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Record W2021855668 · doi:10.1088/1748-9326/6/4/041003

Browning boreal forests of western North America

2011· article· en· W2021855668 on OpenAlexaboutno aff
David L. Verbyla

Bibliographic record

VenueEnvironmental Research Letters · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsNormalized Difference Vegetation IndexBorealTaigaTundraEnvironmental sciencePhysical geographyVegetation (pathology)Remote sensingClimatologyGeographyClimate changeForestryGeologyArcticOceanography

Abstract

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The GIMMS NDVI dataset has been widely used to document a 'browning trend' in North American boreal forests (Goetz et al 2005, Bunn et al 2007, Beck and Goetz 2011). However, there has been speculation (Alcaraz-Segura et al 2010) that this trend may be an artifact due to processing algorithms rather than an actual decline in vegetation activity. This conclusion was based primarily on the fact that GIMMS NDVI did not capture NDVI recovery within most burned areas in boreal Canada, while another dataset consistently showed post-fire increasing NDVI. I believe that the results of Alcaraz-Segura et al (2010) were due simply to different pixel sizes of the two datasets (64 km 2 versus 1 km 2 pixels). Similar results have been obtained from tundra areas greening in Alaska, with the results simply due to these pixel size differences (Stow et al 2007). Furthermore, recent studies have documented boreal browning trends based on NDVI from other sensors. Beck and Goetz (2011) have shown the boreal browning trend derived from a different sensor (MODIS) to be very similar to the boreal browning trend derived from the GIMMS NDVI dataset for the circumpolar boreal region. Parent and Verbyla (2010) found similar declining NDVI patterns based on NDVI from Landsat sensors and GIMMS NDVI in boreal Alaska. Zhang et al (2008) found a similar 'browning trend' in boreal North America based on a production efficiency model using an integrated AVHRR and MODIS dataset. The declining NDVI trend in areas of boreal North America is consistent with tree-ring studies (D'Arrigo et al 2004, McGuire et al 2010, Beck et al 2011). The decline in tree growth may be due to temperature-induced drought stress (Barber et al 2000) caused by higher evaporative demands in a warming climate (Lloyd and Fastie 2002). In a circumpolar boreal study, Lloyd and Bunn (2007) found that a negative relationship between temperature and tree-ring growth occurred more frequently in warmer parts of species' ranges, suggesting that direct temperature stress might be a factor in some species. Since warm growing seasons are also typically dry growing seasons, direct temperature stress and moisture stress could occur simultaneously. For example, 2004 was the warmest summer in over 200 years in boreal Alaska (Barber et al 2004) but it was also during a drought with less than 50 mm of summer precipitation recorded in Fairbanks. In Fairbanks, the length of the growing season, as defined as the period above freezing, has increased by 45 per cent over the past 100 years, with no significant increase in precipitation (Wendler and Shulski 2009). Regional winter runoff has increased, likely associated with permafrost thawing (Brabets and Walvoord 2009), while surface water has decreased, likely associated with increased evapotranspiration (Riordan et al 2006, Anderson et al 2007, Berg et al 2009). The mean annual air temperature in boreal Alaska has increased by over 1.5 °C during the past 50 years (Stafford et al 2000), and is projected to increase by 3–7 °C by end of this century (Walsh et al 2008). Thus, it would be surprising if a declining NDVI trend was not occurring in the western boreal region of North America as the climate continues to warm. Insects and disease in the North American boreal forest may also affect the NDVI browning trends (Malmström and Raffa 2000), as the life histories of damaging insects may be linked to a warming boreal climate. For example, warmer temperatures contributed to the spruce beetle outbreaks in Alaska with a reduction in the beetle life cycle from two years to one year (Berg et al 2006). Thus, as the boreal climate continues to warm, tree growth reduction and mortality from insects and diseases may become more substantial. In boreal Alaska, recent alder dieback and mortality is likely to be related to alder's susceptibility to a canker-causing fungus in drought years (Ruess et al 2009). Recent widespread and prolonged outbreaks of aspen leaf miner and a willow leaf blotch miner in boreal Alaska are likely to have resulted in decreased NDVI (Parent and Verbyla 2010). The NDVI browning trend has expanded in area in boreal North America (Beck and Goetz 2011). If the trend towards a warmer and drier climate continues, these areas may represent a future tipping point where drought-induced mortality across a boreal region may occur. Such events have already occurred in the western United States (van Mantgem et al 2009) and the aspen parklands of the southern Canadian boreal forest (Michaelian et al 2010). References Alcaraz-Segura D, Chuvieco E, Epstein H E, Kasischke E S and Trishchenko A 2010 Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets Glob. Change Biol. 16 760–70 Anderson L, Abbott M B, Finney B P and Burns S J 2007 Late Holocene moisture balance variability in the southwest Yukon Territory, Canada Quatern. Sci. Rev. 26 130–41 Barber V A, Juday G P and Finney B P 2000 Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress Nature 405 668–73 Barber V A, Juday G P, Finney B P and Wilmking M 2004 Reconstruction of summer temperatures in interior Alaska from tree-ring proxies: evidence for changing synoptic climate regimes Clim. Change 63 91–120 Beck P S A and Goetz S J 2011 Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences Environ. Res. Lett. 6 045501 Beck P S A, Juday G P, Alix C, Barber V A, Winslow S E, Sousa E E, Heiser P, Herriges J D and Goetz S J 2011 Changes in forest productivity across Alaska consistent with biome shift Ecol. Lett. 14 373–9 Berg E E, Henry J D, Fastie C L, De Volder A D and Matsuoka S M 2006 Spruce beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and Reserve, Yukon Territory: relationship to summer temperatures and regional differences in disturbance regimes Forest Ecol. Manag. 227 219–32 Berg E E, Hillman K M, Dial R and DeRuwe A 2009 Recent woody invasion of wetlands on the Kenai Peninsula Lowlands, south-central Alaska: a major regime shift after 18 000 years of wet Sphagnum –sedge peat recruitment Canadian J. Forest Res. 39 2033–46 Brabets T P and Walvoord M A 2009 Trends in streamflow in the Yukon River Basin from 1944 to 2004 and the influence of the Pacific Decadal Oscillation J. Hydrol. 371 108–19 Bunn A G, Goetz S J, Kimball J S and Zhang K 2007 Northern high-latitude ecosystems respond to climate change EOS Trans. Am. Geophys. Union 88 333–40 D'Arrigo R, Kaufmann R K, Davi N, Jacoby G C, Laskowski C, Myneni R B and Cherubini P 2004 Thresholds for warming-induced growth decline at elevational tree line in the Yukon Territory, Canada Glob. Biogeochem. Cycles 18 GB3021 Goetz S J, Bunn A G, Fiske G J and Houghton R A 2005 Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance Proc. Natl Acad. Sci. USA 102 13521–5 Lloyd A H and Bunn A G 2007 Responses of the circumpolar boreal forest to the 20th century climate variability Environ. Res. Lett. 2 045013 Lloyd A H and Fastie C L 2002 Spatial and temporal variability in the growth and climate response of treeline trees in Alaska Clim. Change 52 481–509 Malmström C and Raffa K R 2000 Biotic disturbance agents in the boreal forest: considerations for vegetation change models Glob. Change Biol. 6 (Suppl. 1) 35–48 McGuire A D, Ruess R W, Lloyd A, Yarie J, Clein J S and Juday G P 2010 Vulnerability of white spruce tree growth in interior Alaska in response to climate variability: dendrochronological, demographic, and experimental perspectives Canadian J. Forest Res. 40 1197–209 Michealian M, Hogg E H, Hall R J and Arsenault E 2011 Massive mortality of aspen following severe drought along the southern edge of the Canadian boreal forest Glob. Change Biol. 17 2084–94 Parent M B and Verbyla D 2010 The browning of Alaska's boreal forest Remote Sens. 2 2729–47 Riordan B, Verbyla D and McGuire A D 2006 Shrinking ponds in subarctic Alaska based on 1950–2002 remotely sensed images J. Geophys. Res. 111 G04002 Ruess R W, McFarland J M, Trummer L M and Rohrs-Richey J K 2009 Disease-mediated declines in N-fixation inputs by Alnus tenuifolia to early-successional floodplains in interior and south-central Alaska Ecosystems 12 489–502 Stafford J M, Wendler G and Curtis J 2000 Temperature and precipitation of Alaska: 50 year trend analysis Theor. Appl. Climatology 67 33–44 Stow D, Peterson A, Hope A, Engstrom R and Coulter L 2007 Greenness trends of Arctic tundra vegetation in the 1990s: comparison of two NDVI data sets from NOAA AVHRR systems Int. J. Remote Sens. 28 4807–22 van Mantgem P J et al 2009 Widespread increase of tree mortality rates in the western United States Science 323 521–4 Walsh, J E, Chapman W L, Romanovsky V, Christensen J H and Stendel M 2008 Global climate model performance over Alaska and Greenland J. Clim. 21 6156–74 Wendler G and Shulski M 2009 A century of climate change for Fairbanks, Alaska Arctic 62 295–300 Zhang K, Kimball J S, Hogg E H, Zhao M, Oechel W C, Cassano J J and Running S W 2008 Satellite-based model detection of recent climate-driven changes in northern high-latitude vegetation productivity J. Geophys. Res. 113 G03033

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.258
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations49
Published2011
Admission routes1
Has abstractyes

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