Habitat size and number in multi‐habitat landscapes: a model approach based on species‐area curves

Tjørve, Even

doi:10.1034/j.1600-0587.2002.250103.x

Cited by 40 publications

(28 citation statements)

References 34 publications

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“…Different authors have proposed modifications to the classic-SAR models in order to include various habitats. The models proposed by Tjørve [29] and Triantis et al [30] account for the multi-habitat landscape, differing in that the former suggests the combination of multiple species-area curves (i.e. of different habitats) to describe species diversity, and the latter, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…For example, bird diversity is highly influenced by habitat diversity. In order to accommodate this habitat effect, several studies proposed the incorporation of the multi-habitat context in the SAR framework [29]–[32]. The countryside-SAR model proposed by Pereira and Daily [31] is unique in considering that different species groups differentially use extant set of habitats in a given area.…”

Section: Introductionmentioning

confidence: 99%

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Adaptation of Bird Communities to Farmland Abandonment in a Mountain Landscape

Guilherme

Pereira

2013

PLoS ONE

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Widespread farmland abandonment has led to significant landscape transformations of many European mountain areas. These semi-natural multi-habitat landscapes are important reservoirs of biodiversity and their abandonment has important conservation implications. In multi-habitat landscapes the adaptation of communities depends on the differential affinity of the species to the available habitats. We use nested species-area relationships (SAR) to model species richness patterns of bird communities across scales in a mountain landscape, in NW Portugal. We compare the performance of the classic-SAR and the countryside-SAR (i.e. multi-habitat) models at the landscape scale, and compare species similarity decay (SSD) at the regional scale. We find a considerable overlap of bird communities in the different land-uses (farmland, shrubland and oak forest) at the landscape scale. Analysis of the classic and countryside SAR show that specialist species are strongly related to their favourite habitat. Farmland and shrubland have higher regional SSD compared to oak forests. However, this is due to the opportunistic use of farmlands by generalist birds. Forest specialists display significant regional turnover in oak forest. Overall, the countryside-SAR model had a better fit to the data showing that habitat composition determines species richness across scales. Finally, we use the countryside-SAR model to forecast bird diversity under four scenarios of land-use change. Farmland abandonment scenarios show little impact on bird diversity as the model predicts that the complete loss of farmland is less dramatic, in terms of species diversity loss, than the disappearance of native Galicio-Portuguese oak forest. The affinities of species to non-preferred habitats suggest that bird communities can adapt to land-use changes derived from farmland abandonment. Based on model predictions we argue that rewilding may be a suitable management option for many European mountain areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptation of Bird Communities to Farmland Abandonment in a Mountain Landscape

Guilherme

Pereira

2013

PLoS ONE

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“…One remedy for this is to modify the model by replacing log x with log(1 + x) (e.g. Tjørve, 2002), which gives a + b log(1 + x). This model acquires a convex-downward shape (rather than a straight line) in log-linear space but does not go through the origin of axes.…”

Section: Parameterizations Of the Logarithmic Modelmentioning

confidence: 99%

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

Tjørve¹

2009

Journal of Biogeography

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142

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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“…This is regrettable, for expected curve shape has several implications for crucial research decisions, such as the choice of regression models for empirical SARs or the modeling of species diversity in time and space (Tjørve 2002). This is regrettable, for expected curve shape has several implications for crucial research decisions, such as the choice of regression models for empirical SARs or the modeling of species diversity in time and space (Tjørve 2002).…”

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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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Habitat size and number in multi‐habitat landscapes: a model approach based on species‐area curves

Cited by 40 publications

References 34 publications

Adaptation of Bird Communities to Farmland Abandonment in a Mountain Landscape

Adaptation of Bird Communities to Farmland Abandonment in a Mountain Landscape

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

The importance of samples and isolates for species–area relationships

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