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Habitat and population modelling of roe deer using an interactive geographic information system

Identifieur interne : 001401 ( Istex/Corpus ); précédent : 001400; suivant : 001402

Habitat and population modelling of roe deer using an interactive geographic information system

Auteurs : Volker C. Radeloff ; Anna M. Pidgeon ; Patrick Hostert

Source :

RBID : ISTEX:78C729A54F122E79B2371FFB19BFAE854E7F7E8A

English descriptors

Abstract

Management of German roe deer (Capreolus capreolus) populations is a challenge for wildlife managers and foresters because population densities are difficult to estimate in forests and forest regeneration can be negatively affected when roe deer density is high. We describe a model to determine deer population densities compatible with forest management goals, and to assess harvest rates necessary to maintain desired deer densities. A geographic information system (GIS) was used to model wildlife habitat and population dynamics over time. Our model interactively incorporates knowledge of field biologists and foresters via a graphical user interface (GUI). Calibration of the model with deer damage maps allowed us to evaluate density dependence of a roe deer population. Incorporation of local knowledge into temporally dynamic and spatial models increases understanding of population dynamics and improves wildlife management.

Url:
DOI: 10.1016/S0304-3800(98)00164-1

Links to Exploration step

ISTEX:78C729A54F122E79B2371FFB19BFAE854E7F7E8A

Le document en format XML

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<note type="content">Fig. 1: Land cover of the Holzerath study site in 1993; derived from forest compartment maps and aerial photographs.</note>
<note type="content">Fig. 2: Structure of the integrated habitat and population model based on a GUI in a GIS.</note>
<note type="content">Fig. 3: General relationship between the percentage of pregnant yearlings (YPy) to population density (Dy). Habitat suitability determines maximum and minimum densities (Dmin and Dmax).</note>
<note type="content">Fig. 4: Changes of functional grasslands (grasslands plus regenerating forest stands <5 years old) over time.</note>
<note type="content">Fig. 5: Sensitivity of the theoretical model to: (a) different levels of minimum and maximum density (Dmin and Dmax); (b) different minimum percentages of pregnant yearlings (YPmin); and (c) different minimum fawn survival rates (SvFamin); (densities in no./100 ha).</note>
<note type="content">Fig. 6: (a) Roe deer harvest in the study area Holzerath; and (b) sensitivity of the practical model to different levels of minimum and maximum density (Dmin and Dmax). Four density ranges are explored; the habitat suitability model resulted in:Dmin=6 and Dmax=10 (densities in no./100 ha).</note>
<note type="content">Fig. 7: Sensitivity of population densities calculated by the practical model to (a) population density (Dy) in the initial year; and (b) adult survival rates (SvWf and SvWm); (densities in no./100 ha).</note>
<note type="content">Fig. 8: (a) Effects of different harvesting schemes on population density Dy (in no./100 ha); and (b) population structure (classified into: Amy>2, Ymy, Afy>2, Yfy, and Efy)resulting from the actual harvest.</note>
<note type="content">Table 1: Values for the four parameters of the habitat suitability index (after Ueckermann 1988)</note>
<note type="content">Table 2: Estimated roe deer population densities (no./100 ha) for various habitat values (after Ueckermann 1988)</note>
<note type="content">Table 3: Annual survival probabilities of juveniles (8–20 months old), prime age adults (20 months–7 years old) and senescent adults for roe deer in the Chizé population (from Gaillard et al. 1993)</note>
<note type="content">Table 4: Age classes specific densities for a non-harvested population after running the theoretical model for 50 years</note>
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<forename type="first">Volker C.</forename>
<surname>Radeloff</surname>
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<email>radeloff@students.wisc.edu</email>
<note type="correspondence">
<p>Corresponding author. Tel.: +1-608-2656321; fax.: +1-608-2629922</p>
</note>
<affiliation>Department of Forest Ecology and Management, University of Wisconsin, Madison, WI 53706, USA</affiliation>
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<author xml:id="author-2">
<persName>
<forename type="first">Anna M.</forename>
<surname>Pidgeon</surname>
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<affiliation>Department of Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, WI 53706, USA</affiliation>
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<author xml:id="author-3">
<persName>
<forename type="first">Patrick</forename>
<surname>Hostert</surname>
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<affiliation>Department of Geography, University of Trier, 54296 Trier, Germany</affiliation>
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<title level="j">Ecological Modelling</title>
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<p>Management of German roe deer (Capreolus capreolus) populations is a challenge for wildlife managers and foresters because population densities are difficult to estimate in forests and forest regeneration can be negatively affected when roe deer density is high. We describe a model to determine deer population densities compatible with forest management goals, and to assess harvest rates necessary to maintain desired deer densities. A geographic information system (GIS) was used to model wildlife habitat and population dynamics over time. Our model interactively incorporates knowledge of field biologists and foresters via a graphical user interface (GUI). Calibration of the model with deer damage maps allowed us to evaluate density dependence of a roe deer population. Incorporation of local knowledge into temporally dynamic and spatial models increases understanding of population dynamics and improves wildlife management.</p>
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<item>
<term>Capreolus capreolus</term>
</item>
<item>
<term>Density dependence</term>
</item>
<item>
<term>Germany</term>
</item>
<item>
<term>GIS</term>
</item>
<item>
<term>GUI</term>
</item>
<item>
<term>Habitat model</term>
</item>
<item>
<term>Interactive modelling</term>
</item>
<item>
<term>Population dynamics</term>
</item>
<item>
<term>Roe deer</term>
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<term>Spatially explicit</term>
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<ce:title>Habitat and population modelling of roe deer using an interactive geographic information system</ce:title>
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<ce:author>
<ce:given-name>Volker C.</ce:given-name>
<ce:surname>Radeloff</ce:surname>
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<ce:sup>a</ce:sup>
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<ce:e-address>radeloff@students.wisc.edu</ce:e-address>
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<ce:author>
<ce:given-name>Anna M.</ce:given-name>
<ce:surname>Pidgeon</ce:surname>
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<ce:sup>b</ce:sup>
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<ce:author>
<ce:given-name>Patrick</ce:given-name>
<ce:surname>Hostert</ce:surname>
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<ce:sup>c</ce:sup>
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<ce:textfn>Department of Forest Ecology and Management, University of Wisconsin, Madison, WI 53706, USA</ce:textfn>
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<ce:label>b</ce:label>
<ce:textfn>Department of Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, WI 53706, USA</ce:textfn>
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<ce:label>c</ce:label>
<ce:textfn>Department of Geography, University of Trier, 54296 Trier, Germany</ce:textfn>
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<ce:text>Corresponding author. Tel.: +1-608-2656321; fax.: +1-608-2629922</ce:text>
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<ce:simple-para>Management of German roe deer (
<ce:italic>Capreolus capreolus</ce:italic>
) populations is a challenge for wildlife managers and foresters because population densities are difficult to estimate in forests and forest regeneration can be negatively affected when roe deer density is high. We describe a model to determine deer population densities compatible with forest management goals, and to assess harvest rates necessary to maintain desired deer densities. A geographic information system (GIS) was used to model wildlife habitat and population dynamics over time. Our model interactively incorporates knowledge of field biologists and foresters via a graphical user interface (GUI). Calibration of the model with deer damage maps allowed us to evaluate density dependence of a roe deer population. Incorporation of local knowledge into temporally dynamic and spatial models increases understanding of population dynamics and improves wildlife management.</ce:simple-para>
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<ce:section-title>Keywords</ce:section-title>
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<ce:text>Capreolus capreolus</ce:text>
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<ce:keyword>
<ce:text>Density dependence</ce:text>
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<ce:keyword>
<ce:text>Germany</ce:text>
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<ce:keyword>
<ce:text>GIS</ce:text>
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<ce:keyword>
<ce:text>GUI</ce:text>
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<ce:keyword>
<ce:text>Habitat model</ce:text>
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<ce:keyword>
<ce:text>Interactive modelling</ce:text>
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<ce:keyword>
<ce:text>Population dynamics</ce:text>
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<ce:keyword>
<ce:text>Roe deer</ce:text>
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<affiliation>Department of Forest Ecology and Management, University of Wisconsin, Madison, WI 53706, USA</affiliation>
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<abstract lang="en">Management of German roe deer (Capreolus capreolus) populations is a challenge for wildlife managers and foresters because population densities are difficult to estimate in forests and forest regeneration can be negatively affected when roe deer density is high. We describe a model to determine deer population densities compatible with forest management goals, and to assess harvest rates necessary to maintain desired deer densities. A geographic information system (GIS) was used to model wildlife habitat and population dynamics over time. Our model interactively incorporates knowledge of field biologists and foresters via a graphical user interface (GUI). Calibration of the model with deer damage maps allowed us to evaluate density dependence of a roe deer population. Incorporation of local knowledge into temporally dynamic and spatial models increases understanding of population dynamics and improves wildlife management.</abstract>
<note type="content">Fig. 1: Land cover of the Holzerath study site in 1993; derived from forest compartment maps and aerial photographs.</note>
<note type="content">Fig. 2: Structure of the integrated habitat and population model based on a GUI in a GIS.</note>
<note type="content">Fig. 3: General relationship between the percentage of pregnant yearlings (YPy) to population density (Dy). Habitat suitability determines maximum and minimum densities (Dmin and Dmax).</note>
<note type="content">Fig. 4: Changes of functional grasslands (grasslands plus regenerating forest stands <5 years old) over time.</note>
<note type="content">Fig. 5: Sensitivity of the theoretical model to: (a) different levels of minimum and maximum density (Dmin and Dmax); (b) different minimum percentages of pregnant yearlings (YPmin); and (c) different minimum fawn survival rates (SvFamin); (densities in no./100 ha).</note>
<note type="content">Fig. 6: (a) Roe deer harvest in the study area Holzerath; and (b) sensitivity of the practical model to different levels of minimum and maximum density (Dmin and Dmax). Four density ranges are explored; the habitat suitability model resulted in:Dmin=6 and Dmax=10 (densities in no./100 ha).</note>
<note type="content">Fig. 7: Sensitivity of population densities calculated by the practical model to (a) population density (Dy) in the initial year; and (b) adult survival rates (SvWf and SvWm); (densities in no./100 ha).</note>
<note type="content">Fig. 8: (a) Effects of different harvesting schemes on population density Dy (in no./100 ha); and (b) population structure (classified into: Amy>2, Ymy, Afy>2, Yfy, and Efy)resulting from the actual harvest.</note>
<note type="content">Table 1: Values for the four parameters of the habitat suitability index (after Ueckermann 1988)</note>
<note type="content">Table 2: Estimated roe deer population densities (no./100 ha) for various habitat values (after Ueckermann 1988)</note>
<note type="content">Table 3: Annual survival probabilities of juveniles (8–20 months old), prime age adults (20 months–7 years old) and senescent adults for roe deer in the Chizé population (from Gaillard et al. 1993)</note>
<note type="content">Table 4: Age classes specific densities for a non-harvested population after running the theoretical model for 50 years</note>
<subject lang="en">
<genre>Keywords</genre>
<topic>Capreolus capreolus</topic>
<topic>Density dependence</topic>
<topic>Germany</topic>
<topic>GIS</topic>
<topic>GUI</topic>
<topic>Habitat model</topic>
<topic>Interactive modelling</topic>
<topic>Population dynamics</topic>
<topic>Roe deer</topic>
<topic>Spatially explicit</topic>
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