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2008 | 10 |

Tytuł artykułu

Constructions of logical expressions in analysis of vegetation transformations

Autorzy

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The paper reports on the effects and range of anthropogenic pressure exerted on forest communities of the Knyszyńska Forest. A comparison between the potential natural vegetation and real vegetation gives an idea on the degree of damage to forest communities, which has been classified in ecological modelling. Logical expressions have been applied in ecological modelling for spatial analyses of vegetation changes carried out by the program ArcView GIS. The logical expressions applied to the GIS spatial database have permitted finding correlations of occurrence of particular types of the present-day real vegetation (in particular the post-clear-cutting communities, young tree communities and secondary forest communities) relative to the present-day potential natural vegetation. The data obtained in this way have been used in analysis of the scale and range of changes in the forest communities of the Knyszyńska Forest caused by forest management measures.Results of the study have shown that in the Knyszyńska Forest, the secondary communities occupy as much as 88.3% (919.56 km2), while the oldest tree-stands representing natural communities (of 100 - 120 years of age) occupy only 11.7% (122.28 km2). Among the secondary communities the greatest area is occupied by the secondary forest communities representing the stickstand and oldgrowth phases (66.9%) aged from 30 to about 100 years. The contribution of young tree stands - aged from 10 to 30 years is smaller - 16.6%, and that of post-clear-cutting and forest crops forming directly after clear cutting and aged up to 10 years is still smaller - of 4.8%. In the young-tree stands (16.6%) and forest secondary communities (66.9%) the largest is the contribution of those with domination of pine trees (Pinus sylvestris) from artificial reforestation, making 11.2 and 55%, respectively, while the contribution of other secondary communities is much lower.

Wydawca

-

Rocznik

Tom

10

Opis fizyczny

p.75-90,fig.,ref.

Twórcy

autor
  • Department of Environmental Protection and Management, Bialystok Technical University, Wiejska 45a, 15-351 Bialystok, Poland

Bibliografia

  • Aalders H. J. G. L., 2001, GIS in practice, [in:] A. Nienartowicz & M. Kunz (eds.), GIS and remote sensing in studies of landscape structure and functioning, Wydawnictwo UMK, Toruń: 9 - 25.
  • ArcView GIS, 1997, ArcView GIS. Geographical Information Systems, ESRI Polska, Warszawa: 373.
  • Berec L., 2002, Techniques of spatially explicit individual-based models: construction, simulation, and mean-field analysis, Ecological Modelling 150: 55 - 81.
  • Corne S. A., Carver S. J., Kunin W. E., Lennon J. J. & van Hees W. W. S., 2004, Predicting forest attributes in southeast Alaska using artificial neural networks, Forest Science 50: 259 - 276.
  • Czerwiński A., 1995, Geobotanika w ochronie środowiska lasów Podlasia i Mazur [Geobotany in protection of forests environment of Podlasie and Mazury], Wydawnictwa Politechniki Białostockiej, Białystok: 345.
  • Dambacher J. M., Li H. W. & Rossignol P. A., 2003a, Qualitative predictions in model ecosystems, Ecological Modelling 161: 79 - 93.
  • Dambacher J. M., Luh H. K., Li H. W. & Rossignol P. A., 2003b, Qualitative stability and ambiguity in model ecosystems, The American Naturalist 161: 876 - 888.
  • Drouet J. L. & Pagès L., 2003, GRAAL: a model of GRowth, Architecture and carbon ALlocation during the vegetative phase of the whole maize plant. Model description and parameterisation, Ecological Modelling 165: 147 - 173.
  • Drouet J. L. & Pagès L., 2007, GRAAL-CN: A model of GRowth, Architecture and ALlocation for Carbon and Nitrogen dynamics within whole plants formalised at the organ level, Ecological Modelling 206: 231 - 249.
  • Edgar C. C. B. & Burk T. E., 2007, Demonstration and verification of a model that generates defoliation patterns in forested landscapes, Ecological Modelling 205: 301 - 313.
  • Esri 1993, Arc Macro Language devolping Arc/Info menus and macros with AML, ESRI, Redlans.
  • Fath B. D., 2004, Network analysis applied to large-scale cyber-ecosystems, Ecological Modelling 171: 329 - 337.
  • Fath B. D., Scharler U., Ulanowicz R. E. & Hannon B., 2007, Ecological network analysis: network construction, Ecological Modelling 208: 49 - 55.
  • Fuentes M., Kittel T. G. F. & Nychka D., 2006, Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing, Ecological Applications 16: 99 - 116.
  • Gevrey M., Worner S., Kasabov N., Pitt J. & Giraudel J. L., 2006, Estimating risk of events using SOM models: a case study on invasive species establishment, Ecological Modelling 197: 361 - 372.
  • Gimenez O., Rossi V., Choquet R., Dehais C., Doris. B., Varella H., Vila J. P. & Pradel R., 2007, State-space modelling of data on marked individuals, Ecological Modelling 206: 431 - 438.
  • Główny Urząd Statystyczny, Ochrona Środowiska [Central Statistical Office, Nature Protection], 1999, Warszawa: 510.
  • Gough M. C. & Rushton S. P., 2000, The application of GIS-modelling to mustelid landscape ecology, Mammal Review 30: 197 - 216.
  • Jager H. I. & King A. K., 2004, Spatial uncertainty and ecological models, Ecosystems 7: 841 - 847.
  • Jørgensen S. E. & Bendoricchio G., 2001, Fundamentals of Ecological Modeling, 3rd ed., Elsevier, Amsterdam: 530.
  • Kazanci C., 2007, EcoNet: A new software for ecological modeling, simulation and network analysis, Ecological Modelling 208: 3 - 8.
  • Kistowski M. & Iwańska M., 1997, Systemy Informacji Geograficznej. Podstawy techniczne i metodyczne [Geographical Information Systems. Technical and methodical basics], Bogucki Wydawnictwo Naukowe, Poznań: 189.
  • Laughlin D. C. & Grace J. B., 2006, A multivariate model of plant species richness in forested systems: old-growth montane forests with a long history of fire, Oikos 114: 60 - 70.
  • Laughlin D. C. & Abella S. R., 2007, Abiotic and biotic factors explain independent gradients of plant community composition in ponderosa pine forests, Ecological Modelling 205: 231 - 240.
  • Lek S., Scardi M., Verdonschot P., Descy J. P. & Park Y. S., 2005, Modelling Community Structure in Freshwater Ecosystems, Springer: 518.
  • Li H. W., Rossignol P. A. & Castillo G., 2000, Risk analysis of species introductions: insights from qualitative modeling, [in:] R. Claudi & J. H. Leach (eds.), Nonindigenous Fresh Water Organisms, Vectors, Biology, and Impacts, CRC Press, Boca Raton: 431 - 447.
  • Lischke H., Zimmermann N. E., Bolliger J., Rickebusch S. & Löffler T. J., 2006, TreeMig: a forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale, Ecological Modelling 199: 409 - 420.
  • Liu C., Zhang L., Davis C. J., Solomon D. S., Brann T. B. & Caldwell L. E., 2003, Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data, Forest Science 49: 619 - 631.
  • Lundquist J. E. & Sommerfeld R. A., 2002, Use of Fourier transforms to define landscape scales of analysis for disturbances: a case study of thinned and unthinned forest stands, Landscape Ecology 17: 445 - 454.
  • Łaska G., 1999a, Ocena stopnia i zasięgu zniekształceń zbiorowisk leśnych Puszczy Knyszyńskiej [Assessment of the degree and scale of forest community disturbances in the Knyszynska Forest], mskr., Białystok: 178.
  • Łaska G., 1999b, Dzisiejsza potencjalna roślinność naturalna Puszczy Knyszyńskiej - mapa cyfrowa w skali 1:50000 [Present-day potential natural vegetation - the numerical map in scale 1:500000].
  • Łaska G., 1999c, Dzisiejsza roślinność rzeczywista Puszczy Knyszyńskiej - mapa cyfrowa w skali 1:50000. [Present-day real vegetation - the numerical map in scale 1:500000].
  • Łaska G., 1999d, Antropogeniczne przeobrażenia roślinności Puszczy Knyszyńskiej - mapa cyfrowa w skali 1:50000 [Assessment of the degree and scale of anthropogenic disturbances of plant communities in the Knyszynska Forest - the numerical map in scale 1:500000].
  • Łaska G., 2006a, Plant communities in wetland habitats in the Knyszyńska Forest - present state and anthropogenic transformations in the GIS approach, Polish Journal of Enviromental Studies 15: 207 - 214.
  • Łaska G., 2006b, Tendencje dynamiczne zbiorowisk zastępczych w Puszczy Knyszyńskiej [Dynamic tendencies of the secondary communities in the Knyszyńska Forest], Bogucki Wydawnictwo Naukowe, Białystok - Poznań: 500.
  • Łaska G. & Hildebrand R., 2001, Geographical Information Systems (GIS) in analysis of vegetation changes in the Knyszyńska Forest, [in:] A. Nienartowicz & M. Kunz (eds.), GIS and remote sensing in studies of landscape structure and functioning, Wydawnictwo UMK, Toruń: 97 - 116.
  • Matuszkiewicz W., 2001, Przewodnik do oznaczania zbiorowisk roślinnych Polski [A guide to determine plant communities of Poland], PWN, Warszawa: 537.
  • McNeil B. E., Martell R. E. & Read J. M., 2006, GIS and biogeochemical models for examining the legacy of forest disturbance in the Adirondack Park, NY, USA, Ecological Modelling 195: 281 - 295.
  • Nienartowicz A. & Kunz M., 2001, GIS and remote sensing in studies of landscape structure and functioning, Wydawnictwo UMK, Toruń: 303.
  • Olenderek H., Mozgawa J. & Korpetta D., 2001, Polskie leśnictwo w systemach informacji przestrzennej [Geographical Information Systems in Polish forestry], [in:] A. Nienartowicz & M. Kunz (eds.), GIS and remote sensing in studies of landscape structure and functioning, Wydawnictwo UMK, Toruń: 81 - 95.
  • Reed J. M. & Levine S. H., 2005, A model for behavioural regulation of metapopulation dynamics, Ecological Modelling 183: 411 - 423.
  • Reineking B., Veste M., Wissel C. & Huth A., 2006, Environmental variability and allocation trade-offs maintain species diversity in a process-based model of succulent plant communities, Ecological Modelling 199: 486 - 504.
  • Sakanoue S., 2007, Extended logistic model for growth of single-species populations, Ecological Modelling 205: 159 - 168.
  • Scrinzi G., Marzullo L. & Galvagni D., 2007, Development of a neural network model to update forest distribution data for managed alpine stands, Ecological Modelling 206: 331 - 346.
  • Sturtevant B. R., Gustafson E. J., Li W. & He H. S., 2004a, Modelling biological disturbance in LANDIS: a model description and demonstration using spruce budworm, Ecological Modelling 180: 153 - 174.
  • Sturtevant B. R., Gustafson E. J., Li W. & He H. S., 2004b, Modelling disturbance and succession in forest land-scapes using LANDIS: introduction, Ecological Modelling 180: 1 - 5.
  • Tews J., Esther A., Milton S. J. & Jeltsch F., 2006, Linking a population model with an ecosystem model: assessing the impact of land use and climate change on savanna shrub cover dynamics, Ecological Modelling 195: 219 - 228.
  • Tichit M., Doyen L., Lemel J. Y., Renault O. & Durant D., 2007, A co-viability model of grazing and bird community management in farmland, Ecological Modelling 206: 277 - 293.
  • Ulanowicz R. E., 2004, Quantitative methods for ecological network analysis, Computational Biology and Chemistry 28: 321 - 339.
  • Urbański J., 1997, Zrozumieć GIS. Analiza informacji przestrzennej [To understand GIS. Analysis of spatial information], Wydawnictwo Naukowe PWN, Warszawa: 144.
  • Van der Lee G. E. M., Van der Molen D. T., Van den Boogaard H. F. P. & Van der Klis H., 2006, Uncertainty analysis of a spatial habitat suitability model and implications for ecological management of water bodies, Landscape Ecology 21: 1019 - 1032.
  • Van der Zee D., 2001, GIS and landscape change analysis, [in:] A. Nienartowicz & M. Kunz (eds.), GIS and remote sensing in studies of landscape structure and functioning, Wydawnictwo UMK, Toruń: 27 - 38.
  • Wang G., 2007, On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models, Ecological Modelling 200: 521 - 528.
  • Wintle B. A., McCarthy M. A., Volinsky C. T. & Kavanagh R. P., 2003, The use of Bayesian model averaging to better represent uncertainty in ecological models.

Typ dokumentu

Bibliografia

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