Fire severity and fire‐induced landscape heterogeneity affect arboreal mammals in fire‐prone forests
Tóm tắt
In fire‐prone regions, wildfire influences spatial and temporal patterns of landscape heterogeneity. The likely impacts of climate change on the frequency and intensity of wildfire highlights the importance of understanding how fire‐induced heterogeneity may affect different components of the biota. Here, we examine the influence of wildfire, as an agent of landscape heterogeneity, on the distribution of arboreal mammals in fire‐prone forests in south‐eastern Australia. First, we used a stratified design to examine the role of topography, and the relative influence of fire severity and fire history, on the occurrence of arboreal mammals 2–3 years after wildfire. Second, we investigated the influence of landscape context on the occurrence of arboreal mammals at severely burnt sites. Forested gullies supported a higher abundance of arboreal mammals than slopes. Fire severity was the strongest influence, with abundance lower at severely burnt than unburnt sites. The occurrence of mammals at severely burned sites was influenced by landscape context: abundance increased with increasing amount of unburnt and understorey‐only burnt forest within a 1 km radius. These results support the hypothesis that unburnt forest and moist gullies can serve as refuges for fauna in the post‐fire environment and assist recolonization of severely burned forest. They highlight the importance of spatial heterogeneity created by wildfire and the need to incorporate spatial aspects of fire regimes (e.g., creation and protection of refuges) for fire management in fire‐prone landscapes.
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Tài liệu tham khảo
Adams M., 2011, Burning issues: sustainability and management of Australia's southern forests, 10.1071/9780643103467
Bartoń K.2014.MuMIn: multi-model inference. R package. Version 1.10.5.https://cran.r-project.org/web/packages/MuMIn/
Bates D. M.Maechler B.Bolker andS.Walker.2014.lme4: linear mixed-effects models using Eigen and S4. R package version 1.1-7.https://cran.r-project.org/web/packages/lme4/
Buckland S. T., 2001, Introduction to distance sampling: estimating abundance of biological populations, 10.1093/oso/9780198506492.001.0001
Burnham K. P., 2002, Model selection and multimodel inference: a practical information-theoretic approach. Second edition
Lefcheck J. andJ. S.Casallas.2014.R-squared for generalized linear mixed-effects models. R-script code. Version 0.2-4.https://github.com/jslefche/rsquared.glmer
Mazerolle M. J.2014.AICcmodavg. R package. Version 2.00.https://cran.r-project.org/web/packages/AICcmodavg/
R Core Team, 2014, R: a language and environment for statistical computing
Shlisky A., 2007, Fire, ecosystems and people: threats and strategies for global biodiversity conservation
Teague B., 2010, 2009 Victorian bushfires royal commission: final report
Whelan R. J., 2002, Flammable Australia: the fire regimes and biodiversity of a continent, 94