气候变化领域集成服务门户(CCPortal): Using fine scale resolution vegetation data from LiDAR and ground-based sampling to predict Pacific marten resting habitat at multiple spatial scales

CCPortal

DOI	10.1016/j.foreco.2019.117556
	Using fine scale resolution vegetation data from LiDAR and ground-based sampling to predict Pacific marten resting habitat at multiple spatial scales
	Tweedy P.J.; Moriarty K.M.; Bailey J.D.; Epps C.W.
发表日期	2019
ISSN	0378-1127
卷号	452
英文摘要	Pacific marten (Martes caurina) populations have become fragmented and constricted throughout their western range, often due to factors such as increased severity of wildfires and timber harvesting. Future population declines are predicted given decreasing snow packs and changes in vegetation communities. One element of marten habitat, rest and den structures, may be particularly vulnerable. These structures are used for protection from inclement weather and predation, as well as sites for parturition. Rest structures are often considered a limiting habitat element; characterizing their abundance, type, and distribution has been suggested as a way to evaluate habitat quality. We evaluated marten resting habitat, combining vegetation data from light detection and ranging technology (LiDAR) and ground-based surveys. From 2009–2013 and 2015–2017, we located 312 unique rest structures used by 31 martens (18 males, 13 females). With ground-based surveys, we examined selection of used structures by measuring the diameter at breast height for comparison of rest structures and trees located in random plots. For broader landscape-level predicted habitat, we paired used locations with 624 randomly-sampled locations, and optimized 14 habitat covariates at 12 spatial scales using case-controlled logistic regression. Each covariate's optimized scale was used to develop a series of a priori hypothesized multi-scale habitat selection models. Martens selected woody structures that were larger than random structures (rest structures = 97.8 ± 31.0 cm; random = 52.7 ± 24.9 cm, x̅ ± SD, t = 21.6, p < 0.001). Marten habitat selection was also positively associated with increased canopy cover and structural complexity within 270 m radius of suitable rest structures and increased tree cover at the broadest scale evaluated (990 m). Our models revealed elevation was positively correlated with predicted marten resting habitat; average elevation at our used sites was 1940 m. Finally, our model depicted areas of predicted habitat near road systems, but we assumed this was an artifact from our sampling bias. Because both canopy cover and structural complexity were optimized at a 270 m radius, this may be an appropriate scale to consider for management activities such as establishing leave islands or focal areas of restoration. We provide one of the first evaluations of marten habitat incorporating the use of LiDAR, which can be broadly and accurately extrapolated for management planning and restoration prioritization. © 2019
英文关键词	California; Habitat modeling; LiDAR; Martes caurina; Multi-scale; Pacific marten
语种	英语
scopus关键词	Logging (forestry); Optical radar; Quality control; Restoration; Surveys; Vegetation; California; Habitat model; Martes caurina; Multi-scale; Pacific marten; Ecosystems; abundance; conservation management; ecological modeling; environmental planning; ground-based measurement; habitat quality; habitat selection; lidar; mustelid; population decline; population distribution; sampling; satellite data; snowpack; California; Ecosystems; Plants; Quality Control; Restoration; Surveys; California; United States; Martes caurina
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/155755
作者单位	Department of Forest Engineering, Resources and Management, Oregon State University, 280 Peavy Hall, Corvallis, OR 97331, United States; USDA Forest Service, Pacific Northwest Research Station, 3625 93rd Avenue SW, Olympia, WA 98512, United States; Department of Fisheries and Wildlife, Oregon State University, 104 Nash Hall, Corvallis, OR 97331, United States
推荐引用方式 GB/T 7714	Tweedy P.J.,Moriarty K.M.,Bailey J.D.,et al. Using fine scale resolution vegetation data from LiDAR and ground-based sampling to predict Pacific marten resting habitat at multiple spatial scales[J],2019,452.
APA	Tweedy P.J.,Moriarty K.M.,Bailey J.D.,&Epps C.W..(2019).Using fine scale resolution vegetation data from LiDAR and ground-based sampling to predict Pacific marten resting habitat at multiple spatial scales.Forest Ecology and Management,452.
MLA	Tweedy P.J.,et al."Using fine scale resolution vegetation data from LiDAR and ground-based sampling to predict Pacific marten resting habitat at multiple spatial scales".Forest Ecology and Management 452(2019).