Modelling landscape distributions of large forest owls as applied to managing forests in north-east Victoria, Australia

Loyn, Richard; McNabb, Edward G; Volodina, L.; Willig, R. P.

doi:10.1016/s0006-3207(00)00135-x

Cited by 64 publications

(44 citation statements)

References 28 publications

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“…All tests were considered significant at p < 0.05. All significant variables were subsequently entered into a Pearson's correlation matrix to identify collinearities between significant variables (ie, r s ³ 0.65) (Loyn et al 2001). Variables that had collinearities were examined and the variable that explained a greater portion of the deviance was retained.…”

Section: Creation and Evaluation Of Resource Selection Function Modelsmentioning

confidence: 99%

Effects of human activities on the spatial distribution of eastern roe deerCapreolus pygargus bedfordi in the Lesser Khingan Mountains, northeastern China

Jiang

Zhang

et al. 2009

Acta Theriol

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Section: Creation and Evaluation Of Resource Selection Function Modelsmentioning

confidence: 99%

Effects of human activities on the spatial distribution of eastern roe deerCapreolus pygargus bedfordi in the Lesser Khingan Mountains, northeastern China

Jiang

Zhang

et al. 2009

Acta Theriol

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“…All tests were considered significant at P < 0.05. All significant variables were subsequently entered into a Pearson's correlation matrix to identify collinearities between significant variables (i.e., r s ‡ 0.65) (Loyn et al 2001). Variables that had collinearities were examined and the variable that explained a greater portion of the deviance was retained.…”

Section: Creation Of Resource Selection Function Modelsmentioning

confidence: 99%

Multiple spatial‐scale resource selection function models in relation to human disturbance for moose in northeastern China

Jiang

Zhang

et al. 2008

Ecological Research

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The moose (Alces alces cameloides) population in northeastern China is on the southernmost edge of its distribution in Asia. A survey was conducted to determine moose resource selection and the effects of human disturbance on moose in a study area of 20,661 ha located on the northwestern slope of the Lesser Khingan Mountains, located in northeastern China. Predictive models of resource selection were developed using logistic and autologistic regression. All models considered resource variable selection at two spatial scales, patch and landscape. At the patch scale, moose preferred larger birch (Betula platyphylla) patches, but avoided larger tamarack (Larix gmelinii) patches. At the landscape scale, moose preferred higher densities of tamarack patches, i.e., heterogeneity of tamarack stands, selected areas with more abundant annual shoots, terrain conducive to better concealment, higher altitudes and areas saturated with soil moisture. Roads and forest harvest intervals were identified as important human disturbance factors. This is the first time that moose have been reported to avoid roads, and the avoidance distance was nearly 3 km. We believe that in this region moose under the influence of roads are behaviorally plastic, compared with the indifference of moose to the presence of roads in other regions. Moose avoided forest areas logged more than 3 years previously and preferred areas logged 1-2 years previously. In addition, it may be necessary to monitor the effect of the dynamic of density of roe deer on the spatial distribution of the moose population.

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“…Given that finer-scaled habitat variables are unlikely to be captured at landscape level (Austin, 2002), the modelling process could involve two steps, whereby a spatially explicit model is first generated using landscape (GIS) variables, and a second model at site level is developed using fine-scale habitat variables measured on the ground (Gibson et al, 2004). Such multiscale approaches for modelling habitats are not new (Hall and Mannan, 1999;Luck, 2002), but studies involving analyses combining both GIS and on-ground variables are rare (Loyn et al, 2001;Gibson et al, 2004). Because measuring ground variables is money-and timeconsuming, despite the potential power of combining the two above steps, the first one alone is most often used for conservation planning (Gibson et al, 2004), but there are few information about the effectiveness of working only with coarse factors under a GIS option.…”

Section: Introductionmentioning

confidence: 99%

GIS-models work well, but are not enough: Habitat preferences of Lanius collurio at multiple levels and conservation implications

Brambilla

Casale

Bergero

et al. 2009

Biological Conservation

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a b s t r a c tSpecies conservation largely depends on knowledge of habitat needs of target species. GIS-models are increasingly used to assess habitat preferences and distribution of target species, but their accuracy is constrained by availability of digital data layers. We developed a two-steps approach aiming at showing pros and cons of landscape (GIS)-and site-level habitat models, identifying key habitat factors for conservation of a threatened bird species, the red-backed shrike Lanius collurio. A spatially explicit GIS-model was generated using landscape variables, and a second model at site level was developed using fine-scale variables measured on the ground. The GIS-based model was then extrapolated to the entire region to obtain a map of distribution of suitable habitats. Positive associations between shrike occurrence and both hedgerow length and partial shrub cover were detected at both scales. Shrikes were also positively associated with grassland cover at landscape level and with partial cover of untilled herbaceous vegetation at the finer scale, and negatively affected by lucerne cover. The GIS-model led to an affordable map of predicted habitat suitability which should help conservationists to focus on different local priorities, but was unable to identify effects of untilled and lucerne cover. Site-level model gave fine details for habitat management, but its application elsewhere requires ground-measurements of factors. Combining the multiscale models could indicate more urgent actions at large scales (e.g. maintaining suitable habitats, or improving connectivity among isolated patches) and draw a detailed figure of the most suitable habitat for the species. Shrike occurrence was associated with a higher number of shrub and tree species: the indicator value of the species should ensure general benefits for biodiversity from dedicated management.

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Modelling landscape distributions of large forest owls as applied to managing forests in north-east Victoria, Australia

Cited by 64 publications

References 28 publications

Effects of human activities on the spatial distribution of eastern roe deerCapreolus pygargus bedfordi in the Lesser Khingan Mountains, northeastern China

Effects of human activities on the spatial distribution of eastern roe deerCapreolus pygargus bedfordi in the Lesser Khingan Mountains, northeastern China

Multiple spatial‐scale resource selection function models in relation to human disturbance for moose in northeastern China

GIS-models work well, but are not enough: Habitat preferences of Lanius collurio at multiple levels and conservation implications

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