The imprint of humans on landscape patterns and vegetation functioning in the dry subtropics
Dry subtropical regions (DST), originally hosting woodlands and savannas, are subject to contrasting human pressures and land uses and different degrees of water limitation. We quantified how this variable context influences landscape pattern and vegetation functioning, by exploring the associations...
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| Published in | Global change biology Vol. 19; no. 2; pp. 441 - 458 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.02.2013
Wiley-Blackwell |
| Subjects | |
| Online Access | Get full text |
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| Summary: | Dry subtropical regions (DST), originally hosting woodlands and savannas, are subject to contrasting human pressures and land uses and different degrees of water limitation. We quantified how this variable context influences landscape pattern and vegetation functioning, by exploring the associations between three groups of variables describing (i) human pressures (population density, poverty, and market isolation) and climate (water availability), (ii) landscape pattern (woody cover, infrastructure, paddock size, etc.), and (iii) vegetation functioning (magnitude and stability of primary productivity), in regions of Asia, Africa, Australia, and America. We collected data from global socioeconomic databases and remote sensing products for 4525 samples (representing uncultivated and cultivated conditions), located along 35 transects spanning semiarid to subhumid conditions. A Reciprocal Averaging ordination of uncultivated samples revealed a dominant gradient of declining woody cover accompanied by lower and less stable productivity. This gradient, likely capturing increasing vegetation degradation, had a negative relationship with poverty (characterized by infant mortality) and with market isolation (measured by travel time to large cities). With partial overlaps, regions displayed an increasing degradation ranking from Africa to South America, to Australia, to North America, and to Asia. A similar analysis of cultivated samples, showed a dominant gradient of increasing paddock size accompanied by decreasing primary productivity stability, which included all regions except Asia. This gradient was negatively associated with poverty and population density. A unique combination of small paddocks and high infrastructure differentiated Asian cultivated samples. While water availability gradients were related to productivity trends, they were unrelated to landscape pattern. Our comparative approach suggests that, in DST, human pressures have an overwhelming role driving landscape patterns and one shared with water availability shaping vegetation functioning. |
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| Bibliography: | International Research Development Center - No. 106601-001 Figure S1. Library of uncultivated landscapes from high spatial resolution images (<1-10 m, Quickbird or WorldView to Spot images) available at Google Earth. All landscapes belong to Brigalow-Einasleigh, except panel F. Horizontal bars represent ~50 m. Panels A-D exemplify the woody vegetation fraction; E and F, buildings; G agriculture paddocks; H-J, road network; K, anthropogenic water bodies; L, bare ground; M, surface salt; N, continuous matrix; O and P, crown heterogeneity; Q-S, number of crowns; T-V, and W-Y, diversity of features and land cover types, respectively. Upper right map shows the location of landscapes. Green pins represent the sample points. Figure S2. Library of cultivated landscapes from high spatial resolution images (<1-10 m, Quickbird or WorldView to Spot images) available at Google Earth. Landscapes belong to Chaco (panels B, F, H, and K), India & Pakistan (A, E, I, J, and M), Mesquite (D), and Miombo-Mopane (C, G, L, N, and O). Horizontal bars represent ~500 m. Panels A to D exemplify the size of paddocks; E, buildings; F, road network, G and H, the shape of paddocks; I, the presence of trees inside paddocks; J and K, the roughness of paddocks; L, the presence of signs of cultivation interruption within the landscape; M and N the types of boundary between paddocks; and O, the presence of irrigation infrastructure. Yellow pins represent the sample points.Figure S3. Kendall's τ correlation coefficients across different spatial scales between the context variables and (a) the uncultivated first RA axis, (b) the woody vegetation cover, (c) the cultivated first RA axis, and (d) the paddock size. Upper numbers indicate the considered units in the nonparametric test. In the area between gray dotted lines, coefficients are not significant (P < 0.05). Sample points results (Figs and ) were added to the plots to acknowledge the entire up-scaling effect.Figure S4. Kendall's τ correlation coefficients across different spatial scales between the functioning variables and (a) the uncultivated first RA axis, (b) the woody vegetation cover, (c) the cultivated first RA axis, and (d) the paddock size. Upper numbers indicate the considered units in the nonparametric test. In the area between gray dotted lines, coefficients are not significant (P < 0.05). Sample points results (Figs and ) were added to the plots to acknowledge the entire up-scaling effect. Inter-American Institute for Global Change Research - No. CRN II 2031 istex:BC6E335971E9AD07EDC5A8B81335F4C98F27E338 ANPCyT - No. PRH 27 PICT 2008-00187 ark:/67375/WNG-4V4P5DXT-C ArticleID:GCB12060 US National Science Foundation - No. GEO-0452325 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
| ISSN: | 1354-1013 1365-2486 |
| DOI: | 10.1111/gcb.12060 |