Small-scale distribution of terrestrial snails: patterns of species richness and abundance related to area (original) (raw)
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Small-scale environmental heterogeneity and the analysis of species distributions along gradients
Journal of Vegetation Science, 1990
The observed distribution of a species along an environmental gradient is strongly affected by environmental variability within a quadrat. Because a quadrat does not represent a point along an environmental gradient, but rather a range of conditions, it is likely to contain species not typically associated with the mean conditions in the quadrat. Systematic relationships exist between a species' true distribution, the observed distribution as a function of mean quadrat environment, and the frequency distribution of the environment within that quadrat. The observed species habitat breadth increases and the observed maximum abundance decreases as within-quadrat environmental heterogeneity increases.
Journal of Biogeography, 2005
Aim Species richness is an important feature of communities that varies along elevational gradients. Different patterns of distribution have been described in the literature for various taxonomic groups. This study aims to distinguish between species density and species richness and to describe, for land snails in southeastern France, the altitudinal patterns of both at different spatial scales. Journal of Biogeography (J. Biogeogr.) (2005) 32, 985-998 ª 2005 Blackwell Publishing Ltd www.blackwellpublishing.com/jbi
2017
50 Questions: How does the spatial arrangement of sampling units influence recorded plant 51 species richness values at small spatial scales? What are the consequences of these findings 52 for sampling methodology and rarefaction analyses? 53 Location: Six semi-natural grasslands in Western Eurasia (France, Germany, Bulgaria, 54 Hungary, Italy, Turkey). 55 Methods: In each site we established six blocks of 40 cm × 280 cm, subdivided into 5 cm × 56 5 cm micro-quadrats, on which we recorded vascular plant species presence with rooted (all 57 sites) and shoot presence method (four sites). Data of these micro-quadrats were then 58 combined to achieve larger sampling units of 0.01, 0.04 and 0.16 m2 grain size with six 59 different spatial arrangements (square, 4:1 rectangle, 16:1 rectangle, three variants of 60 discontiguous randomly placed micro-quadrats). The effect of the spatial arrangements on 61 species richness was then quantified as relative richness compared to the mean richness...
Ecological Indicators, 2013
The contribution of common species to overall species richness in many cases is greater than that of rare species. However, the explanation of this phenomenon remains vague. One hypothesis is that this is a sampling issue and not a biological one. Therefore standardization methods like the information index and empirical variance have been proposed. But, these standardizations do not explicitly compare the significance of the dataset size of the common and rare sub-assemblage. Here, we investigate the role of dataset size in accounting for the capacity of common and rare species to contribute to diversity spatial patterns. We used a dataset of 5148 vascular plant species recorded in 16,439 sample plots in the Greek Natura 2000 network. Species were ranked according to the number of sample plots they occupied in ascending (rare to common), descending (common to rare) and random order. We analyzed the correlation between the richness of each sub-assemblage and total species richness. When comparing among sub-assemblages with equal number of species, common species are clearly the better predictors of total species richness. But, when comparing among sub-assemblages with equal number of occurrence records, the patterns changed. Common and rare species contribution to the overall richness pattern was comparable, with rare species contributing slightly less than widespread species in some cases and the opposite in other cases. However in all cases, sub-assemblages of random species remarkably outperformed the equal sized sub-assemblages of common or rare species. Our results suggest that common and rare species are biased samples of the community and that equal sized random samples are more representative; thus the greater contribution of common species than rare species to biodiversity patterns might be more a sampling issue than a biological effect of commonness or rarity.
Species–area relationships arise from interaction of habitat heterogeneity and species pool
Hydrobiologia, 2012
Species-area relationships (SARs) represent a ubiquitous and useful empirical regularity characterizing biodiversity. The rate of species accumulation, captured by the value of the exponent, z, varies substantially and for many reasons. We hypothesized that one of the major contributors to this variation is heterogeneity and its change with scale. To test this hypothesis, we used an array of natural microcosms for which we had invertebrate species composition and physical properties of habitat. Using GIS and cluster analysis, we organized the species data into four sets: communities grouped by spatial proximity in the field, randomly, by similarity of their physical habitat and by dissimilarity of their physical habitat. These groupings produced varying levels of heterogeneity at different scales. We fitted species-area and species-volume relationships to the four groups of communities, and obtained z-values for each group or a portion of the group if the slope of the relationship varied. As predicted, we recovered a number of properties reported by others. More interestingly, we found that small-and large-scale habitat heterogeneity produced scale-dependent z-values while the random grouping of pool habitats produced z-values more robust across scales but also susceptible to initial values of habitat richness. Habitat area affected rate at which species accumulated much less than the mean degree of inter-habitat differences: increasing area that is heterogeneous at broader scales produces higher z-values than increasing an area that shows heterogeneity at small scale only. Our results, while from a microcosm system, rely on logic transferable to larger scale data sets.