Species-area curve and spatial pattern

Picard, Nicolas; Karembé, Moussa; Birnbaum, Philippe

doi:10.1080/11956860.2004.11682808

Cited by 20 publications

(20 citation statements)

References 24 publications

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“…Besides these new non‐parametric estimators, new accumulation curve models have also been developed. Following papers by Soberón and Llorente (1993), Nakamura and Peraza (1998), Keating et al (1998), Christen and Nakamura (2000), Gorostiza and Díaz‐Francés (2001), Díaz‐Francés and Gorostiza (2002), Colwell et al (2004), Mao and Colwell (2005) and Mao et al (2005) included nonhomogeneous pure birth processes, maximum likelihood estimation and Bayesian methods into the development and comparison of curve models while Picard et al (2004) developed a curve model that can deal with different spatial patterns.…”

Section: Promising New Research Related To Species Richness Estimationmentioning

confidence: 99%

The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance

2005

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The purpose of this review is to clarify the concepts of bias, precision and accuracy as they are commonly defined in the biostatistical literature, with our focus on the use of these concepts in quantitatively testing the performance of point estimators (specifically species richness estimators). We first describe the general concepts underlying bias, precision and accuracy, and then describe a number of commonly used unscaled and scaled performance measures of bias, precision and accuracy (e.g. mean error, variance, standard deviation, mean square error, root mean square error, mean absolute error, and all their scaled counterparts) which may be used to evaluate estimator performance. We also provide mathematical formulas and a worked example for most performance measures. Since every measure of estimator performance should be viewed as suggestive, not prescriptive, we also mention several other performance measures that have been used by biostatisticians or ecologists. We then outline several guidelines of how to test the performance of species richness estimators: the detailed description of data simulation models and resampling schemes, the use of real and simulated data sets on as many different estimators as possible, mathematical expressions for all estimators and performance measures, and the presentation of results for each scaled performance measure in numerical tables with increasing levels of sampling effort. We finish with a literature review of promising new research related to species richness estimation, and summarize the results of 14 studies that compared estimator performance, which confirm that with most data sets, non‐parametric estimators (mostly the Chao and jackknife estimators) perform better than other estimators, e.g. curve models or fitting species‐abundance distributions.

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Section: Promising New Research Related To Species Richness Estimationmentioning

confidence: 99%

The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance

2005

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“…It has been shown that aggregation and uneven abundances depress the speciesÁarea curves between their endpoints (Solow and Smith 1991, Plotkin et al 2000a, He and Legendre 2002, Green and Ostling 2003, Olszewski 2004, Picard et al 2004, Green and Plotkin 2007), but do not necessarily produce sigmoid shapes (in arithmetic space). It has been shown that aggregation and uneven abundances depress the speciesÁarea curves between their endpoints (Solow and Smith 1991, Plotkin et al 2000a, He and Legendre 2002, Green and Ostling 2003, Olszewski 2004, Picard et al 2004, Green and Plotkin 2007), but do not necessarily produce sigmoid shapes (in arithmetic space).…”

Section: The Sample-area Curve Vs Isolate Curve Continuummentioning

confidence: 99%

The importance of samples and isolates for species–area relationships

Tjørve¹,

Turner²

2009

Ecography

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SpeciesÁarea relationships (SARs) are a key tool for understanding patterns of species diversity. A framework for the interpretation of SARs and their prediction under different landscape configurations remains elusive, however. This article addresses one of these configurations: how species' minimum-area requirements affect the shape of island or other isolate speciesÁarea curves. We distinguish between two classes of SARs: sample-area curves, compiled entirely within larger contiguous areas, and isolate curves, compiled between isolated areas. We develop this conceptual and graphic model in order to illuminate landscape-scale diversity patterns, to discuss how various landscape and species characteristics affect outcomes, and to investigate the dynamics of local extinction under conditions of habitat fragmentation. Minimum-area effects on actual islands and other isolates predictably cause speciesÁarea curves either to be sigmoid in arithmetic space or to be lowered for smaller areas. In order to illustrate the inherent shape of isolate curves, this study fits convex and sigmoid regression models to empirical isolate (island) data sets that cover the small scales expected to include inflection points.

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“…Changes resulting from one factor could be counterbalanced by changes in the other. Picard et al. (2004) derived a model addressing the effect of the spatial distribution of species on the SAR, and found that it could be as important as the species‐abundance distribution in modifying the SAR.…”

Section: Theorymentioning

confidence: 99%

Human impacts on the species–area relationship in reef fish assemblages

et al. 2007

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The relationship between species richness and area is one of the oldest, most recognized patterns in ecology. Here we provide empirical evidence for strong impacts of fisheries exploitation on the slope of the species-area relationship (SAR). Using comparative field surveys of fish on protected and exploited reefs in three oceans and the Mediterranean Sea, we show that exploitation consistently depresses the slope of the SAR for both power-law and exponential models. The magnitude of change appears to be proportional to fishing intensity. Results are independent of taxonomic resolution and robust across coral and rocky reefs, sampling protocols and statistical methods. Changes in species richness, relative abundance and patch occupancy all appear to contribute to this pattern. We conclude that exploitation pressure impacts the fundamental scaling of biodiversity as well as the species richness and spatial distribution patterns of reef fish. We propose that species-area curves can be sensitive indicators of community-level changes in biodiversity, and may be useful in quantifying the human imprint on reef biodiversity, and potentially elsewhere.

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Species-area curve and spatial pattern

Cited by 20 publications

References 24 publications

The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance

The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance

The importance of samples and isolates for species–area relationships

Human impacts on the species–area relationship in reef fish assemblages

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