Patterns of β-diversity in a Mexican tropical dry forest

Balvanera, Patricia; Lott, Emily J.; Segura, Gerardo; Siebe, Christina; A, Islas

doi:10.1658/1100-9233(2002)013[0145:podiam]2.0.co;2

Cited by 67 publications

(86 citation statements)

References 53 publications

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“…However, the magnitude of this eVect has been underestimated or overlooked in recent comparisons (e.g. Balvanera et al 2002;Cottenie 2005;Chust et al 2006). …”

Section: Data Quality and Variation Partitioningmentioning

confidence: 99%

“…Svenning and Skov (2005) provide an exception, but their data were derived from coarse-scale maps rather than actual soil sampling. In contrast, in those studies where environment explained more variation than space, either soil chemical data were included (Duivenvoorden 1995; Gilbert and Lechowicz 2004;Duque et al 2005;Karst et al 2005; possibly Cottenie 2005 in some cases), disturbed landscapes were included (Dalle et al 2002), or the spatial model was especially coarse (Duivenvoorden 1995;Balvanera et al 2002). In these cases, model choice seems to be a strong predictor of the analysis results.…”

Section: Data Quality and Variation Partitioningmentioning

confidence: 99%

“…Variation partitioning has been used in numerous studies on plant species composition, with the total proportion of variation explained ranging from 20 to 72% in some recent temperate forest studies (Borcard et al 1992; Gilbert and Lechowicz 2004;Cottenie 2005;Karst et al 2005;Svenning and Skov 2005;Thomsen et al 2005;Corney et al 2006), and from 16 to 86% in studies in tropical forests (Duivenvoorden 1995;Balvanera et al 2002;Dalle et al 2002;Arbeláez and Duivenvoorden 2004;Svenning et al 2004;Duque et al 2005;Chust et al 2006).…”

mentioning

confidence: 99%

“…Many tropical forest studies have used only environmental data that are easy to obtain, such as topographic or forest structural variables, or coarse information on soils or geology (e.g. Clark et al 1995;1999;Harms et al 2001;Balvanera et al 2002;Dalle et al 2002;Cannon and Leighton 2004;Valencia et al 2004;Chust et al 2006). Others have also included data from laboratory analyses of soil samples (e.g.…”

mentioning

confidence: 99%

“…Such methodological diVerences may have important consequences for the results, but this has been under-appreciated when diVerent studies have been compared (e.g. Balvanera et al 2002;Cottenie 2005;Chust et al 2006). Moreover, Austin (2002) strongly criticised canonical ordination studies for failing to consider the realistic possibility that species responses to environmental gradients are non-linear.…”

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confidence: 99%

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Explaining variation in tropical plant community composition: influence of environmental and spatial data quality

et al. 2007

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The degree to which variation in plant community composition (beta-diversity) is predictable from environmental variation, relative to other spatial processes, is of considerable current interest. We addressed this question in Costa Rican rain forest pteridophytes (1,045 plots, 127 species). We also tested the effect of data quality on the results, which has largely been overlooked in earlier studies. To do so, we compared two alternative spatial models [polynomial vs. principal coordinates of neighbour matrices (PCNM)] and ten alternative environmental models (all available environmental variables vs. four subsets, and including their polynomials vs. not). Of the environmental data types, soil chemistry contributed most to explaining pteridophyte community variation, followed in decreasing order of contribution by topography, soil type and forest structure. Environmentally explained variation increased moderately when polynomials of the environmental variables were included. Spatially explained variation increased substantially when the multi-scale PCNM spatial model was used instead of the traditional, broad-scale polynomial spatial model. The best model combination (PCNM spatial model and full environmental model including polynomials) explained 32% of pteridophyte community variation, after correcting for the number of sampling sites and explanatory variables. Overall evidence for environmental control of beta-diversity was strong, and the main floristic gradients detected were correlated with environmental variation at all scales encompassed by the study (c. 100-2,000 m). Depending on model choice, however, total explained variation differed more than fourfold, and the apparent relative importance of space and environment could be reversed. Therefore, we advocate a broader recognition of the impacts that data quality has on analysis results. A general understanding of the relative contributions of spatial and environmental processes to species distributions and beta-diversity requires that methodological artefacts are separated from real ecological differences.

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“…However, the magnitude of this eVect has been underestimated or overlooked in recent comparisons (e.g. Balvanera et al 2002;Cottenie 2005;Chust et al 2006). …”

Section: Data Quality and Variation Partitioningmentioning

confidence: 99%

Section: Data Quality and Variation Partitioningmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Explaining variation in tropical plant community composition: influence of environmental and spatial data quality

et al. 2007

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Birds in the Himalayas: What drives beta diversity patterns along an elevational gradient?

Ding²,

Jiang

et al. 2018

Ecology and Evolution

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Beta diversity patterns along elevational gradients have become a hot topic in the study of biogeography and can help illuminate the processes structuring mountain ecosystems. Although elevational species richness patterns have been well documented, there remains much uncertainty over the causes of beta diversity patterns across elevational gradients. We conducted bird surveys and obtained high‐resolution climatic data along an elevational gradient in Gyirong Valley in the central Himalayas, China, between 1,800 and 5,400 m elevation. In total, we recorded 182 bird species (including 169 breeding birds). We simulated beta diversity patterns with the mid‐domain effect (MDE) null model and conducted distance‐based redundancy analyses (db‐RDA) to relate beta diversity to dispersal limitations, spatial constraints, habitat complexity, contemporary climate, and historical climate. Mantel tests and variation partitioning were employed to identify the magnitude of independent statistical associations of environmental factors with beta diversity. Patterns of empirical and simulated beta diversity were both hump‐shaped, peaking at intermediate elevations. The db‐RDA indicated that beta diversity was correlated with changes in spatially structured environmental factors, especially with contemporary climate and habitat complexity. Mantel tests and variation partitioning also suggested that climate dissimilarity was the major independent correlate of beta diversity. The random community structure and spatial constraints may also contribute to the overall hump‐shaped pattern. Beta diversity of bird communities in Gyirong Valley could be explained by the combination of different factors but is mainly shaped by the spatially structured environmental factors, especially contemporary climate.

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Composition of woody species in a dynamic forest-woodland-savannah mosaic in Uganda: implications for conservation and management

Nangendo¹,

Steege²,

Bongers³

2006

Forest Diversity and Management

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Abstract. Forest-woodland-savannah mosaics are a common feature in the East African landscape. For the conservation of the woody species that occur in such landscapes, the species patterns and the factors that maintain it need to be understood. We studied the woody species distribution in a forest-woodland-savannah mosaic in Budongo Forest Reserve, Uganda. The existing vegetation gradients were analyzed using data from a total of 591 plots of 400 or 500 m 2 each. Remotely sensed data was used to explore current vegetation cover and the gradients there in for the whole area. A clear species gradient exists in the study area ranging from forest, where there is least disturbance, to wooded grassland, where frequent fire disturbance occurs. Most species are not limited to a specific part of the gradient although many show a maximum abundance at some point along the gradient. Fire and accessibility to the protected area were closely related to variation in species composition along the ordination axis with species like Cynometra alexandri and Uvariopsis congensis occurring at one end of the gradient and Combretum guenzi and Lonchocarpus laxiflorus at the other. The vegetation cover classes identified in the area differed in diversity, density and, especially, basal area. All vegetation cover classes, except open woodland, had indicator species. Diospyros abyssinica, Uvariopsis congensis, Holoptelea grandis and all Celtis species were the indicator species for the forest class, Terminalia velutina and Albizia grandbracteata for closed woodland, Grewia mollis and Combretum mole for very open woodland and Lonchocarpus laxiflorus, Grewia bicolor and Combretum guenzi for the wooded grassland class. Eleven of the species occurred in all cover classes and most of the species that occurred in more than one vegetation cover class showed peak abundance in a specific cover class. Species composition in the study area changes gradually from forest to savannah. Along the gradient, the cover classes are distinguishable in terms of species composition and vegetation structure. These classes are, however, interrelated in species composition. For conservation of the full range of the species within this East African landscape, the mosaic has to be managed as an integrated whole. Burning should be varied over the area with the forest not being burnt at all and the wooded grassland burnt regularly. The different vegetation types that occur between these two extremes should be maintained using a varied fire regime.

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Patterns of β-diversity in a Mexican tropical dry forest

Cited by 67 publications

References 53 publications

Explaining variation in tropical plant community composition: influence of environmental and spatial data quality

Explaining variation in tropical plant community composition: influence of environmental and spatial data quality

Birds in the Himalayas: What drives beta diversity patterns along an elevational gradient?

Composition of woody species in a dynamic forest-woodland-savannah mosaic in Uganda: implications for conservation and management

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