Literature DB >> 19774325

Forecasting the effects of land-use change on forest rodents in Indiana.

Carol E Rizkalla1, Robert K Swihart.   

Abstract

Forest cover in the upper Wabash River basin in Indiana was fragmented due to agricultural conversion beginning more than 175 years ago. Currently, urban expansion is an important driver of land-use change in the basin. A land transformation model was applied to the basin to forecast land use from 2000 to 2020. We assessed the effect of this projected land-use change scenario on five forest rodent species at three scales: using occupancy models at the patch level, proportional occupancy models at the landscape level, and ecologically scaled landscape indices to assess the change in connectivity at the watershed level. At the patch and landscape scales, occupancy models had low predictability but suggest that gray squirrels are most susceptible to land-use change. At the watershed scale, declines in connectivity did not correspond with the decline of forest. This study highlights the importance of map resolution and consideration of matrix elements in constructing forecast models. Unforeseen drivers of land use, such as changing economic incentives, may also have important ramifications.

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Year:  2009        PMID: 19774325     DOI: 10.1007/s00267-009-9375-8

Source DB:  PubMed          Journal:  Environ Manage        ISSN: 0364-152X            Impact factor:   3.266


  5 in total

1.  Predicting which species will benefit from corridors in fragmented landscapes from population growth models.

Authors:  Brian R Hudgens; Nick M Haddad
Journal:  Am Nat       Date:  2003-05-02       Impact factor: 3.926

2.  Biomass energy: the scale of the potential resource.

Authors:  Christopher B Field; J Elliott Campbell; David B Lobell
Journal:  Trends Ecol Evol       Date:  2008-01-22       Impact factor: 17.712

3.  Toward ecologically scaled landscape indices.

Authors:  C C Vos; J Verboom; P F Opdam; C J Ter Braak
Journal:  Am Nat       Date:  2001-01       Impact factor: 3.926

4.  The matrix matters: effective isolation in fragmented landscapes.

Authors:  T H Ricketts
Journal:  Am Nat       Date:  2001-07       Impact factor: 3.926

5.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

  5 in total

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