Sampling design and the shape of species–area curves on the regional scale

Ulrich, Werner; Buszko, J.

doi:10.1016/j.actao.2006.03.005

Cited by 18 publications

(21 citation statements)

References 39 publications

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“…It is also tempting to have a second look at the EPMs, especially the P2 model, which can be made to have a depressed curve compared with the power model at small scales, resulting in a sigmoid curve in arithmetic space and a decelerating curve in log-log space. Ulrich & Buszko (2007) presume that the model may not be the ideal candidate for all SARs but expect it to be useful at intermediate scales. Figure 2a …”

Section: Discussion Of Different Modelsmentioning

confidence: 99%

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Shapes and functions of species–area curves (II): a review of new models and parameterizations

Tjørve¹

2009

Journal of Biogeography

143

156

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This paper has extended and updated my earlier list and analysis of candidate models used in theoretical modelling and empirical examination of species–area relationships (SARs). I have also reviewed trivariate models that can be applied to include a second independent variable (in addition to area) and discussed extensively the justifications for fitting curves to SARs and the choice of model. There is also a summary of the characteristics of several new candidate models, especially extended power models, logarithmic models and parameterizations of the negative‐exponential family and the logistic family. I have, moreover, examined the characteristics and shapes of trivariate linear, logarithmic and power models, including combination variables and interaction terms. The choice of models according to best fit may conflict with problems of non‐normality or heteroscedasticity. The need to compare parameter estimates between data sets should also affect model choice. With few data points and large scatter, models with few parameters are often preferable. With narrow‐scale windows, even inflexible models such as the power model and the logarithmic model may produce good fits, whereas with wider‐scale windows where inflexible models do not fit well, more flexible models such as the second persistence (P2) model and the cumulative Weibull distribution may be preferable. When extrapolations and expected shapes are important, one should consider models with expected shapes, e.g. the power model for sample area curves and the P2 model for isolate curves. The choice of trivariate models poses special challenges, which one can more effectively evaluate by inspecting graphical plots.

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Section: Discussion Of Different Modelsmentioning

confidence: 99%

“…Also, sampling design affects curve shape (Hill et al, 1994;Keeley & Fotheringham, 2005;Ulrich & Buszko, 2007).…”

mentioning

confidence: 99%

Shapes and functions of species–area curves (II): a review of new models and parameterizations

Tjørve¹

2009

Journal of Biogeography

143

156

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“…Whether and how sampling design influences the identification of SAR patterns for vegetation has long been a topic of discussion (Scheiner 2003(Scheiner , 2004Ulrich and Buszko 2007;Keeley and Fotheringham 2009;Dengler and Oldeland 2010). Stohlgren et al (1995) found that a square plot typically captures fewer species than a long, thin rectangle due to the lower ratio of perimeter to surface area in the square.…”

Section: Field Surveymentioning

confidence: 99%

“…Scheiner (2003) divided the types of SARs into two groups, one with contiguous and the other with non-contiguous plots. At small spatial scales, SAR is frequently constructed by a sequential accumulation of replicated plots (Ulrich and Buszko 2007). A contiguous plot design is extremely importance because the plots of a contiguous array are prone to pseudo-replication (Hurlbert 1984).…”

Section: Field Surveymentioning

confidence: 99%

Species-area relationship within and across functional groups at alpine grasslands on the northern Tibetan Plateau, China

Zhou

Shen

et al. 2016

J. Mt. Sci.

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“…Number of species,   : Maximum number of species,  : Cumulative area,       : Constant derived from the sample data set 고 있다 (Kimoto, 1976;Tjørve, 2003;Ulrich and Buszko, 2007).…”

Section: 제한된 채집횟수 및 일부의 표본들로 현장 군집의 모든 종과 상대적인 풍부도를 알아내는 것은 불가능하며 몇몇mentioning

confidence: 99%

Analysis on the Relationship between Number of Species and Survey Area of Benthic Macroinvertebrates Using Weibull Distribution Function

Kong¹,

Kim²

2015

Journal of Korean Society on Water Environment

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The relationship between the number of benthic macroinvertebrate species and the accumulated survey area were investigated in a clean stream and an impaired stream of Korea. Five models to characterize species-area functions were compared, and the Weibull model fitted species-area data well. The other models (Arrhenius, Romell-Gleason, Kylin, Lognormal model) had small or notable bias. The maximum number of species and half-saturation area derived from the Weibull model may be used as the indicators of the carrying capacity and the habitat complexity respectively.

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Sampling design and the shape of species–area curves on the regional scale

Cited by 18 publications

References 39 publications

Shapes and functions of species–area curves (II): a review of new models and parameterizations

Shapes and functions of species–area curves (II): a review of new models and parameterizations

Species-area relationship within and across functional groups at alpine grasslands on the northern Tibetan Plateau, China

Analysis on the Relationship between Number of Species and Survey Area of Benthic Macroinvertebrates Using Weibull Distribution Function

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