Scaling issues in the study of livestock depredation by carnivores.

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Title: Scaling issues in the study of livestock depredation by carnivores.
Authors: Slovikosky, Sandy A.1,2 (AUTHOR) sandy.slovikosky@jesus.ox.ac.uk, Petracca, Lisanne S.3 (AUTHOR), Hunter, Luke T. B.4 (AUTHOR), Frair, Jacqueline L.1 (AUTHOR)
Source: Landscape Ecology. Aug2024, Vol. 39 Issue 8, p1-16. 16p.
Subject Terms: *Animal attacks, *Livestock, *Resource allocation
Abstract: Context: Many studies have documented the magnitude and socioecological drivers of livestock depredation, yet few have assessed how observations of depredation might vary with spatiotemporal scale. Understanding scaling relationships may allow for more accurate aggregation of observations collected across diverse extents and resolutions to better inform management actions. Objectives: Herein we examine how reported metrics of livestock depredation varied by spatial and temporal scale (extent and resolution) after controlling for other drivers of differences among studies. Methods: From 213 published studies we extracted conflict metrics (i.e., number of animals killed, number of attack incidents, and percent annual loss of stock) and regressed each against spatiotemporal extent (i.e., size of study area, duration of study) and resolution (i.e., minimum mapping unit, frequency of observations) while controlling for potential biases. Results: The number of attacks or animals killed was positively related to spatial and temporal extent whereas percent annual loss of stock declined with extent. Further, the effects of scaling variables were modified through interactions with other factors (e.g., human density) known to influence human-carnivore conflict. The data available for spatiotemporal resolution were too sparse to draw conclusions. While scaling relationships were generally linear, they became increasingly noisy at broader extents. Conclusions: Consistent with other ecological investigations, the scale of observation influenced the observed outcomes in human-carnivore conflict metrics. Authors should report the spatiotemporal dimensions of their observations to improve robust inference in comparative studies. Enhanced understanding of scaling relationships in human-carnivore conflict metrics should improve strategic allocation of resources to better mitigate future conflict. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: Scaling issues in the study of livestock depredation by carnivores.
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  Data: <searchLink fieldCode="AR" term="%22Slovikosky%2C+Sandy+A%2E%22">Slovikosky, Sandy A.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sandy.slovikosky@jesus.ox.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Petracca%2C+Lisanne+S%2E%22">Petracca, Lisanne S.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hunter%2C+Luke+T%2E+B%2E%22">Hunter, Luke T. B.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Frair%2C+Jacqueline+L%2E%22">Frair, Jacqueline L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Landscape+Ecology%22">Landscape Ecology</searchLink>. Aug2024, Vol. 39 Issue 8, p1-16. 16p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Animal+attacks%22">Animal attacks</searchLink><br />*<searchLink fieldCode="DE" term="%22Livestock%22">Livestock</searchLink><br />*<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Context: Many studies have documented the magnitude and socioecological drivers of livestock depredation, yet few have assessed how observations of depredation might vary with spatiotemporal scale. Understanding scaling relationships may allow for more accurate aggregation of observations collected across diverse extents and resolutions to better inform management actions. Objectives: Herein we examine how reported metrics of livestock depredation varied by spatial and temporal scale (extent and resolution) after controlling for other drivers of differences among studies. Methods: From 213 published studies we extracted conflict metrics (i.e., number of animals killed, number of attack incidents, and percent annual loss of stock) and regressed each against spatiotemporal extent (i.e., size of study area, duration of study) and resolution (i.e., minimum mapping unit, frequency of observations) while controlling for potential biases. Results: The number of attacks or animals killed was positively related to spatial and temporal extent whereas percent annual loss of stock declined with extent. Further, the effects of scaling variables were modified through interactions with other factors (e.g., human density) known to influence human-carnivore conflict. The data available for spatiotemporal resolution were too sparse to draw conclusions. While scaling relationships were generally linear, they became increasingly noisy at broader extents. Conclusions: Consistent with other ecological investigations, the scale of observation influenced the observed outcomes in human-carnivore conflict metrics. Authors should report the spatiotemporal dimensions of their observations to improve robust inference in comparative studies. Enhanced understanding of scaling relationships in human-carnivore conflict metrics should improve strategic allocation of resources to better mitigate future conflict. [ABSTRACT FROM AUTHOR]
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      – Type: doi
        Value: 10.1007/s10980-024-01932-x
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 1
    Subjects:
      – SubjectFull: Animal attacks
        Type: general
      – SubjectFull: Livestock
        Type: general
      – SubjectFull: Resource allocation
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      – TitleFull: Scaling issues in the study of livestock depredation by carnivores.
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            NameFull: Slovikosky, Sandy A.
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            NameFull: Petracca, Lisanne S.
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            NameFull: Hunter, Luke T. B.
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            – D: 01
              M: 08
              Text: Aug2024
              Type: published
              Y: 2024
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              Value: 8
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