Landscapes Forest and Global Change - ESA - Escola Superior ...

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E. Tetetla-Rangel et al. 2010. Relationships among landscape structure, climate and rare woody species richness 318 The aim of this study was to evaluate the association of rare woody species richness at different levels of rarity with landscape structure, climate and spatial dependence, to generate scientific knowledge necessary to promote the conservation and management of biodiversity of tropical forests. 2. Methodology 2.1 Study area The study area is located at the Yucatan peninsula in SE Mexico (17° 50´ to 21° 30´ north latitude and 87° 00´ to 91° 00´ east longitude), covering a total area of 141,523 km 2 (Figure 1). The peninsula is constituted almost entirely of limestone Karsts from the Eocene. Around 95% of the territory consists of flat lowlands < 100 m above sea level, but some hills reach 275 meters (Ferrusquía-Villafranca 1993). Seven types of tropical forests, in different stages of secession, cover the peninsula (Flores and Carvajal 1994; Durán et al. 1999) and their distribution patterns are determined by precipitation and soil type (Carnevali et al. 2003). . Mexico 200 0 200 400 km Rarewoody species records Landscapes25km² Main citys # # # # #· Campeche ## # # # # # # # ## # # # # # # # # # # # # # Merida # # ## # # #· # # # # # # # # # # ## ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # ## # # # # # # # # # # ## # # ## # # # # # # # ## # # # # ## # # # # # # Cancun # #· # # # # # # # # ## # # # # # ## # # # # # ## ## # ## N 30 0 30 60 km # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # ## # # # # # # # # # # # # ## # ## # # # ## # # # # # # # # # # # # Chetumal # #· ## # # # # # # # # # # # # # # # # # # # ## # # # # # # Figure 1: Study area and rare woody species records within 25 km 2 landscapes. 2.1.2 Rare woody species richness Using herbarium records (Centro de Investigación Científica de Yucatán CICY) we identified rare woody species, which were classified in three levels of rarity – low, medium and high– according of their frequency, habitat specificity and range of potential distribution (modelled in DOMAIN, Carpenter et al. 1993). To calculate rare woody species richness we generated a 25 km 2 grid (landscapes) covering the whole study area (Figure 1). 2.1.3 Environmental variables Three groups of environmental variables were considered as explanatory variables in the regression models in those landscapes containing at least one rare species registered: (1) climate, 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.
E. Tetetla-Rangel et al. 2010. Relationships among landscape structure, climate and rare woody species richness 319 (2) topography and (3) landscape configuration. Several digital climatic maps were interpolated using kriging and the software GS+ (Version 9) to extract information of the average, standard deviation and coefficient of variation of annual precipitation as well as mean, maximum and minimum annual temperature. We also extracted the altitude from a digital elevation model (DEM). We calculated the following landscape metrics: number of patches (NP), patch density (PD), largest patch index (LPI), edge density (ED), landscape shape index (LSI), total edge contrast index (TECI), patch richness (PR), Shannon’s diversity index (SHDI) and Simpson’s diversity index (SIDI) using FRAGSTATS 3.0 (McGarical et al. 2002). 2.1.4 Data analyses To determine the spatial structure of landscape locations and include this as a variable to predict rare woody species richness, we used principal coordinate of neighbor matrices (PCNM, Bocard et al. 2004). To partition the variability into environmental and space components, we used three sets of multiple linear regression models using different independent variables: (1) PCNM vectors, (2) environmental variables, and (3) all selected variables in (1) and ( 2). 3. Results To divide rare species into groups, we first selected those species representing the 25th percentile of frequency distribution of herbarium records to identify species having low frequency. We then assigned these species into 2 categories of habitat specificity according to information from the CICY and Missouri herbariums: high habitat specificity (species restricted to 1 or 2 vegetation types) and low specificity (species reported in > 2 vegetation types). Finally, we used the 25th percentile of the modelled area of potential distribution to assign species into narrow and broad classes (Table 1). Table 1: Criteria used to define rarity levels of woody species. Frequency Specificity Distribution Rarity level Low (< 5 records) Low (3 - 6 habitat types) Wide (> 9886.25 km²) Low Low (< 5 records) High (1 - 2 habitat types) Wide (> 9886.25 km²) Medium Low (< 5 records) Low (3 - 6 habitat types) Narrow (< 9886.25 km²) Medium Low (< 5 records) High (1 - 2 habitat types) Narrow (< 9886.25 km²) High We found 242 rare woody plant species (133 trees, 85 shrubs and 31 lianas) out of the 955 woody species registered in the herbarium for the Yucatan peninsula. Due to the lack of data to model the potential distribution of rare woody species, we only used 138 species (76 trees, 45 shrubs and 17 lianas) to generate the potential distribution maps of these rare species, since the remaining 104 species had only 1 herbarium sample. We found 18 species belonging to the low level of rarity, and 34 and 86 belonging to the medium and high levels, respectively. Total variation in rare species richness explained by environmental variables ranged from 3 to 16% (Table 2). The medium level of rarity had the highest percentage of variation explained and was associated with the highest number of environmental variables. Species richness was negatively associated with maximum temperature and positively related to precipitation. Relationships involving TECI and LSI were negative, indicating that rare species richness decreases as the contrast of patches increases and as the shape of a landscape becomes more irregular. On the other hand, the number of patches and edge density were positively related to rare species of this level. 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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Landscapes Forest and Global Change
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The contributions to this volume ha
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Table of contents Foreword Preface
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Fine-scale mapping of High Nature V
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Characterization of a Maculinea alc
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Urban tree inventory and socio-econ
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xiii Foreword Twenty years ago, Dr.
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Section 1 Scaling in landscape anal
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V. Cortes et al. 2010. Environmenta
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D. S. de Ron et al. 2010. Habitat s
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B. Bożętka 2010. Recent relations
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S.F. Chen et al. 2010. A physiotope
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V.D. de Campos & S M. Carvalho 2010
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N.S. Evseeva & Z.N. Kvasnikova 2010
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N.S. Evseeva & Z.N. Kvasnikova 2010
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S. Frank et al. 2010. A regionally
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M. García et al. 2010. The effect
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M. García et al. 2010. The effect
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S. Gholami et al. 2010. Spatial pat
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P. González-Moreno et al. 2010. Th
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T. Höbinger et al. 2010. Impact of
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P. Matos et al. 2010. Can lichen fu
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E.S. Meier et al. 2010. Projections
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E.S. Meier et al. 2010. Projections
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H. Moutahir et al. 2010. Applicatio
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H. Moutahir et al. 2010. Applicatio
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C. Palaghianu 2010. The use of Voro
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F. Peña-Cortés et al. 2010. Spati
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F.M. Rabenilalana et al. 2010. Mult
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E. Sayad 2010. Nitrogen retrancloca
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E. Sayad 2010. Nitrogen retrancloca
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G. Zhelezov 2010. Evaluation of the
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Section 3 Disturbances in changing
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A. Altamirano et al. 2010. Human-ca
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A. Altamirano et al. 2010. Human-ca
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T. Curt & J. Pausas 2010. Are chang
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R.A Fleming et al. 2010. Forest man
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R.A Fleming et al. 2010. Forest man
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P.C. Goebel et al. 2010. How import
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L.R. Iverson et al. 2010. Merger of
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T.-C. Lin et al. 2010. Immediate ef
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K. L. Martin & P.C. Goebel 2010. Im
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G.M. Pastur et al. 2010. Indirect e
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E.W Saragih et al. 2010. Effects of
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L.R.P. Williamson et al. 2010. Anth
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N. Zurbriggen et al. 2010. Modeling
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Section 4 Biodiversity conservation
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Akbarzadeh et al. 2010. Ecotourism
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Akbarzadeh et al. 2010. Ecotourism
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C. Carvalho-Santos 2010. Fine-scale
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I. Catalano et al. 2010. Wood macro
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R.A. Diaz-Varela et al. 2010. Exten
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R.A. Diaz-Varela et al. 2010. Asses
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P.A.E. Diogo & J. Aranha 2010. GIS
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P.A.E. Diogo & J. Aranha 2010. GIS
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V. Etemad & N. Avani 2010. Investig
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A.E. Eycott et al. 2010. The impact
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E. Fernández-Núñez et al. 2010.
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N. Fracassi & D. Somma 2010. Partic
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J. Hartter et al. 2010. Fortresses
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M.B. Horta et al. 2010. Landscape S
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Sara Marques et al. 2010. Impact of
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M.G.C. Martins & J. Aranha 2010. El
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J.-F. Mas et al. 2010. Modeling lan
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M.L. Leite & J.S.V. Filho2010. Anal
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M. Rehor & V. Ondracek 2010. Applic
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J. Russell et al. 2010. Developing
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Section 7 Management and sustainabi
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Section 9 Symposia
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Network theory to conserve and reco
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Management and conservation of Medi
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IUFRO Unit 8.01.02 Landscape Ecolog
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