Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions

被引:214
作者
Bradley, Bethany A. [1 ,2 ]
Curtis, Caroline A. [2 ]
Fusco, Emily J. [2 ]
Abatzoglou, John T. [3 ]
Balch, Jennifer K. [4 ,5 ]
Dadashi, Sepideh [4 ]
Tuanmu, Mao-Ning [6 ]
机构
[1] Univ Massachusetts, Dept Environm Conservat, Amherst, MA 01003 USA
[2] Univ Massachusetts, Grad Program Organism & Evolutionary Biol, Amherst, MA 01003 USA
[3] Univ Idaho, Dept Geog, Moscow, ID 83844 USA
[4] Univ Colorado, Earth Lab, CIRES, Boulder, CO 80309 USA
[5] Univ Colorado, Dept Geog, Boulder, CO 80309 USA
[6] Acad Sinica, Biodivers Res Ctr, Taipei 11529, Taiwan
基金
美国国家航空航天局;
关键词
Bromus tectorum; Fire regime alteration; Grass-fire cycle; Invasive grass; Invasive plant; Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product; GREAT-BASIN; SAGEBRUSH-STEPPE; INVASIVE PLANTS; CLIMATE-CHANGE; COVER CHANGE; SAGE-GROUSE; TIME-SERIES; VEGETATION; WILDFIRE; REGRESSION;
D O I
10.1007/s10530-017-1641-8
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Cheatgrass (Bromus tectorum) is an invasive grass pervasive across the Intermountain Western US and linked to major increases in fire frequency. Despite widespread ecological impacts associated with cheatgrass, we lack a spatially extensive model of cheatgrass invasion in the Intermountain West. Here, we leverage satellite phenology predictors and thousands of field surveys of cheatgrass abundance to create regional models of cheatgrass distribution and percent cover. We compare cheatgrass presence to fire probability, fire seasonality and ignition source. Regional models of percent cover had low predictive power (34% of variance explained), but distribution models based on a threshold of 15% cover to differentiate high abundance from low abundance had an overall accuracy of 74%. Cheatgrass achieves >= 15% cover over 210,000 km(2) (31%) of the Intermountain West. These lands were twice as likely to burn as those with low abundance, and four times more likely to burn multiple times between 2000 and 2015. Fire probability increased rapidly at low cheatgrass cover (1-5%) but remained similar at higher cover, suggesting that even small amounts of cheatgrass in an ecosystem can increase fire risk. Abundant cheatgrass was also associated with a 10 days earlier fire seasonality and interacted strongly with anthropogenic ignitions. Fire in cheatgrass was particularly associated with human activity, suggesting that increased awareness of fire danger in invaded areas could reduce risk. This study suggests that cheatgrass is much more spatially extensive and abundant than previously documented and that invasion greatly increases fire frequency, even at low percent cover.
引用
收藏
页码:1493 / 1506
页数:14
相关论文
共 57 条
[1]   Out of the weeds? Reduced plant invasion risk with climate change in the continental United States [J].
Allen, Jenica M. ;
Bradley, Bethany A. .
BIOLOGICAL CONSERVATION, 2016, 203 :306-312
[2]  
[Anonymous], MAP INVASIVE ANN GRA
[3]   Human-started wildfires expand the fire niche across the United States [J].
Balch, Jennifer K. ;
Bradley, Bethany A. ;
Abatzoglou, John T. ;
Nagy, R. Chelsea ;
Fusco, Emily J. ;
Mahood, Adam L. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2017, 114 (11) :2946-2951
[4]   Introduced annual grass increases regional fire activity across the arid western USA (1980-2009) [J].
Balch, Jennifer K. ;
Bradley, Bethany A. ;
D'Antonio, Carla M. ;
Gomez-Dans, Jose .
GLOBAL CHANGE BIOLOGY, 2013, 19 (01) :173-183
[5]  
Bargeron CT., 2007, WILDLAND WEEDS, V10, P4
[6]   Cheatgrass Percent Cover Change: Comparing Recent Estimates to Climate Change - Driven Predictions in the Northern Great Basin [J].
Boyte, Stephen P. ;
Wylie, Bruce K. ;
Major, Donald J. .
RANGELAND ECOLOGY & MANAGEMENT, 2016, 69 (04) :265-279
[7]   The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth [J].
Boyte, Stephen P. ;
Wylie, Bruce K. ;
Major, Donald J. ;
Brown, Jesslyn F. .
INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2015, 8 (02) :118-132
[8]   Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin [J].
Bradley, BA ;
Mustard, JF .
REMOTE SENSING OF ENVIRONMENT, 2005, 94 (02) :204-213
[9]   Comparison of phenology trends by land cover class: a case study in the Great Basin, USA [J].
Bradley, Bethany A. ;
Mustard, John F. .
GLOBAL CHANGE BIOLOGY, 2008, 14 (02) :334-346
[10]  
Bradley BA, 2006, ECOL APPL, V16, P1132, DOI 10.1890/1051-0761(2006)016[1132:CTLDOA]2.0.CO