental Protection, 161–166. Networking environmental information’. part 1. Brno, Czech Republic,
September 7–9, 2005 . - Schröder, B and Reineking, B. 2004. Modelling the species-habitat relationship – an overview of habitat modelling techniques. In: Dormann, CF, Blaschke, T, Lausch, A, Schröder, B and Söndgerath, D (eds.), Habitatmodelle – Methodology, Application, Benefits, 5–25. Proceedings of the workshop from October 8–10. 2003 at the UFZ Leipzig. UFZ reports 9/2004.
- Schröder, W. 1994.
Knowledge Acquisition, Hypothesis Formation and Statistics . In: Schröder, W, Vetter, L and Fränzle, O (eds.), Neuere Statistische Verfahren und Modellbildung in der Geoökologie S, 1–16. Braunschweig: Wiesbaden. DOI: 10.1007/978-3-322-83735-6_1 - Schröder, W, Garbe-Schönberg, CD and Fränzle, O. 1991. The validity of environmental data – criteria for their reliability: representativeness, quality assurance and control. Environmental sciences and pollution research, 3: 237–241. DOI: 10.1007/BF02936814
- Schröder, W, Nickel, S, Jenssen, M and Riediger, J. 2015. Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: a pilot study in Germany. Science of the Total Environment, 521–522: 108–122. DOI: 10.1016/j.scitotenv.2015.03.048
- Schröder, W and Pesch, R. 2013.
Hypothesis testing in landscape ecology . In: Schröder, W, Fränzle, O and Müller, F (eds.), Handbuch der Umweltwissenschaften. Fundamentals and applications of ecosystem research, 1–10. Section V-1.1. 22. Erg.Lfg., Wiley-VCH, Weinheim. - Schröder, W, Pesch, R, Harmens, H, Fagerli, H and Ilyin, I. 2012. Does spatial auto-correlation call for a revision of latest heavy metal and nitrogen deposition maps? Environmental Sciences Europe, 20: 1–15. DOI: 10.1186/2190-4715-24-20
- Schröder, W, Pesch, R and Schmidt, G. 2004. Soil monitoring in Germany. Spatial representativity and methodical comparability.” Journal of Soils and Sediments, 4(1): 49–58. DOI: 10.1007/BF02990829
- Schröder, W, Pesch, R and Schmidt, G. 2006a. Identifying and closing gaps in environmental monitoring by means of metadata, ecoregionalisation and geostatistics. The UNESCO biosphere reserve Rhön (Germany) as an example. Environmental Monitoring and Assessment, 114: 461–488. DOI: 10.1007/s10661-006-4934-4
- Schröder, W and Schmidt, G. 2003.
Cross-media environmental monitoring in Baden-Württemberg. Results of a model project . In: Landesanstalt für Umweltschutz (ed.), Cross-media environmental monitoring, 39–60. Status and perspectives. Karlsruhe. - Schröder, W and Schmidt, G. 2005.
Metadatabases and GIS as technical support for material exposure and impact analysis in environmental monitoring . In: Fränzle, S, Markert, B and Wünschmann, S (eds.), Lehrbuch der Technischen Umweltchemie, 11: 211–227. Landsberg (ecomed), Chap. 4. - Schröder, W, Schmidt, G and Pesch, R. 2003a. Representation and comparability of data and areas of long-term soil monitoring. Journal of Plant Nutrition and Soil Science, 166: 649–659. DOI: 10.1002/jpln.200321162
- Schröder, W, Schmidt, G and Pesch, R. 2003b. Harmonization of environmental monitoring. Tools for examination of methodical comparability and spatial representativity. Gate to Environmental and Health Sciences, July 2003, 1–13. DOI: 10.1065/ehs2003.07.010
- Schröder, W, Schmidt, G and Pesch, R. 2005. Gaps in environmental monitoring? Analysis of measurement programmes and transfer of data on immission, deposition and metal accumulation in mosses from measurement networks in Bavaria, Hesse and Thuringia to the Rhön biosphere reserve. Journal for Applied Environmental Research, 15/16(1): 53–77.
- Schröder, W, Schmidt, G, Pesch, R and Eckstein, T. 2002.
Harmonisation of environmental monitoring. Instruments for testing methodological comparability and spatial representation . In: Fränzle, O, Müller, F and Schröder, W (eds.), Manual on environmental sciences. Fundamentals and applications of ecosystem research, 1–22. Landsberg am Lech, Chap. V-1.3, 8th Eq. Lfg. - Schröder, W, Weis, M and Schmidt, G. 2006b. Area-related aspects of the GMO monitoring information system. Graphical conception, development and testing of spatial documentation and analysis methods. Final report of subproject 3 of the joint project ‘Informationssystem for the monitoring of genetically modified organisms (Ismo)’. FKZ 804 67 030 in the Environmental Research Plan of the Federal Minister for the Environment, Nature Conservation and Nuclear Safety. On behalf of the Federal Agency for Nature Conservation, Bonn.
- Stachowiak, H. 1973. General model theory. Vienna: Springer.
- Stevens, SS. 1946. On the theory of scales of measurement. Science, 103: 677–680. DOI: 10.1126/science.103.2684.677
- Troll, C. 1939. Aerial photo plan and ecological soil research. Journal of the Gesellschaft für Erdkunde zu Berlin, 7(8): 241–298.
- USEPA (United States Environmental Protection Agency). 2002. Guidance on choosing a sampling design for environmental data collection for use in developing a quality assurance project plan EPA (QA/G-5S). EPA/240/R-02/005. Office of Environmental Information, Washington, DC 20460, S. 1–166.
https://www.epa.gov/sites/production/files/2015-06/documents/g5s-final.pdf [Last accessed 12 February 2018]. - Uzhinskiy, A, Ososkov, G and Frontasyeva, M. 2017. Data management of the Envorinment. Open System Publications, 4: 42–43. (
https://www.osp.ru/ )https://www.osp.ru/os/2017/04/13053390/ [Last accessed 12 February 2018] - Valcu, M and Kempenaers, B. 2010. Spatial autocorrelation: An overlooked concept in behavioral ecology. Behavioral Ecology. DOI: 10.1093/beheco/arq107
- Vetter, L, Schröder, W and Fränzle, O. 1986.
Scientific theoretical aspects of hypothesis acquisition and operationalization in geography . In: Fränzle, O (ed.), Geoecological environmental assessment. Scientific theoretical and methodological contributions to analysis and planning, 64: 1–17. Kieler Geographische Schriften. - Wagner, HH and Fortin, M-J. 2005. Spatial analysis of landscapes: Concepts and statistics. Ecology, 86: 1975–1987. DOI: 10.1890/04-0914
- Wheeler, J, Servilla, M and Vanderbilt, K. 2017.
Beyond discovery: Cross-platform application of ecological metadata language in support of quality assurance and control . In: Johnston, L (ed.), Curating Research Data, 184–87. Chicago, Illinois: Association of College and Research Libraries. - Wissenschaftsrat. 2011. Recommendations on research infrastructures in the humanities and social sciences. Drs. 10465-11, Berlin.
https://www.wissenschaftsrat.de/download/archiv/10465-11.pdf [Last accessed 12 February 2018].