Forest Landscapes and Global Change

Forest Landscapes and Global Change New Frontiers in Management, Conservation and Restoration

The contributions to this volume have not been peer-reviewed. Only minor changes in format may have been carried out by the editors. Title: Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. Editors: João Carlos Azevedo, Manuel Feliciano, José Castro & Maria Alice Pinto Published by: Instituto Politécnico de Bragança Apartado 1038, 5301-854 Bragança, Portugal http://www.ipb.pt Printed by: Serviços de Imagem do Instituto Politécnico de Bragança ISBN: 978-972-745-110-4 Cover design: Atilano Suarez, Serviços de Imagem do Instituto Politécnico de Bragança

Forest Landscapes and Global Change New Frontiers in Management, Conservation and Restoration

Proceedings of the IUFRO Landscape Ecology Working Group International Conference September 21-27, 2010 Bragança, Portugal

Edited by João Carlos Azevedo Manuel Feliciano José Castro Maria Alice Pinto

Instituto Politécnico de Bragança, Portugal September, 2010

R.A. Diaz-Varela et al. 2010. Extent and characteristics of mire habitats in Galicia (NW Iberian Peninsula)

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Extent and characteristics of mire habitats in Galicia (NW Iberian Peninsula): implications for their conservation and management Ramón Alberto Diaz-Varela, Pablo Ramil-Rego, Manuel Antonio Rodríguez-Guitián & Carmen Cillero-Castro 1

Universidade de Santiago de Compostela, Spain

Abstract NW Iberian Peninsula hosts a variety of mire habitats linked to wet environments that contrast with surrounding regional vegetation. These wetlands are confined to locations meeting certain azonal conditions, due to local topographies that prevent or delay drainage, to the occurrence of significant rainfall, or to a combination of both. Despite of their international relevance and their designation as habitats of interest for biodiversity conservation by the EU habitats Directive, they are threatened mainly by changes in land use, particularly by agriculture intensification/abandonment and the development of wind farms. In the present work we address the mapping, description and analysis of these habitats in a spatial explicit way in the region of Galicia (NW Iberian Peninsula), in order to support the development of strategies of planning and management. Results showed differences in the extent, distribution and characteristics of the different types of mire habitats with important implications in their conservation. Keywords: Mire habitats; NW Iberian Peninsula; spatial distribution; habitat environmental controls; CHAID

1. Introduction Mire habitats are azonal wetland ecosystems confined to areas with particular environmental conditions defined by a positive water balance, a low organic mater decomposition rate and where the vegetation remains composition has the potential to form peat (Goodwillie 1980; Raeymaekers 2000; Rydin and Jeglum 2008). Their conservation value has been recognized internationally by different institutions and treaties, as the Ramsar Convention. In the EU context, most of mire types were included as interest of priority habitats in the Annex II of the European Directive 92/43/CEE for their inclusion in the European network of protected areas Natura 2000 (European Commission, 2003). They also host a great amount of species of bacteriae, protozoans, fungi, algae, lichens, bryophytes, vascular plants, invertebrates and vertebrates of interest for biodiversity conservation. Most of them have they optimal or even exclusive habitats in these environments (Rydin and Jeglum 2008). In relation with plant communities, mire habitats frequently include endemic taxa and usually show particular floristic compositions along geographical and altitudinal gradients because of their azonal and fragmented distribution. A key issue in the planning and conservation of these habitats is the assessment and modelling of their spatial distribution in relation with key environmental factors as this information might be used for the optimization of conservation efforts (Wainwright and Mulligan 2004). There are a number of factors that determine the occurrence and the type of mire habitats, being the most important climate, topography and nutrient supply (Graniero and Price 1999). In this work we aimed at the exploration of the relationship and dependence between different environmental controls and the occurrence of different mire habitats in a sector of the NW of Spain. More Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.) 2010, Instituto Politécnico de Bragança, Bragança, Portugal.


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specifically, we carried out a data mining procedure to determinate the influence of topography, climate, lithology and distance to sea in the occurrence of four types of mire habitats.

2. Methodology We conducted our analyses in the region of Galicia, located in the NW of Spain (code NUTS ES11 of the European Union) and covering an area of 29 574 Km2 (cf. fig. 1). Altitudes range from sea level to about 2000 m a.s.l. showing a contrasted relief. Biogeographically most of the area is included in the Atlantic Region, but a sector of the SE quadrant of the scene corresponds with Mediterranean region, being a matter of discussion the exact extent of the Mediterranean domain in the area (European Environment Agency 2008, Rodríguez Guitián and Ramil Rego 2008; Rivas-Martinez and Rivas-Saenz 2009). Natural vegetation comprises different deciduous forest, mostly dominated by Quercus robur, but most of the present day land cover shows an important degree of human intervention, being frequent afforestations with non native species, scrubs and heatlands along with mosaics of traditional and modern agrarian landscapes. Mire habitats maps were done using different data sources, from national habitat inventories (http://www.mma.es/portal/secciones/biodiversidad/banco_datos/info_disponible/index_atlas_m anual_habitats.htm) and wetland catalogs (Galician Wetland Inventory, http://medioambiente.xunta.es/espazosNaturais/humidais/index.htm) to monographic works (Izco Sevillano et al. 2001) and regional maps for protected areas planning (Ramil-Rego and Crecente Maseda 2005). All this information was processed in a Geographic Information System software (ArcGisTM V9.2) and converted to different raster layers (one for mire type) with a cell size of 90 m. Mire classification legend was based on the typology of the Annex I of the European Habitat Directive (consolided version of the 92/43/CEE Directive) and included four types of mire habitats: Blanket bogs, Cladium mariscus fens, Calcareous fens and Tufa formations, and was a compromise between the available input information and the categories of the Directive. Other mire habitats of the Annex I of the directive (i.e. Raised bogs, Depressions on peat substrates of the Rhynchosporion and Transition mires and quaking bogs) were not considered in this analysis as are often included in mosaics of other habitat complexes (frequently wet heathland or moorland) in the existing cartography. We considered four groups of explanatory variables (climate, topography, geology and distance to sea) as potential environmental controls for mire habitats (cf. table 1). Climate variables were obtained from regional climate maps available in literature (Martínez Cortizas and Pérez Alberti 1999), where the variables are expressed in intervals of different amplitude labelled as a numeric scale (cf. table 2). Topographic variables were computed from a Digital Elevation Model using the ArcGisTM V9.2 software package. Geology map were done by reclassifying the original classes of geological charts from the Spanish Geological Institute in five classes, namely sedimentary (sd), granitic (gr), ultrabasic (ub), siliceous metamorphic (mt) and calcareous (ca) materials. We also computed the minimum distance to sea as an indicator of the degree of continentality. All the information layers were converted to raster format with the same spatial reference and resolution as the mire habitat maps. We extracted the explanatory variables data by means of the spatial overlay of the masks corresponding with each mire type. The final output for the subsequent analyses was a table with mire type as dependent / grouping variable and several explanatory or independent variables. In order to asses the effect or explanatory power of these variables, we performed a classification using the tree based segmentation technique CHAID, acronym of Chi-Squared Automatic Interaction Detection (Biggs et al. 1991; Kass 1980). CHAID is an exploratory analysis based in the recursive partitioning of a feature space of several independent or potential predictors, that themselves might interact, in relation to a dependent or response variable. Both predictors and response variables may be continuous, ordinal or categorical. Since CHAID is a non-parametric technique, no normalization of original variables is needed (Van Diepen and Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.) 2010, Instituto Politécnico de Bragança, Bragança, Portugal.


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Franses 2006). CHAID technique was applied using mire classes as response variables against the abovementioned collection of potential predictors. For the validation of the model, we randomly split the original data in a training and validation with 50 % of the cases assigned to each subset. We finally generated a contingency matrix for the contrast of the observed and predicted classes for the validation set, and subsequently we computed the KAPPA statistic (Bishop et al. 1975) as a measure of model accuracy.

3. Results and Discussion Results of the CHAID classification are presented in figure 2. According the diagram, the most powerful discriminant variable was distance to sea. In the second level nodes other variables related to geology, topography and climate were taking into account for the discrimination of mire types. Finally, water balance was considered for the discrimination in the third level between some blanket bogs and Cladium fens with similar values regarding their distance to sea and slope. Overall accuracy reached more than 96 % (table 3) while the estimation of KAPPA index, regarded as a more reliable indication of the overall accuracy due to its compensation of chance agreement (Congalton 1991), achieved a value of 0.93, indicating an almost perfect agreement between the classification and reference values (Landis and Koch 1977). Per class accuracies reached particularly high values for blanket bogs and tufa, while fen and Cladium mires accuracies were lowered because the confusions with each other and also with tufa mires. According these results, blanket bogs occur on sub-coastal areas at different exposures, more frequently facing north and under high annual rainfall or water balance, as previously stated by biogeographic studies of this habitat in NW Iberian Peninsula (Rodríguez Guitián et al. 2007). Cladium fens are located close to the coastline, on sedimentary deposits (corresponding in most cases with the inland border of coastal salt marshes). They also occur in the inland, on flat surfaces under not particularly high water balances, where some confusion with tufa or fens could happen. Even when some fen localities may occur in the inland, they tend to appear close to the coast, on ultra basic material sharing these environmental conditions with localities of Cladium fens and tufa formations. Finally, tufa occurs on the coast, in springs or water table ruptures leaching fossil/raised coastal dunes systems still rich in carbonates on coastal cliffs, or alternatively in the inland, linked to the few ditches of limestone rocks in the region (Ramil Rego et al. 2008).

4. Conclusions In the present work we explored the role of different environmental controls on the occurrence of four types of mire habitats. We found out that the degree of continentality (using the reliefcorrected distance to sea as an indicator) play a potential key role in the differences in spatial distribution of types in the region of Galicia. However mire types occurrence can not be differentiated or explained on the basis of just one kind of environmental control, but rather a combination of different controls (as geology, distance to sea, climate or lithology), being the importance of each one related to the ecology, tolerance and requirements of each particular habitat. The results corroborate in a quantitative and spatially explicit way the previous knowledge on the ecological differences between the different mire habitats in the region. This information has a potential application in habitat management plans for protected areas in combination to other datasets (e.g. spatial pattern and distribution, vulnerability and resilience, future climatic and land use scenarios) and also constitutes a first step towards a predictive biogeographic modelling of habitat distribution.

Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.) 2010, Instituto Politécnico de Bragança, Bragança, Portugal.


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References Biggs, D.; Deville, B. and Suen, E., 1991. A Method of Choosing Multiway Partitions for Classification and Decision Trees. Journal of Applied Statistics, 18: 49-62. Bishop, Y. M. M.; Fienberg, S. E. and Holland, P. W., 1975. Discrete multivariate analysis : theory and practice. MIT Press, Cambridge. Massachusetts. Congalton, R. G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35-46. European Commission 2003. Interpretation Manual of European Union Habitats - EUR 25. European Commission. DG Environment. Nature and Biodiversity. Brussels. 129 pp. European Environment Agency, 2008. Biogeographical regions in Europe. http://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe (Access 03/01/2010) Goodwillie, R., 1980. Les Tourbières en Europe. Comite Europeen Pour la Sauvegarde de la Nature et des Ressources Naturelles. Conseil de L´Europe. Collection Sauvegarde de la Nature Nº 19. Strasbourg. 82 pp. Graniero, P. A. and Price, J. S., 1999. The importance of topographic factors on the distribution of bog and heath in a Newfoundland blanket bog complex. CATENA, 36: 233-254. Izco Sevillano, J.; Díaz Varela, R. A.; Martínez Sánchez, S.; Rodríguez Guitián, M. A.; Ramil Rego, P. and Pardo Gamundi, I. M., 2001. Análisis y Valoración de la Sierra de O Xistral: un Modelo de Aplicación de la Directiva Hábitat en Galicia. Xunta de Galicia. Santiago de Compostela. 161 pp. Kass, G. V., 1980. An Exploratory Technique for Investigating Large Quantities of Categorical Data. Journal of Applied Statistics, 29: 119-127. Landis, J. R. and Koch, G. G., 1977. The measurement of observer agreement for categorical data. Biometrics, 33: 159-174. Martínez Cortizas, A. and Pérez Alberti, A. (Dir.), 1999. Atlas climático de Galicia. Xunta de Galicia. Santiago de Compostela. 207 pp. Raeymaekers, G., 2000. Conserving mires in the European Union. Actions Co-Financed by LIFE-Nature. European Commission. DG XI. 90 pp. Ramil-Rego, P. and Crecente Maseda, R. (Dir.), 2005. Planes de conservación de las ZEPVN de Galicia. Xunta de Galicia. Consellería de Medio Ambiente. Dirección Xeral de Conservación da Natureza. Santiago de Compostela. Ramil-Rego et al., 2008. Os hábitats de Interesse Comunitario en Galicia. Descrición e Valoración Territorial. Monografías do IBADER. Universidade de Santiago de Compostela. Lugo. Spain. 263 pp. Rivas-Martinez, S. and Rivas-Saenz, S., 2009. Worldwide Bioclimatic Classification System, 1996-2009. Phytosociological Research Center, Spain. http://www.globalbioclimatics.org. Rodríguez Guitián, M.A; Ramil-Rego, P.; Real, C.; Díaz Varela, R.; Ferreiro da Costa, J. and Cillero, C., 2009. Caracterización vegetacional de los complejos de turberas de cobertor activas del SW europeo. En: F. Llamas & C. Acedo (Coords.): Botánica PirenaicoCantábrica en el siglo XXI. Área de Publicaciones. Universidad de León. León: 633-653 Rydin, H. and Jeglum, J., 2008. The biology of peatlands. Oxford University Press. New York. 343 pp. Rodríguez Guitián, M.A. and Ramil-Rego, P. 2008. Fitogeografía de Galicia NW Ibérico. análisis histórico y nueva propuesta corológica. Recursos Rurais, 14: 19-50. Van Diepen, M. and Franses, P.H., 2006. Evaluating chi-squared automatic interaction detection. Information Systems, 31: 814-831. Wainwright, J. and Mulligan, M. (Eds.), 2004. Environmental Modelling. Finding Simplicity in Complexity. John Willey and Sons, Ltd. West Sussex, England. 408 pp.



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Table 1: Explanatory variables Variable group Climate

Topography

Geology Distance to sea

Variable Annual average temperature Annual total rainfall Annual total evapotranspiration Annual water balance Transformed aspect Curvature Plan curvature (across slope) Profile curvature (along slope) Slope Terrain shape index Wetness index Elevation Geologic material Distance to sea

Acronym Temp Rain ETP Waterbal TRASP Curv Plancurv Profcurv Slope Tershp Wenidn DEM MAT Distosea

Type Ordinal Ordinal Ordinal Ordinal Scale Scale Scale Scale Scale Scale Scale Scale Nominal Scale

Units Adimensional (intervals) Adimensional (intervals) Adimensional (intervals) Adimensional (intervals) Adimensional Adimensional Adimensional Adimensional degrees Adimensional Adimensional m a.s.l. Adimensiona m

Table 2: Intervals for the climate variables Average year temperature Total year rainfall Code Intervals (ºC) Code Intervals (mm) 1 15 9 > 2000

Total year Evapotranspiration Code Intervals (mm) 1 800 -

Total year water balance Code Intervals (mm) 1 -250/-200 2 -100/-50 3 0-200 4 200-400 5 400-600 6 600-800 7 800-1000 8 1000-1500 -

Table 3. Accuracy of the CHAID classification

Observed blanket cladium fen tuf Percent correct

Blanket Cladium 1767 0 13 122 11 11 1 14 60,4% 5,0%

Predicted Fen Tuf Percent correct 0 1 99,9% 4 40 68,2% 49 0 69,0% 10 924 97,4% 2,1% 32,5% 96,5%



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Figure 1: Study area

Figure 2: CHAID results Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.) 2010, Instituto Politécnico de Bragança, Bragança, Portugal.