Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality

The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine ( Pinus ponderosa ) mortality, largely attributable to the western pine beetle ( Dendroctonus brevicomis ; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates res...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Nature communications Ročník 12; číslo 1; s. 129 - 13
Hlavní autori: Koontz, Michael J., Latimer, Andrew M., Mortenson, Leif A., Fettig, Christopher J., North, Malcolm P.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 08.01.2021
Nature Publishing Group
Nature Portfolio
Predmet:
631/158/2165
704/158/2454
704/158/851
Aerial surveys
Animals
Bark
Beetles
California
Climate adaptation
Climate change
Climatic conditions
Dendroctonus brevicomis
Drone aircraft
Drought
Droughts
Ecological Parameter Monitoring - statistics & numerical data
Evergreen trees
Host-Parasite Interactions - physiology
Humanities and Social Sciences
Mortality
Mortality patterns
multidisciplinary
open climate campaign
Pest outbreaks
Pheromones - metabolism
Pine
Pine trees
Pinus ponderosa
Pinus ponderosa - parasitology
Pinus ponderosa - physiology
Plant Bark - parasitology
Plant Diseases - parasitology
Plant Dispersal
Science
Science (multidisciplinary)
Trees
Trees - parasitology
Trees - physiology
Water
Water deficit
Weevils - pathogenicity
Weevils - physiology
California
ISSN:2041-1723, 2041-1723
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine ( Pinus ponderosa ) mortality, largely attributable to the western pine beetle ( Dendroctonus brevicomis ; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km 2 , validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality. The 2012–2016 drought and western pine beetle outbreaks caused unprecedented mortality of ponderosa pine in the Sierra Nevada, California. Here, the authors analyse drone-based data from almost half a million trees and find an interaction between host size and climatic water deficit, with higher mortality for large trees in dry, warm conditions but not in cooler or wetter conditions.
AbstractList The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine ( Pinus ponderosa ) mortality, largely attributable to the western pine beetle ( Dendroctonus brevicomis ; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km 2 , validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality. The 2012–2016 drought and western pine beetle outbreaks caused unprecedented mortality of ponderosa pine in the Sierra Nevada, California. Here, the authors analyse drone-based data from almost half a million trees and find an interaction between host size and climatic water deficit, with higher mortality for large trees in dry, warm conditions but not in cooler or wetter conditions.
The recent Californian hot drought (2012-2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine beetle (Dendroctonus brevicomis; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km2, validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality.The recent Californian hot drought (2012-2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine beetle (Dendroctonus brevicomis; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km2, validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality.
The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine beetle (Dendroctonus brevicomis; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km2, validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality. The 2012–2016 drought and western pine beetle outbreaks caused unprecedented mortality of ponderosa pine in the Sierra Nevada, California. Here, the authors analyse drone-based data from almost half a million trees and find an interaction between host size and climatic water deficit, with higher mortality for large trees in dry, warm conditions but not in cooler or wetter conditions.
The recent Californian hot drought (2012-2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine beetle (Dendroctonus brevicomis; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km , validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality.
The 2012–2016 drought and western pine beetle outbreaks caused unprecedented mortality of ponderosa pine in the Sierra Nevada, California. Here, the authors analyse drone-based data from almost half a million trees and find an interaction between host size and climatic water deficit, with higher mortality for large trees in dry, warm conditions but not in cooler or wetter conditions.
The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine ( Pinus ponderosa ) mortality, largely attributable to the western pine beetle ( Dendroctonus brevicomis ; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km 2 , validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality.
ArticleNumber 129
Author Mortenson, Leif A.
Latimer, Andrew M.
North, Malcolm P.
Koontz, Michael J.
Fettig, Christopher J.
Author_xml – sequence: 1
  givenname: Michael J.
  orcidid: 0000-0002-8276-210X
  surname: Koontz
  fullname: Koontz, Michael J.
  email: michael.koontz@colorado.edu
  organization: Graduate Group in Ecology, University of California, Department of Plant Sciences, University of California, Earth Lab, University of Colorado-Boulder
– sequence: 2
  givenname: Andrew M.
  orcidid: 0000-0001-8098-0448
  surname: Latimer
  fullname: Latimer, Andrew M.
  organization: Graduate Group in Ecology, University of California, Department of Plant Sciences, University of California
– sequence: 3
  givenname: Leif A.
  surname: Mortenson
  fullname: Mortenson, Leif A.
  organization: USDA Forest Service, Pacific Southwest Research Station
– sequence: 4
  givenname: Christopher J.
  surname: Fettig
  fullname: Fettig, Christopher J.
  organization: USDA Forest Service, Pacific Southwest Research Station
– sequence: 5
  givenname: Malcolm P.
  surname: North
  fullname: North, Malcolm P.
  organization: Graduate Group in Ecology, University of California, Department of Plant Sciences, University of California, USDA Forest Service, Pacific Southwest Research Station
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33420082$$D View this record in MEDLINE/PubMed
BookMark eNp9kjtvFDEUhUcoiISQP0CBLNHQDPj9aJBQxCNSJBqoLY_nzsbLrB1sL2j59Xh3EkhSxI0t-5xPx_fe591RTBG67iXBbwlm-l3hhEvVY4p7irkQ_e5Jd9JOpCeKsqM75-PurJQ1bosZojl_1h0zxinGmp506TynUvri3QwoxArZ-RpSRGlCV6lUVDMAKuEPIBdH5OewcTV49Ns1KRphCj5UtEq_IMeCBpd_oAGgztCHOG49jAtgk3J1c6i7F93Tyc0Fzm720-77p4_fzr_0l18_X5x_uOy94Lj2jPsBNNeUSgA8mEnqQTJMuBiZbnfYGc-YkWKSimsMzEzaADZawuidJOy0u1i4Y3Jre51b7LyzyQV7uEh5ZV1uH5nBaqGIZxQrTRU3gzHgRAML4yUwoqbGer-wrrfDpvEh1uzme9D7LzFc2VYSq5ThguzDvLkB5PRzC6XaTSge5tlFSNtiKVdSSIKpatLXD6TrtM2xleqgooK0MjTVq7uJ_kW57WsT6EXg9-3NMNnWJrdvbAsYZkuw3U-RXabItimyhymyu2alD6y39EdNbDGVJo4ryP9jP-L6CyUm2hE
CitedBy_id crossref_primary_10_1016_j_jhydrol_2022_127431
crossref_primary_10_3390_f14102074
crossref_primary_10_1371_journal_pone_0288067
crossref_primary_10_1093_forestry_cpaf043
crossref_primary_10_3390_rs14133205
crossref_primary_10_1016_j_plantsci_2022_111319
crossref_primary_10_1002_eap_2514
crossref_primary_10_1016_j_jag_2024_104257
crossref_primary_10_1038_s41467_023_43083_8
crossref_primary_10_3390_s23249907
crossref_primary_10_1002_ecs2_4478
crossref_primary_10_1016_j_foreco_2023_121478
crossref_primary_10_3389_ffgc_2021_679104
crossref_primary_10_1016_j_foreco_2022_120183
crossref_primary_10_1016_j_foreco_2024_122184
crossref_primary_10_1016_j_foreco_2024_122382
crossref_primary_10_3389_ffgc_2025_1498430
crossref_primary_10_1038_s41598_022_08825_6
crossref_primary_10_1111_nph_18144
crossref_primary_10_1093_jofore_fvab026
crossref_primary_10_1007_s11104_022_05315_6
crossref_primary_10_1016_j_scitotenv_2023_164223
crossref_primary_10_1016_j_foreco_2022_120099
crossref_primary_10_3390_microorganisms10030503
crossref_primary_10_1016_j_agrformet_2025_110533
crossref_primary_10_3389_ffgc_2024_1513721
crossref_primary_10_24072_pcjournal_223
crossref_primary_10_3390_fire6110428
crossref_primary_10_1002_eap_2854
crossref_primary_10_1186_s42408_023_00175_6
crossref_primary_10_1016_j_jhydrol_2023_130387
crossref_primary_10_1038_s41467_024_44991_z
crossref_primary_10_1016_j_ecolmodel_2024_110814
crossref_primary_10_3389_ffgc_2023_1130721
crossref_primary_10_1038_s41598_025_04370_0
crossref_primary_10_3390_rs13061089
crossref_primary_10_1016_j_foreco_2024_121814
crossref_primary_10_1016_j_compag_2024_108785
crossref_primary_10_1016_j_foreco_2023_121135
crossref_primary_10_1016_j_rse_2023_113842
crossref_primary_10_1016_j_foreco_2022_120480
crossref_primary_10_1145_3625387
crossref_primary_10_1002_ecs2_4454
crossref_primary_10_1016_j_ecolind_2025_113326
crossref_primary_10_1111_afe_12586
crossref_primary_10_1016_j_cois_2022_101003
crossref_primary_10_1016_j_agrformet_2022_109025
crossref_primary_10_1016_j_foreco_2025_122549
crossref_primary_10_1007_s11056_023_09991_9
crossref_primary_10_1111_1365_2745_13830
crossref_primary_10_3389_ffgc_2025_1566459
crossref_primary_10_1093_treephys_tpad151
crossref_primary_10_3390_pathogens10111390
crossref_primary_10_1016_j_foreco_2023_121307
crossref_primary_10_1111_plb_13469
crossref_primary_10_1016_j_agrformet_2025_110592
crossref_primary_10_3390_rs14225861
crossref_primary_10_1002_ecs2_4206
crossref_primary_10_1007_s10340_022_01510_7
crossref_primary_10_1002_ecs2_4400
crossref_primary_10_1016_j_foreco_2024_122010
Cites_doi 10.1525/bio.2010.60.8.6
10.1146/annurev-ento-020117-043339
10.17605/OSF.IO/3CWF9
10.1088/1748-9326/aa8f55
10.1002/ecy.1963
10.1002/eap.1902
10.4039/Ent114797-9
10.1641/B580607
10.1111/1365-2664.12782
10.32614/CRAN.package.ForestTools
10.1146/annurev.ecolsys.31.1.343
10.1016/S0176-1617(11)81633-0
10.18637/jss.v028.i05
10.1111/j.1469-8137.2008.02436.x
10.1016/j.foreco.2006.10.011
10.1038/s41467-019-12380-6
10.1890/02-4004
10.1016/j.foreco.2011.11.038
10.1186/2192-1709-2-25
10.1093/jee/98.6.2041
10.1002/eco.1286
10.3390/rs11111309
10.4039/tce.2012.38
10.18637/jss.v076.i01
10.18637/jss.v080.i01
10.1029/2005RG000183
10.1007/s10980-016-0460-0
10.1002/fee.2031
10.1111/1365-2745.13176
10.1111/2041-210X.13132
10.1139/x11-041
10.1016/j.ecolmodel.2009.01.039
10.1126/science.164.3885.1284
10.1603/EC12184
10.1111/ele.12711
10.1139/x2012-102
10.1016/j.isprsjprs.2018.11.008
10.1890/06-1715.1
10.1016/B978-0-12-417156-5.00001-0
10.1016/j.foreco.2018.09.006
10.1093/ee/7.5.732
10.1002/2015GL064593
10.1016/j.rse.2013.07.041
10.1007/s00049-007-0378-8
10.1038/s41467-020-17213-5
10.1111/j.1461-0248.2007.01073.x
10.3390/rs10060912
10.1046/j.1365-2699.1998.00233.x
10.1111/ecog.02769
10.3390/s19163595
10.3390/rs5094163
10.1007/978-3-319-24744-1_18
10.2307/1942586
10.7930/NCA4.2018.CH6
10.1016/j.foreco.2016.04.051
10.3390/f5010153
10.1006/tpbi.1997.1350
10.3390/rs8060501
10.1111/1365-2664.12540
10.1016/j.foreco.2019.01.033
10.1093/bioinformatics/btq046
10.2307/1940551
10.1002/esp.3609
10.1038/s41467-020-17214-4
10.1007/BF00987534
10.3390/rs11151797
10.1111/nph.13477
10.1002/2014GL062433
10.1111/j.1469-8137.2005.01436.x
10.1603/EN13244
10.1111/rssa.12378
10.3389/ffgc.2019.00039
10.1073/pnas.1523397113
10.14358/PERS.78.1.75
10.1016/j.foreco.2009.11.030
10.3390/rs10081266
10.1080/10618600.1998.10474787
10.1016/B978-0-12-417156-5.00014-9
10.1139/x2012-031
10.1016/j.foreco.2018.12.006
10.1016/j.rse.2006.03.012
10.1016/j.foreco.2019.02.011
10.1603/029.102.0621
10.3390/f10030237
10.1007/BF00988635
10.3732/ajb.1600326
10.3390/f6051721
10.3390/f5010088
10.6078/D16K5W
10.1016/1047-3203(90)90014-M
10.17605/OSF.IO/WPK5Z
10.2307/2986328
ContentType Journal Article
Copyright The Author(s) 2021
The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s) 2021
– notice: The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID C6C
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7QL
7QP
7QR
7SN
7SS
7ST
7T5
7T7
7TM
7TO
7X7
7XB
88E
8AO
8FD
8FE
8FG
8FH
8FI
8FJ
8FK
ABUWG
AEUYN
AFKRA
ARAPS
AZQEC
BBNVY
BENPR
BGLVJ
BHPHI
C1K
CCPQU
DWQXO
FR3
FYUFA
GHDGH
GNUQQ
H94
HCIFZ
K9.
LK8
M0S
M1P
M7P
P5Z
P62
P64
PHGZM
PHGZT
PIMPY
PJZUB
PKEHL
PPXIY
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
RC3
SOI
7X8
5PM
DOA
DOI 10.1038/s41467-020-20455-y
DatabaseName Springer Nature Link
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Bacteriology Abstracts (Microbiology B)
Calcium & Calcified Tissue Abstracts
Chemoreception Abstracts
Ecology Abstracts
Entomology Abstracts (Full archive)
Environment Abstracts
Immunology Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
Nucleic Acids Abstracts
Oncogenes and Growth Factors Abstracts
ProQuest - Health & Medical Complete保健、医学与药学数据库
ProQuest Central (purchase pre-March 2016)
Medical Database (Alumni Edition)
ProQuest Pharma Collection
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Natural Science Collection
ProQuest Hospital Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest One Sustainability
ProQuest Central
ProQuest SciTech Premium Collection Technology Collection Advanced Technologies & Aerospace Collection
ProQuest Central Essentials - QC
Biological Science Collection
Proquest Central
ProQuest Technology Collection
Natural Science Collection
Environmental Sciences and Pollution Management
ProQuest One
ProQuest Central Korea
Engineering Research Database
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Central Student
AIDS and Cancer Research Abstracts
SciTech Premium Collection
ProQuest Health & Medical Complete (Alumni)
Biological Sciences
ProQuest Health & Medical Collection
Medical Database
Biological Science Database
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
Biotechnology and BioEngineering Abstracts
ProQuest Central Premium
ProQuest One Academic
ProQuest - Publicly Available Content Database
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
Genetics Abstracts
Environment Abstracts
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Publicly Available Content Database
ProQuest Central Student
Oncogenes and Growth Factors Abstracts
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
Nucleic Acids Abstracts
SciTech Premium Collection
ProQuest Central China
Environmental Sciences and Pollution Management
ProQuest One Applied & Life Sciences
ProQuest One Sustainability
Health Research Premium Collection
Natural Science Collection
Health & Medical Research Collection
Biological Science Collection
Chemoreception Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
ProQuest Central (New)
ProQuest Medical Library (Alumni)
Advanced Technologies & Aerospace Collection
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
ProQuest Technology Collection
Health Research Premium Collection (Alumni)
Biological Science Database
Ecology Abstracts
ProQuest Hospital Collection (Alumni)
Biotechnology and BioEngineering Abstracts
Entomology Abstracts
ProQuest Health & Medical Complete
ProQuest One Academic UKI Edition
Engineering Research Database
ProQuest One Academic
Calcium & Calcified Tissue Abstracts
ProQuest One Academic (New)
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest One Health & Nursing
ProQuest Natural Science Collection
ProQuest Pharma Collection
ProQuest Central
ProQuest Health & Medical Research Collection
Genetics Abstracts
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
Bacteriology Abstracts (Microbiology B)
AIDS and Cancer Research Abstracts
ProQuest SciTech Collection
Advanced Technologies & Aerospace Database
ProQuest Medical Library
Immunology Abstracts
Environment Abstracts
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList
MEDLINE - Academic

MEDLINE

Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Biology
EISSN 2041-1723
EndPage 13
ExternalDocumentID oai_doaj_org_article_8571c320782749b99ea565f59c6e317f
PMC7794511
33420082
10_1038_s41467_020_20455_y
Genre Research Support, U.S. Gov't, Non-P.H.S
Journal Article
GeographicLocations California
GeographicLocations_xml – name: California
GrantInformation_xml – fundername: USDA Forest Service Western Wildland Environmental Threat Assessment Center Pacific Southwest Research Station Climate Change Competitive Grant Program CU Boulder’s Grand Challenge Initiative University of Colorado Boulder Grand Challenge
– fundername: ;
GroupedDBID ---
0R~
39C
3V.
53G
5VS
70F
7X7
88E
8AO
8FE
8FG
8FH
8FI
8FJ
AAHBH
AAJSJ
ABUWG
ACGFO
ACGFS
ACIWK
ACMJI
ACPRK
ACSMW
ADBBV
ADFRT
ADMLS
ADRAZ
AENEX
AEUYN
AFKRA
AFRAH
AHMBA
AJTQC
ALIPV
ALMA_UNASSIGNED_HOLDINGS
AMTXH
AOIJS
ARAPS
ASPBG
AVWKF
AZFZN
BBNVY
BCNDV
BENPR
BGLVJ
BHPHI
BPHCQ
BVXVI
C6C
CCPQU
DIK
EBLON
EBS
EE.
EMOBN
F5P
FEDTE
FYUFA
GROUPED_DOAJ
HCIFZ
HMCUK
HVGLF
HYE
HZ~
KQ8
LK8
M1P
M48
M7P
M~E
NAO
O9-
OK1
P2P
P62
PIMPY
PQQKQ
PROAC
PSQYO
RNS
RNT
RNTTT
RPM
SNYQT
SV3
TSG
UKHRP
AASML
AAYXX
AFFHD
CITATION
PHGZM
PHGZT
PJZUB
PPXIY
PQGLB
CGR
CUY
CVF
ECM
EIF
NPM
7QL
7QP
7QR
7SN
7SS
7ST
7T5
7T7
7TM
7TO
7XB
8FD
8FK
AZQEC
C1K
DWQXO
FR3
GNUQQ
H94
K9.
P64
PKEHL
PQEST
PQUKI
PRINS
RC3
SOI
7X8
PUEGO
5PM
ID FETCH-LOGICAL-c540t-34cbe848226ee0b9f68b630145d3826e0a9c33965f67480e39f89e0986edca613
IEDL.DBID DOA
ISICitedReferencesCount 71
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000626604400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2041-1723
IngestDate Fri Oct 03 12:35:15 EDT 2025
Tue Nov 04 01:53:46 EST 2025
Thu Sep 04 15:51:05 EDT 2025
Sat Nov 29 15:09:52 EST 2025
Wed Feb 19 02:23:43 EST 2025
Sat Nov 29 06:29:05 EST 2025
Tue Nov 18 22:43:10 EST 2025
Fri Feb 21 02:39:01 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c540t-34cbe848226ee0b9f68b630145d3826e0a9c33965f67480e39f89e0986edca613
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0001-8098-0448
0000-0002-8276-210X
OpenAccessLink https://doaj.org/article/8571c320782749b99ea565f59c6e317f
PMID 33420082
PQID 2476251630
PQPubID 546298
PageCount 13
ParticipantIDs doaj_primary_oai_doaj_org_article_8571c320782749b99ea565f59c6e317f
pubmedcentral_primary_oai_pubmedcentral_nih_gov_7794511
proquest_miscellaneous_2476561027
proquest_journals_2476251630
pubmed_primary_33420082
crossref_citationtrail_10_1038_s41467_020_20455_y
crossref_primary_10_1038_s41467_020_20455_y
springer_journals_10_1038_s41467_020_20455_y
PublicationCentury 2000
PublicationDate 2021-01-08
PublicationDateYYYYMMDD 2021-01-08
PublicationDate_xml – month: 01
  year: 2021
  text: 2021-01-08
  day: 08
PublicationDecade 2020
PublicationPlace London
PublicationPlace_xml – name: London
– name: England
PublicationTitle Nature communications
PublicationTitleAbbrev Nat Commun
PublicationTitleAlternate Nat Commun
PublicationYear 2021
Publisher Nature Publishing Group UK
Nature Publishing Group
Nature Portfolio
Publisher_xml – name: Nature Publishing Group UK
– name: Nature Publishing Group
– name: Nature Portfolio
References Baldwin, B. G. et al. Master spatial file for native California vascular plants used by Baldwin et al. (2017 Amer. J. Bot.), Dryad, Dataset, 2017. https://doi.org/10.6078/D16K5W.
Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Local-structure-wpb-severity. https://doi.org/10.17605/OSF.IO/WPK5Z (2019).
HoffmanMDGelmanAThe no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloJ. Mach. Learn. Res.2014153132147791319.60150
LoganJAWhitePBentzBJPowellJAModel analysis of spatial patterns in mountain pine beetle outbreaksTheor. Popul. Biol.1998532362551:STN:280:DC%2BC2sjnvFeluw%3D%3D96820260941.9203010.1006/tpbi.1997.1350
ShinPSankeyTMooreMThodeAEvaluating unmanned aerial vehicle images for estimating forest canopy fuels in a ponderosa pine standRemote Sens.20181012662018RemS...10.1266S10.3390/rs10081266
Netherer, S., Panassiti, B., Pennerstorfer, J. & Matthews, B. Acute drought Is an important driver of bark beetle infestation in Austrian Norway spruce stands. Front. For. Glob. Change2, 39 (2019).
ZhangWAn easy-to-use airborne LiDAR data filtering method based on cloth simulationRemote Sens.201685012016RemS....8..501Z10.3390/rs8060501
FettigCJMortensonLABulaonBMFoulkPBTree mortality following drought in the central and southern Sierra Nevada, California, U.SFor. Ecol. Manag.201943216417810.1016/j.foreco.2018.09.006
Raffa, K. F., Grégoire, J.-C. & Staffan Lindgren, B. Natural History and Ecology of Bark Beetles 1–40 (Elsevier, 2015).
KaiserKEMcGlynnBLEmanuelREEcohydrology of an outbreak: mountain pine beetle impacts trees in drier landscape positions firstEcohydrology2013644445410.1002/eco.1286
Shiklomanov, A. N. et al. Enhancing global change experiments through integration of remote-sensing techniques. Front. Ecol. Environ. 17, 215–224 (2019).
JamesMRRobsonSMitigating systematic error in topographic models derived from UAV and ground-based image networksEarth Surf. Process. Landf.201439141314202014ESPL...39.1413J10.1002/esp.3609
VegaCPTrees: a point-based approach to forest tree extraction from LiDAR dataInt. J. Appl. Earth Observ. Geoinf.201433981082014IJAEO..33...98V
Thistle, H. W. et al. Surrogate pheromone plumes in three forest trunk spaces: composite statistics and case studies. For. Sci.50, 610–625 (2004).
GriffinDAnchukaitisKJHow unusual is the 2012-2014 California drought?Geophys. Res. Lett.201441901790232014GeoRL..41.9017G10.1002/2014GL062433
RestainoCForest structure and climate mediate drought-induced tree mortality in forests of the Sierra Nevada, USAEcol. Appl.20190e0190210.1002/eap.1902
GabryJSimpsonDVehtariABetancourtMGelmanAVisualization in Bayesian workflowJ. R. Stat. Soc. Ser. A2019182389402390266510.1111/rssa.12378
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
KleinWHParkerDLJensenCEAttack, emergence, and stand depletion trends of the mountain pine beetle in a lodgepole pine stand during an outbreakEnviron. Entomol.1978773273710.1093/ee/7.5.732
SeyboldSJManagement of western North American bark beetles with semiochemicalsAnnu. Rev. Entomol.2018634074321:CAS:528:DC%2BC2sXhs12rtbrL2905897710.1146/annurev-ento-020117-043339
Fettig, C. J. & Hilszczański, J. Bark Beetles 555–584. https://doi.org/10.1016/B978-0-12-417156-5.00014-9 (Elsevier, 2015).
StephensonNLDasAJHeight-related changes in forest composition explain increasing tree mortality with height during an extreme droughtNat. Commun.2020112020NatCo..11.3402S1:CAS:528:DC%2BB3cXhtlKgur3P32636488734176410.1038/s41467-020-17213-5
WeinsteinBGMarconiSBohlmanSZareAWhiteEIndividual tree-crown detection in RGB imagery using semi-supervised deep learning neural networksRemote Sens.20191113092019RemS...11.1309W10.3390/rs11111309
dos Santos, A. A. et al. Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs. Sensors (Basel)19, 3595 (2019).
Fettig, C. J. in Insects and Diseases of Mediterranean Forest Systems (eds Lieutier, F. & Paine, T. D.) 499–528 (Springer International Publishing, 2016).
BedardWDWestern pine beetle: field response to its sex pheromone and a synergistic host terpene, myrceneScience1969164128412851969Sci...164.1284B1:CAS:528:DyaF1MXksVOjtbg%3D1777256910.1126/science.164.3885.1284
LarsonAJChurchillDTree spatial patterns in fire-frequent forests of western North America, including mechanisms of pattern formation and implications for designing fuel reduction and restoration treatmentsFor. Ecol. Manag.2012267749210.1016/j.foreco.2011.11.038
ChubatyAMRoitbergBDLiCA dynamic host selection model for mountain pine beetle, Dendroctonus ponderosae HopkinsEcol. Model.20092201241125010.1016/j.ecolmodel.2009.01.039
HartSJVeblenTTSchneiderDMolotchNPSummer and winter drought drive the initiation and spread of spruce beetle outbreakEcology201798269827072875262310.1002/ecy.1963
Hunziker, P. Velox: Fast Raster Manipulation and Extraction (2017).
MitchellRGPreislerHKAnalysis of spatial patterns of lodgepole pine attacked by outbreak populations of the mountain pine beetleFor. Sci.19913713901408
GrayPCA convolutional neural network for detecting sea turtles in drone imageryMethods Ecol. Evol.20191034535510.1111/2041-210X.13132
Rouse, W., Haas, R. H., Deering, W. & Schell, J. A. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation (Remote Sensing Center, Texas A&M Univ., 1973).
WyngaardJEmergent challenges for science sUAS data management: fairness through community engagement and best practices developmentRemote Sens.20191117972019RemS...11.1797W10.3390/rs11151797
Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Drone-derived data supporting “Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality”. https://doi.org/10.17605/OSF.IO/3CWF9 (2020).
Berryman, A. A. in Bark Beetles in North American Conifers: A System for the Study of Evolutionary Biology 264–314 (University of Texas Press, 1982).
HayesCJFettigCJMerrillLDEvaluation of multiple funnel traps and stand characteristics for estimating western pine beetle-caused tree mortalityJ. Econ. Entomol.2009102217021822006984610.1603/029.102.0621
WaringRHPitmanGBModifying lodgepole pine stands to change susceptibility to mountain pine beetle attackEcology19856688989710.2307/1940551
FlintLEFlintALThorneJHBoyntonRFine-scale hydrologic modeling for regional landscape applications: The California Basin Characterization Model development and performanceEcol. Process.2013210.1186/2192-1709-2-25
DronesMadeEasy. Map Pilot for DJI on iOS. App Storehttps://itunes.apple.com/us/app/map-pilot-for-dji/id1014765000?mt=8 (2018).
FettigCJEfficacy of ‘Verbenone Plus’ for protecting ponderosa pine trees and stands from Dendroctonus brevicomis (Coleoptera: Curculionidae) attack in British Columbia and CaliforniaJ. Econ. Entomol.2012105166816802315616310.1603/EC12184
BürknerP-Cbrms: an R package for bayesian multilevel models using StanJ. Stat. Softw.20178012810.18637/jss.v080.i01
Fettig, C. J. in Managing Sierra Nevada Forests. PSW-GTR-237 Ch. 2 (USDA Forest Service, 2012).
JeronimoSMAForest structure and pattern vary by climate and landform across active-fire landscapes in the montane Sierra NevadaFor. Ecol. Manag.2019437708610.1016/j.foreco.2019.01.033
ByersJAWoodDLInterspecific inhibition of the response of the bark beetles, Dendroctonus brevicomis and Ips paraconfusus, to their pheromones in the fieldJ. Chem. Ecol.198061491641:CAS:528:DyaL3cXitFSitrk%3D10.1007/BF00987534
BrodrickPGAsnerGPRemotely sensed predictors of conifer tree mortality during severe droughtEnviron. Res. Lett.2017121150132017ERL....12k5013B10.1088/1748-9326/aa8f55
WallinKFRaffaKFFeedback between individual host selection behavior and population dynamics in an eruptive herbivoreEcol. Monogr.20047410111610.1890/02-4004
DJI. Zenmuse X3 - Creativity Unleashed. DJI Officialhttps://www.dji.com/zenmuse-x3/info (2015).
RaffaKFBerrymanAAThe role of host plant resistance in the colonization behavior and ecology of bark beetles (Coleoptera: Scolytidae)Ecol. Monogr.198353274910.2307/1942586
JactelHBrockerhoffEGTree diversity reduces herbivory by forest insectsEcol. Lett.2007108358481766371710.1111/j.1461-0248.2007.01073.x
DeRoseRJLongJNDrought-driven disturbance history characterizes a southern Rocky Mountain subalpine forestCan. J. Res.2012421649166010.1139/x2012-102
StephensonNActual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scalesJ. Biogeogr.19982585587010.1046/j.1365-2699.1998.00233.x
McDowellNMechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?N. Phytol.200817871973910.1111/j.1469-8137.2008.02436.x
MaSConcilioAOakleyBNorthMChenJSpatial variability in microclimate in a mixed-conifer forest before and after thinning and burning treatmentsFor. Ecol. Manag.201025990491510.1016/j.foreco.2009.11.030
WangYIs field-measured tree height as reliable as believed – a comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forestISPRS J. Photogramm. Remote Sens.20191471321452019JPRS..147..132W10.1016/j.isprsjprs.2018.11.008
ShepherdWPHuberDPWSeyboldSJFettigCJAntennal responses of the western pine beetle, Dendroctonus brevicomis (Coleoptera: Curculionidae), to stem volatiles of its primary host, Pinus ponderosa, and nine sympatric nonhost angiosperms and conifersChemoecology2007172092211:CAS:528:DC%2BD1cXhtVOrtbc%3D10.1007/s00049-007-0378-8
BaldwinBGSpecies richness and endemism in the native flora of CaliforniaAm. J. Bot.20171044875012834162810.3732/ajb.1600326
BrooksSPGelmanAGeneral methods for monitoring convergence of iterative simulationsJ. Comput. Graph. Stat.199874341665662
SambarajuKRCarrollALAukemaBHMultiyear weather anomalies associated with range shifts by the mountain pine beetle preceding large epidemicsFor. Ecol
AM Chubaty (20455_CR34) 2009; 220
KF Raffa (20455_CR19) 1983; 53
20455_CR79
Y Wang (20455_CR102) 2019; 147
20455_CR75
20455_CR115
20455_CR114
20455_CR117
20455_CR116
RJ DeRose (20455_CR25) 2012; 42
WD Bedard (20455_CR76) 1969; 164
ML Evenden (20455_CR45) 2014; 43
CJ Fettig (20455_CR57) 2008; 23
BJ Bentz (20455_CR24) 2010; 60
P Shin (20455_CR93) 2018; 10
J Gabry (20455_CR113) 2019; 182
20455_CR87
HL Person (20455_CR68) 1928; 26
20455_CR85
20455_CR84
20455_CR83
20455_CR81
F Meyer (20455_CR99) 1990; 1
CJ Fettig (20455_CR58) 2012; 105
20455_CR80
C Senf (20455_CR16) 2017; 32
KR Sambaraju (20455_CR30) 2019; 438
GA Fricker (20455_CR63) 2019; 434
CJ Fettig (20455_CR14) 2019; 432
KF Wallin (20455_CR21) 2004; 74
SM Robeson (20455_CR3) 2015; 42
NC Coops (20455_CR89) 2006; 103
JGPW Clevers (20455_CR90) 2013; 23
M Graf (20455_CR35) 2012; 144
HL Person (20455_CR69) 1931; 29
20455_CR59
CJ Fettig (20455_CR56) 2005; 98
PC Gray (20455_CR73) 2019; 10
KF Raffa (20455_CR46) 1982; 114
20455_CR53
20455_CR51
KF Raffa (20455_CR12) 2008; 58
NL Stephenson (20455_CR15) 2019; 75
MK Jakubowski (20455_CR92) 2013; 5
MD Hoffman (20455_CR110) 2014; 15
20455_CR1
20455_CR6
AJ Larson (20455_CR49) 2012; 267
KE Kaiser (20455_CR28) 2013; 6
C Restaino (20455_CR9) 2019; 0
JA Byers (20455_CR77) 1980; 6
20455_CR61
PG Brodrick (20455_CR5) 2017; 12
RH Waring (20455_CR8) 1985; 66
P Chesson (20455_CR62) 2000; 31
VR Kane (20455_CR48) 2014; 151
CI Millar (20455_CR74) 2007; 17
AEL Stovall (20455_CR67) 2020; 11
P-C Bürkner (20455_CR109) 2017; 80
JL Morris (20455_CR50) 2017; 54
G Pau (20455_CR98) 2010; 26
20455_CR36
20455_CR33
20455_CR32
GP Asner (20455_CR4) 2016; 113
CJ Hayes (20455_CR31) 2009; 102
M Kuhn (20455_CR101) 2008; 28
VR Franceschi (20455_CR22) 2005; 167
RG Mitchell (20455_CR38) 1991; 37
M Faccoli (20455_CR41) 2014; 5
TE Kolb (20455_CR7) 2016; 380
JA Logan (20455_CR20) 1998; 53
C Vega (20455_CR96) 2014; 33
N Stephenson (20455_CR105) 1998; 25
WH Klein (20455_CR37) 1978; 7
20455_CR42
HK Preisler (20455_CR39) 1993; 42
SMA Jeronimo (20455_CR52) 2019; 437
BG Weinstein (20455_CR103) 2019; 11
N McDowell (20455_CR54) 2008; 178
CI Millar (20455_CR107) 2012; 42
20455_CR18
SJ Seybold (20455_CR55) 2018; 63
MR James (20455_CR72) 2014; 39
C Fettig (20455_CR60) 2014; 5
20455_CR10
LS Pile (20455_CR70) 2019; 10
WP Shepherd (20455_CR78) 2007; 17
20455_CR97
R Seidl (20455_CR17) 2016; 53
20455_CR94
DJN Young (20455_CR11) 2017; 20
NL Stephenson (20455_CR66) 2020; 11
SP Brooks (20455_CR112) 1998; 7
A Gitelson (20455_CR88) 1994; 143
D Griffin (20455_CR2) 2014; 41
L Marini (20455_CR29) 2017; 40
20455_CR27
WRL Anderegg (20455_CR47) 2015; 208
B Carpenter (20455_CR111) 2017; 76
HA Moeck (20455_CR44) 1981; 7
S Ma (20455_CR64) 2010; 259
W Zhang (20455_CR86) 2016; 8
BG Baldwin (20455_CR108) 2017; 104
20455_CR23
J Wyngaard (20455_CR82) 2019; 11
SJ Hart (20455_CR26) 2017; 98
20455_CR100
W Li (20455_CR91) 2012; 78
20455_CR104
CK Boone (20455_CR13) 2011; 41
AEL Stovall (20455_CR65) 2019; 10
J Frey (20455_CR71) 2018; 10
CJ Fettig (20455_CR43) 2007; 238
L Eysn (20455_CR95) 2015; 6
LE Flint (20455_CR106) 2013; 2
H Jactel (20455_CR40) 2007; 10
References_xml – reference: FrickerGAMore than climate? Predictors of tree canopy height vary with scale in complex terrain, Sierra Nevada, CA (USA)For. Ecol. Manag.201943414215310.1016/j.foreco.2018.12.006
– reference: MaSConcilioAOakleyBNorthMChenJSpatial variability in microclimate in a mixed-conifer forest before and after thinning and burning treatmentsFor. Ecol. Manag.201025990491510.1016/j.foreco.2009.11.030
– reference: RaffaKFBerrymanAAThe role of host plant resistance in the colonization behavior and ecology of bark beetles (Coleoptera: Scolytidae)Ecol. Monogr.198353274910.2307/1942586
– reference: SambarajuKRCarrollALAukemaBHMultiyear weather anomalies associated with range shifts by the mountain pine beetle preceding large epidemicsFor. Ecol. Manag.2019438869510.1016/j.foreco.2019.02.011
– reference: MoeckHAWoodDLLindahlKQHost selection behavior of bark beetles (Coleoptera: Scolytidae) attacking Pinus ponderosa, with special emphasis on the western pine beetle, Dendroctonus brevicomisJ. Chem. Ecol.1981749831:STN:280:DC%2BC2czltVSnuw%3D%3D2442042710.1007/BF00988635
– reference: GitelsonAMerzlyakMNSpectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimationJ. Plant Physiol.19941432862921:CAS:528:DyaK2cXisVSgs7g%3D10.1016/S0176-1617(11)81633-0
– reference: Berryman, A. A. in Bark Beetles in North American Conifers: A System for the Study of Evolutionary Biology 264–314 (University of Texas Press, 1982).
– reference: JeronimoSMAForest structure and pattern vary by climate and landform across active-fire landscapes in the montane Sierra NevadaFor. Ecol. Manag.2019437708610.1016/j.foreco.2019.01.033
– reference: Roussel, J.-R. lidRplugins: Extra Functions and Algorithms for lidR Package (2019).
– reference: HartSJVeblenTTSchneiderDMolotchNPSummer and winter drought drive the initiation and spread of spruce beetle outbreakEcology201798269827072875262310.1002/ecy.1963
– reference: KaneVRAssessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National ParkRemote Sens. Environ.2014151891012014RSEnv.151...89K10.1016/j.rse.2013.07.041
– reference: Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys.45, RG2004 (2007).
– reference: Micasense. MicaSense. https://support.micasense.com/hc/en-us/articles/215261448-RedEdge-User-Manual-PDF-Download- (2015).
– reference: WaringRHPitmanGBModifying lodgepole pine stands to change susceptibility to mountain pine beetle attackEcology19856688989710.2307/1940551
– reference: StephensonNLDasAJHeight-related changes in forest composition explain increasing tree mortality with height during an extreme droughtNat. Commun.2020112020NatCo..11.3402S1:CAS:528:DC%2BB3cXhtlKgur3P32636488734176410.1038/s41467-020-17213-5
– reference: FettigCJMcKelveySRHuberDPWNonhost angiosperm volatiles and verbenone disrupt response of western pine beetle, Dendroctonus brevicomis (Coleoptera: Scolytidae), to attractant-baited trapsJ. Econ. Entomol.200598204120481:CAS:528:DC%2BD2sXpslyit7k%3D1653913110.1093/jee/98.6.2041
– reference: Miller, J. M. & Keen, F. P. Biology and Control of the Western Pine Beetle: A Summary of The First Fifty Years of Research (US Department of Agriculture, 1960).
– reference: BrooksSPGelmanAGeneral methods for monitoring convergence of iterative simulationsJ. Comput. Graph. Stat.199874341665662
– reference: StephensonNActual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scalesJ. Biogeogr.19982585587010.1046/j.1365-2699.1998.00233.x
– reference: Fettig, C. J. in Managing Sierra Nevada Forests. PSW-GTR-237 Ch. 2 (USDA Forest Service, 2012).
– reference: Vose, J. M. et al. in Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment Vol II (eds Reidmiller, D. R., et al.) 232–267. https://nca2018.globalchange.gov/chapter/6/https://doi.org/10.7930/NCA4.2018.CH6 (2018).
– reference: JakubowskiMKLiWGuoQKellyMDelineating individual trees from LiDAR data: a comparison of vector- and raster-based segmentation approachesRemote Sens.20135416341862013RemS....5.4163J10.3390/rs5094163
– reference: dos Santos, A. A. et al. Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs. Sensors (Basel)19, 3595 (2019).
– reference: USDAFS. Press Release: Survey Finds 18 Million Trees Died in California in 2018. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/FSEPRD609321.pdf (USDAFS, 2019).
– reference: YoungDJNLong-term climate and competition explain forest mortality patterns under extreme droughtEcol. Lett.20172078862800043210.1111/ele.12711
– reference: FaccoliMBernardinelliIComposition and elevation of spruce forests affect susceptibility to bark beetle attacks: implications for forest managementForests201458810210.3390/f5010088
– reference: PileLSMeyerMDRojasRRoeOSmithMTDrought impacts and compounding mortality on forest trees in the southern Sierra NevadaForests20191023710.3390/f10030237
– reference: Fettig, C. J. & Hilszczański, J. Bark Beetles 555–584. https://doi.org/10.1016/B978-0-12-417156-5.00014-9 (Elsevier, 2015).
– reference: GriffinDAnchukaitisKJHow unusual is the 2012-2014 California drought?Geophys. Res. Lett.201441901790232014GeoRL..41.9017G10.1002/2014GL062433
– reference: FettigCJDabneyCPMcKelveySRHuberDPWNonhost angiosperm volatiles and verbenone protect individual ponderosa pines from attack by western pine beetle and red turpentine beetle (Coleoptera: Curculionidae, Scolytinae)West J. Appl.2008234045
– reference: DJI. DJI - The World Leader in Camera Drones/Quadcopters for Aerial Photography. DJI Officialhttps://www.dji.com/matrice100/info (2015).
– reference: WangYIs field-measured tree height as reliable as believed – a comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forestISPRS J. Photogramm. Remote Sens.20191471321452019JPRS..147..132W10.1016/j.isprsjprs.2018.11.008
– reference: PersonHLTheory in explanation of the selection of certain trees by the western pine beetleJ. For.1931296966991:CAS:528:DyaA38XktVam
– reference: WyngaardJEmergent challenges for science sUAS data management: fairness through community engagement and best practices developmentRemote Sens.20191117972019RemS...11.1797W10.3390/rs11151797
– reference: Hijmans, R. J. et al. Raster: Geographic Data Analysis and Modeling (2019).
– reference: BentzBJClimate change and bark beetles of the western United States and Canada: direct and indirect effectsBioScience20106060261310.1525/bio.2010.60.8.6
– reference: CarpenterBStan: a probabilistic programming languageJ. Stat. Softw.20177613210.18637/jss.v076.i01
– reference: LarsonAJChurchillDTree spatial patterns in fire-frequent forests of western North America, including mechanisms of pattern formation and implications for designing fuel reduction and restoration treatmentsFor. Ecol. Manag.2012267749210.1016/j.foreco.2011.11.038
– reference: USDAFS. Press Release: Record 129 million dead trees in California. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fseprd566303.pdf (USDAFS, 2017).
– reference: Plowright, A. ForestTools: Analyzing Remotely Sensed Forest Data (2018).
– reference: Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Local-structure-wpb-severity. https://doi.org/10.17605/OSF.IO/WPK5Z (2019).
– reference: Fettig, C. J. in Insects and Diseases of Mediterranean Forest Systems (eds Lieutier, F. & Paine, T. D.) 499–528 (Springer International Publishing, 2016).
– reference: AsnerGPProgressive forest canopy water loss during the 2012-2015 California droughtProc. Natl Acad. Sci. USA2016113E249E2551:CAS:528:DC%2BC2MXitV2isLzI2671202010.1073/pnas.1523397113
– reference: Hunziker, P. Velox: Fast Raster Manipulation and Extraction (2017).
– reference: EysnLA benchmark of LiDAR-based single tree detection methods using heterogeneous forest data from the alpine spaceForests201561721174710.3390/f6051721
– reference: DeRoseRJLongJNDrought-driven disturbance history characterizes a southern Rocky Mountain subalpine forestCan. J. Res.2012421649166010.1139/x2012-102
– reference: StovallAELShugartHHYangXReply to ‘Height-related changes in forest composition explain increasing tree mortality with height during an extreme drought’Nat. Commun.2020112020NatCo..11.3401S1:CAS:528:DC%2BB3cXhtlKgurzN32636374734079010.1038/s41467-020-17214-4
– reference: RestainoCForest structure and climate mediate drought-induced tree mortality in forests of the Sierra Nevada, USAEcol. Appl.20190e0190210.1002/eap.1902
– reference: KolbTEObserved and anticipated impacts of drought on forest insects and diseases in the United StatesFor. Ecol. Manag.201638032133410.1016/j.foreco.2016.04.051
– reference: AndereggWRLTree mortality from drought, insects, and their interactions in a changing climateN. Phytol.201520867468310.1111/nph.13477
– reference: RobesonSMRevisiting the recent California drought as an extreme valueGeophys. Res. Lett.201542677167792015GeoRL..42.6771R10.1002/2015GL064593
– reference: MorrisJLManaging bark beetle impacts on ecosystems and society: Priority questions to motivate future researchJ. Appl. Ecol.20175475076010.1111/1365-2664.12782
– reference: KaiserKEMcGlynnBLEmanuelREEcohydrology of an outbreak: mountain pine beetle impacts trees in drier landscape positions firstEcohydrology2013644445410.1002/eco.1286
– reference: SenfCCampbellEMPflugmacherDWulderMAHostertPA multi-scale analysis of western spruce budworm outbreak dynamicsLandsc. Ecol.20173250151410.1007/s10980-016-0460-0
– reference: ShepherdWPHuberDPWSeyboldSJFettigCJAntennal responses of the western pine beetle, Dendroctonus brevicomis (Coleoptera: Curculionidae), to stem volatiles of its primary host, Pinus ponderosa, and nine sympatric nonhost angiosperms and conifersChemoecology2007172092211:CAS:528:DC%2BD1cXhtVOrtbc%3D10.1007/s00049-007-0378-8
– reference: CleversJGPWGitelsonAARemote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3Int. J. Appl. Earth Observ. Geoinf.2013233443512013IJAEO..23..344C
– reference: FettigCJThe effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United StatesFor. Ecol. Manag.2007238245310.1016/j.foreco.2006.10.011
– reference: Rouse, W., Haas, R. H., Deering, W. & Schell, J. A. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation (Remote Sensing Center, Texas A&M Univ., 1973).
– reference: Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Drone-derived data supporting “Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality”. https://doi.org/10.17605/OSF.IO/3CWF9 (2020).
– reference: WallinKFRaffaKFFeedback between individual host selection behavior and population dynamics in an eruptive herbivoreEcol. Monogr.20047410111610.1890/02-4004
– reference: FranceschiVRKrokenePChristiansenEKreklingTAnatomical and chemical defenses of conifer bark against bark beetles and other pestsN. Phytol.20051673533761:CAS:528:DC%2BD2MXpt1ersL8%3D10.1111/j.1469-8137.2005.01436.x
– reference: BedardWDWestern pine beetle: field response to its sex pheromone and a synergistic host terpene, myrceneScience1969164128412851969Sci...164.1284B1:CAS:528:DyaF1MXksVOjtbg%3D1777256910.1126/science.164.3885.1284
– reference: StovallAELShugartHYangXTree height explains mortality risk during an intense droughtNat. Commun.2019101610.1038/s41467-019-12380-61:CAS:528:DC%2BC1MXhvVOltrfP
– reference: Roussel, J.-R., Auty, D., De Boissieu, F. & Meador, A. S. lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications (2019).
– reference: BooneCKAukemaBHBohlmannJCarrollALRaffaKFEfficacy of tree defense physiology varies with bark beetle population density: a basis for positive feedback in eruptive speciesCan. J. Res.2011411174118810.1139/x11-041
– reference: Oliver, W. W. Is self-thinning in ponderosa pine ruled by Dendroctonus bark beetles? In Forest Health Through Silviculture: Proceedings of the 1995 National Silviculture Workshop 6 (1995).
– reference: VegaCPTrees: a point-based approach to forest tree extraction from LiDAR dataInt. J. Appl. Earth Observ. Geoinf.201433981082014IJAEO..33...98V
– reference: KuhnMBuilding predictive models in R using the caret packageJ. Stat. Softw.20082812610.18637/jss.v028.i05
– reference: WeinsteinBGMarconiSBohlmanSZareAWhiteEIndividual tree-crown detection in RGB imagery using semi-supervised deep learning neural networksRemote Sens.20191113092019RemS...11.1309W10.3390/rs11111309
– reference: McDowellNMechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?N. Phytol.200817871973910.1111/j.1469-8137.2008.02436.x
– reference: MeyerFBeucherSMorphological segmentationJ. Vis. Commun. Image Represent.19901214610.1016/1047-3203(90)90014-M
– reference: Baldwin, B. G. et al. Master spatial file for native California vascular plants used by Baldwin et al. (2017 Amer. J. Bot.), Dryad, Dataset, 2017. https://doi.org/10.6078/D16K5W.
– reference: Netherer, S., Panassiti, B., Pennerstorfer, J. & Matthews, B. Acute drought Is an important driver of bark beetle infestation in Austrian Norway spruce stands. Front. For. Glob. Change2, 39 (2019).
– reference: GrafMReidMLAukemaBHLindgrenBSAssociation of tree diameter with body size and lipid content of mountain pine beetlesCan. Entomol.201214446747710.4039/tce.2012.38
– reference: ChubatyAMRoitbergBDLiCA dynamic host selection model for mountain pine beetle, Dendroctonus ponderosae HopkinsEcol. Model.20092201241125010.1016/j.ecolmodel.2009.01.039
– reference: FettigCJMortensonLABulaonBMFoulkPBTree mortality following drought in the central and southern Sierra Nevada, California, U.SFor. Ecol. Manag.201943216417810.1016/j.foreco.2018.09.006
– reference: RaffaKFBerrymanAAAccumulation of monoterpenes and associated volatiles following inoculation of grand fir with a fungus transmitted by the fir engraver, Scolytus ventralis (Coleoptera: Scolytidae)Can. Entomol.19821147978101:CAS:528:DyaL38XlsF2ks7g%3D10.4039/Ent114797-9
– reference: JamesMRRobsonSMitigating systematic error in topographic models derived from UAV and ground-based image networksEarth Surf. Process. Landf.201439141314202014ESPL...39.1413J10.1002/esp.3609
– reference: FettigCMcKelveySResiliency of an interior ponderosa pine forest to bark beetle infestations following fuel-reduction and forest-restoration treatmentsForests2014515317610.3390/f5010153
– reference: MillarCIStephensonNLStephensSLClimate change and forests of the future: Managing in the face of uncertaintyEcol. Appl.200717214521511821395810.1890/06-1715.1
– reference: FettigCJEfficacy of ‘Verbenone Plus’ for protecting ponderosa pine trees and stands from Dendroctonus brevicomis (Coleoptera: Curculionidae) attack in British Columbia and CaliforniaJ. Econ. Entomol.2012105166816802315616310.1603/EC12184
– reference: DJI. Zenmuse X3 - Creativity Unleashed. DJI Officialhttps://www.dji.com/zenmuse-x3/info (2015).
– reference: GrayPCA convolutional neural network for detecting sea turtles in drone imageryMethods Ecol. Evol.20191034535510.1111/2041-210X.13132
– reference: GabryJSimpsonDVehtariABetancourtMGelmanAVisualization in Bayesian workflowJ. R. Stat. Soc. Ser. A2019182389402390266510.1111/rssa.12378
– reference: FlintLEFlintALThorneJHBoyntonRFine-scale hydrologic modeling for regional landscape applications: The California Basin Characterization Model development and performanceEcol. Process.2013210.1186/2192-1709-2-25
– reference: CoopsNCJohnsonMWulderMAWhiteJCAssessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestationRemote Sens. Environ.200610367802006RSEnv.103...67C10.1016/j.rse.2006.03.012
– reference: BürknerP-Cbrms: an R package for bayesian multilevel models using StanJ. Stat. Softw.20178012810.18637/jss.v080.i01
– reference: ShinPSankeyTMooreMThodeAEvaluating unmanned aerial vehicle images for estimating forest canopy fuels in a ponderosa pine standRemote Sens.20181012662018RemS...10.1266S10.3390/rs10081266
– reference: KleinWHParkerDLJensenCEAttack, emergence, and stand depletion trends of the mountain pine beetle in a lodgepole pine stand during an outbreakEnviron. Entomol.1978773273710.1093/ee/7.5.732
– reference: BaldwinBGSpecies richness and endemism in the native flora of CaliforniaAm. J. Bot.20171044875012834162810.3732/ajb.1600326
– reference: MariniLClimate drivers of bark beetle outbreak dynamics in Norway spruce forestsEcography2017401426143510.1111/ecog.02769
– reference: DronesMadeEasy. Map Pilot for DJI on iOS. App Storehttps://itunes.apple.com/us/app/map-pilot-for-dji/id1014765000?mt=8 (2018).
– reference: LiWGuoQJakubowskiMKKellyMA new method for segmenting individual trees from the LiDAR point cloudPhotogramm. Eng. Remote Sens.201278758410.14358/PERS.78.1.75
– reference: LoganJAWhitePBentzBJPowellJAModel analysis of spatial patterns in mountain pine beetle outbreaksTheor. Popul. Biol.1998532362551:STN:280:DC%2BC2sjnvFeluw%3D%3D96820260941.9203010.1006/tpbi.1997.1350
– reference: MitchellRGPreislerHKAnalysis of spatial patterns of lodgepole pine attacked by outbreak populations of the mountain pine beetleFor. Sci.19913713901408
– reference: ZhangWAn easy-to-use airborne LiDAR data filtering method based on cloth simulationRemote Sens.201685012016RemS....8..501Z10.3390/rs8060501
– reference: JactelHBrockerhoffEGTree diversity reduces herbivory by forest insectsEcol. Lett.2007108358481766371710.1111/j.1461-0248.2007.01073.x
– reference: Raffa, K. F., Grégoire, J.-C. & Staffan Lindgren, B. Natural History and Ecology of Bark Beetles 1–40 (Elsevier, 2015).
– reference: SeyboldSJManagement of western North American bark beetles with semiochemicalsAnnu. Rev. Entomol.2018634074321:CAS:528:DC%2BC2sXhs12rtbrL2905897710.1146/annurev-ento-020117-043339
– reference: RaffaKFCross-scale drivers of natural disturbances prone to anthropogenic amplification: The dynamics of bark beetle eruptionsBioScience20085850151710.1641/B580607
– reference: HoffmanMDGelmanAThe no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloJ. Mach. Learn. Res.2014153132147791319.60150
– reference: Geiszler, D. R. & Gara, R. I. in Theory and Practice of Mountain Pine Beetle Management in Lodgepole Pine Forests: Symposium Proceedings (eds Berryman, A. A., Amman, G. D. & Stark, R. W.) (1978).
– reference: EvendenMLWhitehouseCMSykesJFactors influencing flight capacity of the mountain pine beetle (Coleoptera: Curculionidae: Scolytinae)Environ. Entomol.2014431871961:STN:280:DC%2BC2c3pvFGhtg%3D%3D2436793010.1603/EN13244
– reference: SeidlRSmall beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetleJ. Appl Ecol.20165353054010.1111/1365-2664.12540
– reference: ByersJAWoodDLInterspecific inhibition of the response of the bark beetles, Dendroctonus brevicomis and Ips paraconfusus, to their pheromones in the fieldJ. Chem. Ecol.198061491641:CAS:528:DyaL3cXitFSitrk%3D10.1007/BF00987534
– reference: StephensonNLDasAJAmperseeNJBulaonBMWhich trees die during drought? The key role of insect host-tree selectionJ. Ecol.2019752383240110.1111/1365-2745.13176
– reference: ChessonPMechanisms of maintenance of species diversityAnnu. Rev. Ecol. Syst.20003134336610.1146/annurev.ecolsys.31.1.343
– reference: FreyJKovachKStemmlerSKochBUAV photogrammetry of forests as a vulnerable process. A sensitivity analysis for a structure from motion RGB-image pipelineRemote Sens.2018109122018RemS...10..912F10.3390/rs10060912
– reference: MillarCIForest mortality in high-elevation whitebark pine (Pinus albicaulis) forests of eastern California, USA: influence of environmental context, bark beetles, climatic water deficit, and warmingCan. J. For. Res.20124274976510.1139/x2012-031
– reference: Thistle, H. W. et al. Surrogate pheromone plumes in three forest trunk spaces: composite statistics and case studies. For. Sci.50, 610–625 (2004).
– reference: PreislerHKModelling spatial patterns of trees attacked by bark-beetlesAppl. Stat.1993425010825.6288810.2307/2986328
– reference: Shiklomanov, A. N. et al. Enhancing global change experiments through integration of remote-sensing techniques. Front. Ecol. Environ. 17, 215–224 (2019).
– reference: PersonHLTree selection by the western pine beetleJ. For.192826564578
– reference: PauGFuchsFSklyarOBoutrosMHuberWEBImage: An R package for image processing with applications to cellular phenotypesBioinformatics2010269799811:CAS:528:DC%2BC3cXjvFykur0%3D20338898284498810.1093/bioinformatics/btq046
– reference: BrodrickPGAsnerGPRemotely sensed predictors of conifer tree mortality during severe droughtEnviron. Res. Lett.2017121150132017ERL....12k5013B10.1088/1748-9326/aa8f55
– reference: HayesCJFettigCJMerrillLDEvaluation of multiple funnel traps and stand characteristics for estimating western pine beetle-caused tree mortalityJ. Econ. Entomol.2009102217021822006984610.1603/029.102.0621
– reference: R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
– volume: 60
  start-page: 602
  year: 2010
  ident: 20455_CR24
  publication-title: BioScience
  doi: 10.1525/bio.2010.60.8.6
– ident: 20455_CR59
– volume: 33
  start-page: 98
  year: 2014
  ident: 20455_CR96
  publication-title: Int. J. Appl. Earth Observ. Geoinf.
– ident: 20455_CR36
– volume: 63
  start-page: 407
  year: 2018
  ident: 20455_CR55
  publication-title: Annu. Rev. Entomol.
  doi: 10.1146/annurev-ento-020117-043339
– ident: 20455_CR114
  doi: 10.17605/OSF.IO/3CWF9
– volume: 12
  start-page: 115013
  year: 2017
  ident: 20455_CR5
  publication-title: Environ. Res. Lett.
  doi: 10.1088/1748-9326/aa8f55
– volume: 98
  start-page: 2698
  year: 2017
  ident: 20455_CR26
  publication-title: Ecology
  doi: 10.1002/ecy.1963
– ident: 20455_CR83
– volume: 0
  start-page: e01902
  year: 2019
  ident: 20455_CR9
  publication-title: Ecol. Appl.
  doi: 10.1002/eap.1902
– volume: 114
  start-page: 797
  year: 1982
  ident: 20455_CR46
  publication-title: Can. Entomol.
  doi: 10.4039/Ent114797-9
– volume: 58
  start-page: 501
  year: 2008
  ident: 20455_CR12
  publication-title: BioScience
  doi: 10.1641/B580607
– ident: 20455_CR79
– volume: 54
  start-page: 750
  year: 2017
  ident: 20455_CR50
  publication-title: J. Appl. Ecol.
  doi: 10.1111/1365-2664.12782
– ident: 20455_CR97
  doi: 10.32614/CRAN.package.ForestTools
– volume: 31
  start-page: 343
  year: 2000
  ident: 20455_CR62
  publication-title: Annu. Rev. Ecol. Syst.
  doi: 10.1146/annurev.ecolsys.31.1.343
– volume: 143
  start-page: 286
  year: 1994
  ident: 20455_CR88
  publication-title: J. Plant Physiol.
  doi: 10.1016/S0176-1617(11)81633-0
– ident: 20455_CR33
– volume: 28
  start-page: 1
  year: 2008
  ident: 20455_CR101
  publication-title: J. Stat. Softw.
  doi: 10.18637/jss.v028.i05
– volume: 178
  start-page: 719
  year: 2008
  ident: 20455_CR54
  publication-title: N. Phytol.
  doi: 10.1111/j.1469-8137.2008.02436.x
– ident: 20455_CR1
– volume: 238
  start-page: 24
  year: 2007
  ident: 20455_CR43
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2006.10.011
– volume: 10
  start-page: 1
  year: 2019
  ident: 20455_CR65
  publication-title: Nat. Commun.
  doi: 10.1038/s41467-019-12380-6
– volume: 74
  start-page: 101
  year: 2004
  ident: 20455_CR21
  publication-title: Ecol. Monogr.
  doi: 10.1890/02-4004
– volume: 267
  start-page: 74
  year: 2012
  ident: 20455_CR49
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2011.11.038
– volume: 2
  year: 2013
  ident: 20455_CR106
  publication-title: Ecol. Process.
  doi: 10.1186/2192-1709-2-25
– volume: 98
  start-page: 2041
  year: 2005
  ident: 20455_CR56
  publication-title: J. Econ. Entomol.
  doi: 10.1093/jee/98.6.2041
– volume: 6
  start-page: 444
  year: 2013
  ident: 20455_CR28
  publication-title: Ecohydrology
  doi: 10.1002/eco.1286
– volume: 15
  start-page: 31
  year: 2014
  ident: 20455_CR110
  publication-title: J. Mach. Learn. Res.
– volume: 11
  start-page: 1309
  year: 2019
  ident: 20455_CR103
  publication-title: Remote Sens.
  doi: 10.3390/rs11111309
– volume: 144
  start-page: 467
  year: 2012
  ident: 20455_CR35
  publication-title: Can. Entomol.
  doi: 10.4039/tce.2012.38
– volume: 76
  start-page: 1
  year: 2017
  ident: 20455_CR111
  publication-title: J. Stat. Softw.
  doi: 10.18637/jss.v076.i01
– volume: 80
  start-page: 1
  year: 2017
  ident: 20455_CR109
  publication-title: J. Stat. Softw.
  doi: 10.18637/jss.v080.i01
– ident: 20455_CR85
  doi: 10.1029/2005RG000183
– volume: 32
  start-page: 501
  year: 2017
  ident: 20455_CR16
  publication-title: Landsc. Ecol.
  doi: 10.1007/s10980-016-0460-0
– ident: 20455_CR51
  doi: 10.1002/fee.2031
– volume: 75
  start-page: 2383
  year: 2019
  ident: 20455_CR15
  publication-title: J. Ecol.
  doi: 10.1111/1365-2745.13176
– volume: 10
  start-page: 345
  year: 2019
  ident: 20455_CR73
  publication-title: Methods Ecol. Evol.
  doi: 10.1111/2041-210X.13132
– volume: 41
  start-page: 1174
  year: 2011
  ident: 20455_CR13
  publication-title: Can. J. Res.
  doi: 10.1139/x11-041
– volume: 220
  start-page: 1241
  year: 2009
  ident: 20455_CR34
  publication-title: Ecol. Model.
  doi: 10.1016/j.ecolmodel.2009.01.039
– volume: 164
  start-page: 1284
  year: 1969
  ident: 20455_CR76
  publication-title: Science
  doi: 10.1126/science.164.3885.1284
– volume: 105
  start-page: 1668
  year: 2012
  ident: 20455_CR58
  publication-title: J. Econ. Entomol.
  doi: 10.1603/EC12184
– volume: 37
  start-page: 1390
  year: 1991
  ident: 20455_CR38
  publication-title: For. Sci.
– volume: 23
  start-page: 344
  year: 2013
  ident: 20455_CR90
  publication-title: Int. J. Appl. Earth Observ. Geoinf.
– volume: 20
  start-page: 78
  year: 2017
  ident: 20455_CR11
  publication-title: Ecol. Lett.
  doi: 10.1111/ele.12711
– volume: 42
  start-page: 1649
  year: 2012
  ident: 20455_CR25
  publication-title: Can. J. Res.
  doi: 10.1139/x2012-102
– volume: 147
  start-page: 132
  year: 2019
  ident: 20455_CR102
  publication-title: ISPRS J. Photogramm. Remote Sens.
  doi: 10.1016/j.isprsjprs.2018.11.008
– ident: 20455_CR10
– volume: 17
  start-page: 2145
  year: 2007
  ident: 20455_CR74
  publication-title: Ecol. Appl.
  doi: 10.1890/06-1715.1
– ident: 20455_CR94
– ident: 20455_CR23
  doi: 10.1016/B978-0-12-417156-5.00001-0
– volume: 432
  start-page: 164
  year: 2019
  ident: 20455_CR14
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2018.09.006
– volume: 7
  start-page: 732
  year: 1978
  ident: 20455_CR37
  publication-title: Environ. Entomol.
  doi: 10.1093/ee/7.5.732
– volume: 42
  start-page: 6771
  year: 2015
  ident: 20455_CR3
  publication-title: Geophys. Res. Lett.
  doi: 10.1002/2015GL064593
– volume: 151
  start-page: 89
  year: 2014
  ident: 20455_CR48
  publication-title: Remote Sens. Environ.
  doi: 10.1016/j.rse.2013.07.041
– volume: 17
  start-page: 209
  year: 2007
  ident: 20455_CR78
  publication-title: Chemoecology
  doi: 10.1007/s00049-007-0378-8
– volume: 11
  year: 2020
  ident: 20455_CR66
  publication-title: Nat. Commun.
  doi: 10.1038/s41467-020-17213-5
– ident: 20455_CR80
– volume: 10
  start-page: 835
  year: 2007
  ident: 20455_CR40
  publication-title: Ecol. Lett.
  doi: 10.1111/j.1461-0248.2007.01073.x
– volume: 10
  start-page: 912
  year: 2018
  ident: 20455_CR71
  publication-title: Remote Sens.
  doi: 10.3390/rs10060912
– volume: 25
  start-page: 855
  year: 1998
  ident: 20455_CR105
  publication-title: J. Biogeogr.
  doi: 10.1046/j.1365-2699.1998.00233.x
– volume: 40
  start-page: 1426
  year: 2017
  ident: 20455_CR29
  publication-title: Ecography
  doi: 10.1111/ecog.02769
– volume: 26
  start-page: 564
  year: 1928
  ident: 20455_CR68
  publication-title: J. For.
– ident: 20455_CR32
– ident: 20455_CR104
  doi: 10.3390/s19163595
– ident: 20455_CR116
– volume: 5
  start-page: 4163
  year: 2013
  ident: 20455_CR92
  publication-title: Remote Sens.
  doi: 10.3390/rs5094163
– ident: 20455_CR18
  doi: 10.1007/978-3-319-24744-1_18
– ident: 20455_CR87
– volume: 53
  start-page: 27
  year: 1983
  ident: 20455_CR19
  publication-title: Ecol. Monogr.
  doi: 10.2307/1942586
– ident: 20455_CR75
  doi: 10.7930/NCA4.2018.CH6
– volume: 380
  start-page: 321
  year: 2016
  ident: 20455_CR7
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2016.04.051
– volume: 5
  start-page: 153
  year: 2014
  ident: 20455_CR60
  publication-title: Forests
  doi: 10.3390/f5010153
– volume: 53
  start-page: 236
  year: 1998
  ident: 20455_CR20
  publication-title: Theor. Popul. Biol.
  doi: 10.1006/tpbi.1997.1350
– volume: 8
  start-page: 501
  year: 2016
  ident: 20455_CR86
  publication-title: Remote Sens.
  doi: 10.3390/rs8060501
– volume: 53
  start-page: 530
  year: 2016
  ident: 20455_CR17
  publication-title: J. Appl Ecol.
  doi: 10.1111/1365-2664.12540
– volume: 437
  start-page: 70
  year: 2019
  ident: 20455_CR52
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2019.01.033
– volume: 26
  start-page: 979
  year: 2010
  ident: 20455_CR98
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btq046
– volume: 66
  start-page: 889
  year: 1985
  ident: 20455_CR8
  publication-title: Ecology
  doi: 10.2307/1940551
– volume: 39
  start-page: 1413
  year: 2014
  ident: 20455_CR72
  publication-title: Earth Surf. Process. Landf.
  doi: 10.1002/esp.3609
– volume: 11
  year: 2020
  ident: 20455_CR67
  publication-title: Nat. Commun.
  doi: 10.1038/s41467-020-17214-4
– volume: 6
  start-page: 149
  year: 1980
  ident: 20455_CR77
  publication-title: J. Chem. Ecol.
  doi: 10.1007/BF00987534
– volume: 11
  start-page: 1797
  year: 2019
  ident: 20455_CR82
  publication-title: Remote Sens.
  doi: 10.3390/rs11151797
– volume: 208
  start-page: 674
  year: 2015
  ident: 20455_CR47
  publication-title: N. Phytol.
  doi: 10.1111/nph.13477
– volume: 41
  start-page: 9017
  year: 2014
  ident: 20455_CR2
  publication-title: Geophys. Res. Lett.
  doi: 10.1002/2014GL062433
– volume: 167
  start-page: 353
  year: 2005
  ident: 20455_CR22
  publication-title: N. Phytol.
  doi: 10.1111/j.1469-8137.2005.01436.x
– volume: 43
  start-page: 187
  year: 2014
  ident: 20455_CR45
  publication-title: Environ. Entomol.
  doi: 10.1603/EN13244
– ident: 20455_CR53
– volume: 182
  start-page: 389
  year: 2019
  ident: 20455_CR113
  publication-title: J. R. Stat. Soc. Ser. A
  doi: 10.1111/rssa.12378
– ident: 20455_CR27
  doi: 10.3389/ffgc.2019.00039
– volume: 113
  start-page: E249
  year: 2016
  ident: 20455_CR4
  publication-title: Proc. Natl Acad. Sci. USA
  doi: 10.1073/pnas.1523397113
– volume: 78
  start-page: 75
  year: 2012
  ident: 20455_CR91
  publication-title: Photogramm. Eng. Remote Sens.
  doi: 10.14358/PERS.78.1.75
– volume: 259
  start-page: 904
  year: 2010
  ident: 20455_CR64
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2009.11.030
– volume: 10
  start-page: 1266
  year: 2018
  ident: 20455_CR93
  publication-title: Remote Sens.
  doi: 10.3390/rs10081266
– ident: 20455_CR100
– volume: 7
  start-page: 434
  year: 1998
  ident: 20455_CR112
  publication-title: J. Comput. Graph. Stat.
  doi: 10.1080/10618600.1998.10474787
– ident: 20455_CR6
– ident: 20455_CR61
  doi: 10.1016/B978-0-12-417156-5.00014-9
– ident: 20455_CR81
– volume: 42
  start-page: 749
  year: 2012
  ident: 20455_CR107
  publication-title: Can. J. For. Res.
  doi: 10.1139/x2012-031
– volume: 23
  start-page: 40
  year: 2008
  ident: 20455_CR57
  publication-title: West J. Appl.
– volume: 434
  start-page: 142
  year: 2019
  ident: 20455_CR63
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2018.12.006
– volume: 103
  start-page: 67
  year: 2006
  ident: 20455_CR89
  publication-title: Remote Sens. Environ.
  doi: 10.1016/j.rse.2006.03.012
– volume: 438
  start-page: 86
  year: 2019
  ident: 20455_CR30
  publication-title: For. Ecol. Manag.
  doi: 10.1016/j.foreco.2019.02.011
– volume: 29
  start-page: 696
  year: 1931
  ident: 20455_CR69
  publication-title: J. For.
– volume: 102
  start-page: 2170
  year: 2009
  ident: 20455_CR31
  publication-title: J. Econ. Entomol.
  doi: 10.1603/029.102.0621
– volume: 10
  start-page: 237
  year: 2019
  ident: 20455_CR70
  publication-title: Forests
  doi: 10.3390/f10030237
– volume: 7
  start-page: 49
  year: 1981
  ident: 20455_CR44
  publication-title: J. Chem. Ecol.
  doi: 10.1007/BF00988635
– volume: 104
  start-page: 487
  year: 2017
  ident: 20455_CR108
  publication-title: Am. J. Bot.
  doi: 10.3732/ajb.1600326
– ident: 20455_CR84
– volume: 6
  start-page: 1721
  year: 2015
  ident: 20455_CR95
  publication-title: Forests
  doi: 10.3390/f6051721
– volume: 5
  start-page: 88
  year: 2014
  ident: 20455_CR41
  publication-title: Forests
  doi: 10.3390/f5010088
– ident: 20455_CR115
  doi: 10.6078/D16K5W
– volume: 1
  start-page: 21
  year: 1990
  ident: 20455_CR99
  publication-title: J. Vis. Commun. Image Represent.
  doi: 10.1016/1047-3203(90)90014-M
– ident: 20455_CR42
– ident: 20455_CR117
  doi: 10.17605/OSF.IO/WPK5Z
– volume: 42
  start-page: 501
  year: 1993
  ident: 20455_CR39
  publication-title: Appl. Stat.
  doi: 10.2307/2986328
SSID ssj0000391844
Score 2.587406
Snippet The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine ( Pinus ponderosa ) mortality, largely attributable to the western...
The recent Californian hot drought (2012-2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine...
The recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine...
The 2012–2016 drought and western pine beetle outbreaks caused unprecedented mortality of ponderosa pine in the Sierra Nevada, California. Here, the authors...
SourceID doaj
pubmedcentral
proquest
pubmed
crossref
springer
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 129
SubjectTerms 631/158/2165
704/158/2454
704/158/851
Aerial surveys
Animals
Bark
Beetles
California
Climate adaptation
Climate change
Climatic conditions
Dendroctonus brevicomis
Drone aircraft
Drought
Droughts
Ecological Parameter Monitoring - statistics & numerical data
Evergreen trees
Host-Parasite Interactions - physiology
Humanities and Social Sciences
Mortality
Mortality patterns
multidisciplinary
open climate campaign
Pest outbreaks
Pheromones - metabolism
Pine
Pine trees
Pinus ponderosa
Pinus ponderosa - parasitology
Pinus ponderosa - physiology
Plant Bark - parasitology
Plant Diseases - parasitology
Plant Dispersal
Science
Science (multidisciplinary)
Trees
Trees - parasitology
Trees - physiology
Water
Water deficit
Weevils - pathogenicity
Weevils - physiology
SummonAdditionalLinks – databaseName: Biological Science Database
  dbid: M7P
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Nb9UwDLdggMSFb1hhoCBxg2h9TdomJwQTEwc07QBot6hN3VEx2q19Az3-euy0r9PjYxeubVq5seP-HDs_A7xQtaGAa1FJVJhJXeVWlsZ4uaiR4HKC2odq988f8oMDc3RkD6cNt2Eqq1z7xOCoq87zHvluomnZpoQe4tenZ5K7RnF2dWqhcRWuMUtCEkr3Duc9FmY_N1pPZ2ViZXYHHTwDx0zMw57K1cb_KND2_w1r_lky-VveNPyO9m__74fcgVsTEBVvRsu5C1ewvQc3xtaUq_vQ7bG4ciAVomBOiX48ASG6WvDBEMHZbDE0P1EUbSX8SRO4X8UPwq69qJCJKZbiOHTyHURZ9F9FiUiGKZu2InOqxhd8C-ifIoEH8Gn_3ce993JqziA9gbylVNqXaDThiwwxLm2dmTLj-CytFIUsGBfWK2WztOZ2JjEqWxuLsTUZTWJBIOIhbLVdi9sg6tTrLCnKhfGcaC1MnKYqN74msBmjthEs1ipyfmIu5wYaJy5k0JVxo1odqdUFtbpVBC_nZ05H3o5LR79lzc8jmXM7XOj6YzctYWfSfOFVwpgq17a0FgsSsE6tz5BQWB3BzlrhbnIEg7vQdgTP59u0hDkvU7TYnY9jGMYmeQSPRjObJVFKc4VKEkG-YYAbom7eaZsvgSY8J1dLcDqCV2tTvRDr31Px-PKveAI3E67q4U0oswNby_4cn8J1_33ZDP2zsPx-AYxnNrc
  priority: 102
  providerName: ProQuest
Title Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality
URI https://link.springer.com/article/10.1038/s41467-020-20455-y
https://www.ncbi.nlm.nih.gov/pubmed/33420082
https://www.proquest.com/docview/2476251630
https://www.proquest.com/docview/2476561027
https://pubmed.ncbi.nlm.nih.gov/PMC7794511
https://doaj.org/article/8571c320782749b99ea565f59c6e317f
Volume 12
WOSCitedRecordID wos000626604400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: DOA
  dateStart: 20150101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: M~E
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Advanced Technologies & Aerospace Database
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: P5Z
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/hightechjournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Biological Science Database
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: M7P
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/biologicalscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: BENPR
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Health & Medical Collection
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: 7X7
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/healthcomplete
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2041-1723
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000391844
  issn: 2041-1723
  databaseCode: PIMPY
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwEB7BAhIXxJvAUhmJG0SbxE5sH9nVrkCCKkKACpcocSZQsaQo6YK6v35nnLRseV645FC7rTUz9nyTGX8D8Fg2hgKuuA5RYhaqWtuwMsaFcYMElxNUzle7v3upp1Mzm9n8XKsvrgkb6IEHwe2ZVMdOJuzJtLKVtVgSBmlS6zIk39fw6Rtpey6Y8mewtBS6qPGWTCTNXq_8mcDREjOwp-FqyxN5wv7focxfiyV_yph6R3R0Ha6NCFI8G1Z-Ay5gexOuDD0lV7dgccD_FvYkexRMBtENVxfEohF8o0NwGlr081MUZVsLdzz3pK3iO4HOTtTIjBJL8dG34O1FVXafRYVIFhVS9E52UA8_8MXDdoLwt-Ht0eGbg-fh2FUhdITOlqFUrkKjCBhkiFFlm8xUGQdWaS0p1sCotE5KSzLmPiQRStsYi5E1GcmgJO9_B3baRYv3QDSpU1lSVrFxnCEtTZSmUhvXkIYiVDaAeC3hwo2U49z54rjwqW9pikErBWml8FopVgE82Xzn60C48dfZ-6y4zUwmy_YfkAkVowkV_zKhAHbXai_GHdwXiSI3kRJajQJ4tBmmvccJlbLFxckwh_FnogO4O1jJZiVSKi4tSQLQW_aztdTtkXb-yfN7azojCQcH8HRtaT-W9WdR3P8fongAVxMu2uF3TGYXdpbdCT6Ey-7bct53E7ioZ9o_zQQu7R9O89cTv-8mXDKb0zNPP9BI_uJV_v4MC8kumw
linkProvider Directory of Open Access Journals
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLQguvB-BAkaCE0TNxk5iHxCCQtVVt6seCionkziTsqIkJdlSLT-K34jHSbZaHr31wDVxIsf5ZuYbz3gG4CkvpHW4hrmPHGNf5InyMymNPyzQ0uUQhXHZ7h_GyWQi9_fV7gr87M_CUFplrxOdos4rQ3vk66GwYhtZ9hC8OvrmU9coiq72LTRaWGzj_MS6bM3L0Vv7f5-F4ea7vY0tv-sq4BvLTmY-FyZDKaxhjBGDTBWxzGJyLKKcW66NQaoM5yqOCurDESBXhVQYKBljblJr_ex7L8CqILAPYHV3tLP7cbGrQ_XWpRDd6ZyAy_VGOF1EXhpVfo_8-ZIFdI0C_sZu_0zS_C1S6wzg5rX_bemuw9WOarPXrWzcgBUsb8Kltvnm_BZUG7Q8fmNBioyqZtTtGQ9WFYyOvjCK17Nm-gNZWubMHE5ddVt2Ytl5zXKk0hszduB6FTcsS-svLEO0oudPy9wKTN6-4Kvzb6yvcxven8vX3oFBWZV4D1gRGRGHaTaUhkLJqQyiiCfSFJZOByiUB8MeEtp0tdmpRcihdjkCXOoWRtrCSDsY6bkHzxfPHLWVSc4c_YaQthhJVcXdhao-0J2S0jJKhoaHxBoToTKlMLUTLCJlYrQ8s_BgrQeY7lRdo0_R5cGTxW2rpCjylJZYHbdjiKiHiQd3W1gvZsK5oByc0INkCfBLU12-U04_u0LoiTUm1mHw4EUvGqfT-vdS3D_7Kx7D5a29nbEejybbD-BKSDlMtOUm12Awq4_xIVw032fTpn7UCT-DT-ctNL8Ak62S8g
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6V8hAX3o9AASPBCaLNxk5iHxCClhVVq9UeAFVcQuJMyoqSlGRLtfw0fh0zTnar5dFbD1wTJ3Kcb2a-8YxnAJ7IUpPDNSx8lBj7qkiMn2tt_WGJRJdDVNZlu3_YTcZjvbdnJmvwc3EWhtMqFzrRKeqitrxHPggViW1E7CEYlH1axGRr9PLwm88dpDjSumin0UFkB-fH5L61L7a36F8_DcPRm3ebb_2-w4BvianMfKlsjlqRkYwRg9yUsc5jdjKiQhLvxiAzVkoTRyX35AhQmlIbDIyOsbAZWUJ67zk4n5CPyemEk-jjcn-HK69rpfpzOoHUg1Y5rcT-GteAj_z5ii10LQP-xnP_TNf8LWbrTOHo6v-8iNfgSk_AxatOYq7DGlY34GLXknN-E-pNXiq_Jeii4FoaTXfyQ9Sl4AMxgqP4op3-QJFVhbAHU1fzVhwTZ29EgVyQYyb2XQfjVuRZ80XkiCSQ_rQqSIyK7gVfnddDHtAteH8mX3sb1qu6wrsgysiqOMzyobYcYM50EEUy0bYkkh2gMh4MF_BIbV-xnRuHHKQuc0DqtINUSpBKHaTSuQfPls8cdvVKTh39mlG3HMm1xt2FutlPe9WV6igZWhkyl0yUyY3BjCZYRsbGSOyz9GBjAba0V4BteoI0Dx4vb5Pq4nhUVmF91I1h-h4mHtzpIL6ciZSKM3NCD5IV8K9MdfVONf3syqMnZGLIjfDg-UJMTqb176W4d_pXPIJLJCnp7vZ45z5cDjmxiffh9Aasz5ojfAAX7PfZtG0eOi0g4NNZS8wvuHiaVQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Cross-scale+interaction+of+host+tree+size+and+climatic+water+deficit+governs+bark+beetle-induced+tree+mortality&rft.jtitle=Nature+communications&rft.au=Koontz%2C+Michael+J.&rft.au=Latimer%2C+Andrew+M.&rft.au=Mortenson%2C+Leif+A.&rft.au=Fettig%2C+Christopher+J.&rft.date=2021-01-08&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2041-1723&rft.volume=12&rft_id=info:doi/10.1038%2Fs41467-020-20455-y&rft_id=info%3Apmid%2F33420082&rft.externalDocID=PMC7794511
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2041-1723&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2041-1723&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2041-1723&client=summon