Evidence for a General Species–time–area Relationship

Adler, Peter B.; White, Ethan P.; Lauenroth, William K.; Kaufman, Dawn M.; Rassweiler, Andrew; Rusak, James A.

doi:10.1890/05-0067

Cited by 157 publications

(293 citation statements)

References 27 publications

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“…Therefore, if fitted surfaces are only discussed inside the observed ranges then we may also consider models or parameter values that have the ability to produce unrealistic shapes (or results). Thus, both Adler et al (2005) and Storch et al (2005) have reported negative interaction terms (when applying model (7)). …”

Section: Adding An Interaction Termmentioning

confidence: 98%

“…Adler & Lauenroth (2003) and Adler et al (2005) have assessed the latter as a possible model for speciestime-area relationship (STAR). One often employs a logtransformation to improve the fit.…”

Section: Trivariate Modelsmentioning

confidence: 99%

“…Multiple regressions assessing the effect of two or more independent variables (transformed or not) on species numbers are also very common (e.g. Dodson, 1992;Adler & Lauenroth, 2003;Triantis et al, 2003;Adler et al, 2005;Storch et al, 2005;Kalmar & Currie, 2006, 2007Ulrich, 2006;Evans et al, 2007).…”

Section: Trivariate Modelsmentioning

confidence: 99%

“…The model is fitted as a straight line in log-log space. Adler & Lauenroth (2003) and Adler et al (2005) have also tried this model, with a multiplicative combination term, as a candidate STAR model. One may also consider models with additive combination terms, such as a + b(x + y) and a + b(log x + log y) or other.…”

Section: Combination Variablesmentioning

confidence: 99%

“…In models (5), (6) and (7), multiplicative interaction terms as d(xy) and d(log x)(log y) (where d represents a fourth parameter) investigates this effect. Model (7) is the same as the 'full' species-time-area model used by Adler & Lauenroth (2003) and Adler et al (2005). Storch et al (2005) (using NDVI as a measure of energy) and others have applied the same model to species-area-energy relationships.…”

Section: Adding An Interaction Termmentioning

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: Adding An Interaction Termmentioning

confidence: 98%

Section: Trivariate Modelsmentioning

confidence: 99%

Section: Trivariate Modelsmentioning

confidence: 99%

Section: Combination Variablesmentioning

confidence: 99%

Section: Adding An Interaction Termmentioning

confidence: 99%

See 3 more Smart Citations

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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The effect of the sampling scale on zooplankton community assessment and its implications for the conservation of temporary ponds in south‐west Spain

Fahd

Florencio

Keller

et al. 2006

Aquatic Conservation

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ABSTRACT1. The zooplankton (rotifer and microcrustacean) assemblages of temporary ponds in the Don˜ana National Park (south-west Spain) have been compared in two surveys of contrasting scales that resulted in the same number of samples: an extensive survey of 36 ponds sampled in May 1998 (or widespread survey) and a survey of nine ponds sampled four times over 2 years (or cumulative survey).2. The total number of microcrustacean and rotifer taxa was larger in the cumulative survey (43 and 41 taxa, respectively) than in the widespread survey (39 and 34, respectively). Crustacean assemblages became less alike throughout the cumulative survey.3. The presence of invertebrates (Coleoptera, Odonata, Heteroptera and crayfish) and aquatic vertebrates (fish and salamanders) was recorded as an estimate of potential predator impact on zooplankton. Several pond features (water depth, conductivity, pH, chlorophyll a concentration, distance to the nearest permanent pond and to the marsh) were also measured in both surveys.4. A combination of these environmental factors was more strongly related to the similarity matrices derived from the zooplankton assemblages of the cumulative survey (Rho ¼ 0:7) than to those of the widespread survey (Rho50.4). The distance of ponds to the marsh was an important factor in explaining this correlation as well as the strongest factor in the ordination of crustacean assemblages following a CCA.5. Predation by exotic fish in long-hydroperiod ponds where overflow drains to the nearby marsh (fish source) is the mechanism likely to explain the changes in crustacean composition recorded in the cumulative survey.6. The cumulative survey was more suitable for the study of zooplankton diversity as it rendered a higher number of taxa and gave more insight into the mechanisms that explain taxon richness. Thus,

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Species‐time‐area and phylogenetic‐time‐area relationships in tropical tree communities

Swenson

Kress

et al. 2013

Ecology and Evolution

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The species-area relationship (SAR) has proven to be one of the few strong generalities in ecology. The temporal analog of the SAR, the species-time relationship (STR), has received considerably less attention. Recent work primarily from the temperate zone has aimed to merge the SAR and the STR into a synthetic and unified species-time-area relationship (STAR) as originally envisioned by Preston (1960). Here we test this framework using two tropical tree communities and extend it by deriving a phylogenetic-time-area relationship (PTAR). The work finds some support for Preston's prediction that diversity-time relationships, both species and phylogenetic, are sensitive to the spatial scale of the sampling. Contrary to the Preston's predictions we find a decoupling of diversity-area and diversity-time relationships in both forests as the time period used to quantify the diversity-area relationship changes. In particular, diversity-area and diversity-time relationships are positively correlated using the initial census to quantify the diversity-area relationship, but weakly or even negatively correlated when using the most recent census. Thus, diversity-area relationships could forecast the temporal accumulation of biodiversity of the forests, but they failed to “back-cast” the temporal accumulation of biodiversity suggesting a decoupling of space and time.

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Evidence for a General Species–time–area Relationship

Cited by 157 publications

References 27 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

The effect of the sampling scale on zooplankton community assessment and its implications for the conservation of temporary ponds in south‐west Spain

Species‐time‐area and phylogenetic‐time‐area relationships in tropical tree communities

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