Landscape characteristics associated with species richness and occurrence of small native mammals inhabiting a coastal heathland: a spatial modelling approach

Gibson, Lesley; Wilson, Barbara A.; Aberton, John

doi:10.1016/j.biocon.2004.01.027

Cited by 40 publications

(29 citation statements)

References 43 publications

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“…Thirdly, descriptors of structure and composition of the first vegetative strata, being more directly related to small mammal resources would be pertinent (Catling and Coops, 1999; Pearce et al, 2001; Gibson et al, 2004). This move to direct variables would be of particular relevance in a predictive modelling setting since indirect variables limit model transferability across large areas (Guisan and Thuiller, 2005; Randin et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

“…Slope, aspect and elevation were derived from the DEM in GRASS GIS ( ). The sun index (SI), which provides a proxy for net incident solar radiation, was estimated as (Gibson et al, 2004):

S I = cos (aspect) \times tan (slope) \times 100.

…”

Section: Methodsmentioning

confidence: 99%

“…Hierarchical partitioning (MacNally, 2000, 2002), widely used in explanatory modelling (Gibson et al, 2004; Greaves et al, 2006; Olivier et al, 2000), permits to partition the variance explained by a model in order to isolate the independent versus joint contributions of each variable. We used this method to estimate the independent effects of each variable on the log-likelihood.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, clustering procedure can be biased by the subjective choice of similarity criterion and decision threshold (Anderson and Clements, 2000). In the modelling step (ii) groups are typically modelled against environmental variables, individually via GLM or GAM, or simultaneously using polychotomous regression or discriminant analysis (Lehmann et al, 2002; Gibson et al, 2004; Ferrier and Guisan, 2006). Modelling techniques have been deeply investigated in a “model then classify” approach by which species responses are grouped into assemblages after prediction of their occurrences (Lehmann et al, 2002; Gibson et al, 2004; Ferrier and Guisan, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Modelling and spatial discrimination of small mammal assemblages: An example from western Sichuan (China)

Vaniscotte¹,

Pleydell²,

Raoul³

et al. 2009

Ecological Modelling

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We investigate the relationship between landscape heterogeneity and the spatial distribution of small mammals in two areas of Western Sichuan, China. Given a large diversity of species trapped within a large number of habitats, we first classified small mammal assemblages and then modelled the habitat of each in the space of quantitative environmental descriptors. Our original two step "classify then model" procedure is appropriate for the frequently encountered study scenario: trapping data collected in remote areas with sampling guided by expert field knowledge.In the classification step, we defined assemblages by grouping sites of similar species composition and relative densities using an expert-class-merging procedure which reduced redundancy in the habitat factor used within a multinomial logistic regression predicting species trapping probabilities. Assemblages were thus defined as mixtures of small mammal frequency distributions in discrete groups of sampled sites.In the modelling step, assemblages' habitats and environments of the two sampled areas were discriminated in the space of remotely sensed environmental descriptors. First, we compared the discrimination of assemblage/study areas by linear and non-linear forms of Discriminant Analysis (Linear Discriminant Analysis versus Mixture Discriminant Analysis) and of Multiple Regression (Generalized Linear Models versus Multiple Adaptive Regression Splines). The "best" predictive modelling technique was then used to quantify the contribution of each environmental variable in discriminations of assemblages and areas.Mixtures of Gaussians provided a more efficient model of assemblage coverage in environmental space than a single Gaussian cluster model. However, non-linearity in assemblage response to

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Section: Discussionmentioning

confidence: 99%

“…Slope, aspect and elevation were derived from the DEM in GRASS GIS ( ). The sun index (SI), which provides a proxy for net incident solar radiation, was estimated as (Gibson et al, 2004):

S I = cos (aspect) \times tan (slope) \times 100.

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling and spatial discrimination of small mammal assemblages: An example from western Sichuan (China)

Vaniscotte¹,

Pleydell²,

Raoul³

et al. 2009

Ecological Modelling

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show abstract

“…The absence sites may be represented by random locations across the study area (Ferrier et al 2002;Stockwell and Peterson 2002;IrfanUllah et al 2007) or by mean values and amplitudes of relevant ecological factors (Hirzel et al 2002). When logistic regression models are used, the number of presence sites and absence sites is often equalized (Osborne et al 2001;Gibson et al 2004). Modern GPS technology enables the recording of a large number of observation points and also the use of irregular observation tracks-as the route taken in Weld mapping is usually irregular.…”

Section: Absence Sitesmentioning

confidence: 99%

Similarity-based large-scale distribution mapping of orchids

Remm

2008

Biodivers Conserv

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The expected presence/absence of a target species outside the area of actual observations is commonly estimated using statistical models or decision criteria. This investigation demonstrates a similarity-based solution for predictive mapping as an alternative to generalizing models. The maps of the expected distribution of 12 orchids were created using Wnd sites from Weld observations and absence sites generated onto the observation track. The expected presence/absence of a species in a location was calculated according to the similarity between the location and selected examples of presence and absence sites. A machine learning system selected the best predictive sets for each species out of 161 cartographic and remote sensing features. The usefulness of the predictive distribution maps was expressed as the ratio of the density of Wnd sites per track in the predicted presence area relative to the density per track in the predicted absence area. The predictive mapping was more eYcient for Dactylorhiza incarnata, D. russowii, Gymnadenia conopsea, and Goodyera repens. Soil properties and the proportion of Wnd sites for the other species in the vicinity were the most indicative site characteristics. The rarer species were found to be better indicators of the occurrence of the other species than were the more common orchids. The proposed approach-to direct subsequent Weld observations to sites where the occurrence of the target species was predicted but has not yet been recorded-helped discover new populations of orchids and enhance the representativeness of absence sites.Keywords Orchidaceae · Similarity-based reasoning · Predictive distribution mapping · Indicator value of site characteristics Abbreviations EELIS Estonian Nature Infosystem, a government database of protected areas and veriWed permanent Wnd sites of protected species

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Relationship between habitat characteristics and densities of southern Idaho ground squirrels

et al. 2013

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Many wildlife species living in sagebrush‐steppe habitats of the Intermountain West of the United States have experienced population declines. Effective management of such species, especially restoration efforts, is contingent on understanding relationships between habitat characteristics and population densities of the target species. Unfortunately, even such basic information is often lacking. The goal of this 2‐year study was to determine the relationship between southern Idaho ground squirrel (Urocitellus endemicus) population densities and habitat variables: soils (texture), topography (slope, aspect), and vegetation (canopy cover, species diversity). We measured population density indirectly through burrow entrance counts and categorized them into high‐burrow and low‐burrow densities. We used logistic regression and Akaike's Information Criterion to identify a best subset of models. We employed model averaging and calculated odds ratios for averaged parameter estimates found in the best models. A high density of burrows was associated with greater percentages of silt; east‐facing aspects; greater plant species diversity; and greater cover of perennial grasses, perennial grasses and forbs, and native perennial forbs. Low burrow density was associated with greater percentages of sand; south‐facing aspects; greater cover of exotic annuals; and lesser plant species diversity. Management of southern Idaho ground squirrel habitat should focus on protecting areas with existing native vegetation and restoring native, perennial vegetation in areas that are infested with exotic annuals, especially in areas possessing suitable soil types and topographic features. © 2013 The Wildlife Society.

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Landscape characteristics associated with species richness and occurrence of small native mammals inhabiting a coastal heathland: a spatial modelling approach

Cited by 40 publications

References 43 publications

Modelling and spatial discrimination of small mammal assemblages: An example from western Sichuan (China)

Modelling and spatial discrimination of small mammal assemblages: An example from western Sichuan (China)

Similarity-based large-scale distribution mapping of orchids

Relationship between habitat characteristics and densities of southern Idaho ground squirrels

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