Predicting Human‐Carnivore Conflict: a Spatial Model Derived from 25 Years of Data on Wolf Predation on Livestock

Predicting Human‐Carnivore Conflict: a Spatial Model Derived from 25 Years of Data on Wolf... Abstract: Many carnivore populations escaped extinction during the twentieth century as a result of legal protections, habitat restoration, and changes in public attitudes. However, encounters between carnivores, livestock, and humans are increasing in some areas, raising concerns about the costs of carnivore conservation. We present a method to predict sites of human‐carnivore conflicts regionally, using as an example the mixed forest‐agriculture landscapes of Wisconsin and Minnesota (U.S.A.). We used a matched‐pair analysis of 17 landscape variables in a geographic information system to discriminate affected areas from unaffected areas at two spatial scales (townships and farms). Wolves (Canis lupus) selectively preyed on livestock in townships with high proportions of pasture and high densities of deer (Odocoileus virginianus) combined with low proportions of crop lands, coniferous forest, herbaceous wetlands, and open water. These variables plus road density and farm size also appeared to predict risk for individual farms when we considered Minnesota alone. In Wisconsin only, farm size, crop lands, and road density were associated with the risk of wolf attack on livestock. At the level of townships, we generated two state‐wide maps to predict the extent and location of future predation on livestock. Our approach can be applied wherever spatial data are available on sites of conflict between wildlife and humans. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Conservation Biology Wiley

Predicting Human‐Carnivore Conflict: a Spatial Model Derived from 25 Years of Data on Wolf Predation on Livestock

Loading next page...
 
/lp/wiley/predicting-human-carnivore-conflict-a-spatial-model-derived-from-25-HwEYpzPjHI
Publisher site
See Article on Publisher Site

Abstract

Abstract: Many carnivore populations escaped extinction during the twentieth century as a result of legal protections, habitat restoration, and changes in public attitudes. However, encounters between carnivores, livestock, and humans are increasing in some areas, raising concerns about the costs of carnivore conservation. We present a method to predict sites of human‐carnivore conflicts regionally, using as an example the mixed forest‐agriculture landscapes of Wisconsin and Minnesota (U.S.A.). We used a matched‐pair analysis of 17 landscape variables in a geographic information system to discriminate affected areas from unaffected areas at two spatial scales (townships and farms). Wolves (Canis lupus) selectively preyed on livestock in townships with high proportions of pasture and high densities of deer (Odocoileus virginianus) combined with low proportions of crop lands, coniferous forest, herbaceous wetlands, and open water. These variables plus road density and farm size also appeared to predict risk for individual farms when we considered Minnesota alone. In Wisconsin only, farm size, crop lands, and road density were associated with the risk of wolf attack on livestock. At the level of townships, we generated two state‐wide maps to predict the extent and location of future predation on livestock. Our approach can be applied wherever spatial data are available on sites of conflict between wildlife and humans.

Journal

Conservation BiologyWiley

Published: Feb 1, 2004

References

  • Biological, conservation, and ethical implications of exploiting and controlling wolves
    Haber, Haber
  • Modeling disjunct gray wolf populations in semi‐wild landscapes
    Haight, Haight; Mladenoff, Mladenoff; Wydeven, Wydeven
  • The challenge and opportunity of recovering wolf populations
    Mech, Mech

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off