Fine-scale Topoclimate Modeling and Climatic Treeline Prediction of Great Basin Bristlecone Pine (Pinus longaeva) in the American Southwest

Great Basin bristlecone pine (Pinus longaeva) and foxtail pine (Pinus balfouriana) are valuable paleoclimate resources due to the climatic sensitivity of their annually-resolved rings. Recent treeline research has shown that growing season temperatures limit tree growth at and just below the upper treeline. In the Great Basin, the presence of precisely dated remnant wood above modern treeline shows that this ecotone shifts at centennial timescales tracking long-term changes in climate; in some areas during the Holocene climatic optimum treeline was 100 meters higher than at present. Such phenomena has motivated this analysis; regional treeline position models built exclusively from climate data may identify characteristics specific to Great Basin treelines and inform future physiological studies, and provide a measure of climate sensitivity specific to bristlecone and foxtail pine treelines. This study implements a topoclimatic analysis—using topographic position to explain patterns in surface temperatures across complex mountainous terrain—to model treeline position of three semi-arid bristlecone and/or foxtail pine treelines in the Great Basin as a function of topographically modified climate variables calculated from in situ measurements. Results indicate: (1) the treelines used in this study require a growing season length of between 143 - 152 days and average temperature ranging from 5.5 - 7.6 °C, (2) site-specific treeline position models may be improved through topoclimatic analysis—specifically the inclusion of an integrated measure of climate rather than a growing season isotherm measured in degrees, (3) treeline position in the Great Basin is likely out of equilibrium with the current climate indicating a potential shift in the primary growth-limiting factor at the highest elevations where trees are found

Cluster Analysis and Topoclimate Modeling to Examine Bristlecone Pine Tree-ring Growth Signals in the Great Basin, USA

Tree rings have long been used to make inferences about the environmental factors that influence tree growth. Great Basin bristlecone pine is a long-lived species and valuable dendroclimatic resource, but often with mixed growth signals; in many cases, not all trees at one location are limited by the same environmental variable. Past work has identified an elevational threshold below the upper treeline above which trees are limited by temperature, and below which trees tend to be moisture limited. This study identifies a similar threshold in terms of temperature instead of elevation through fine-scale topoclimatic modeling, which uses a suite of topographic and temperature-sensor data to predict temperatures across landscapes. We sampled trees near the upper limit of growth at four high-elevation locations in the Great Basin region, USA, and used cluster analysis to find dual-signal patterns in radial growth. We observed dual-signal patterns in ring widths at two of those sites, with the signals mimicking temperature and precipitation patterns. Trees in temperature-sensitive clusters grew in colder areas, while moisture-sensitive cluster trees grew in warmer areas. We found thresholds between temperatureand moisture-sensitivity ranging from 7.4 °C to 8°C growing season mean temperature. Our findings allow for a better physiological understanding of bristlecone pine growth, and seek to improve the accuracy of climate reconstructions

Fine-scale Modeling of Bristlecone Pine Treeline Position in the Great Basin, USA

Great Basin bristlecone pine (Pinus longaeva) and foxtail pine (Pinus balfouriana) are valuable paleoclimate resources due to their longevity and climatic sensitivity of their annually-resolved rings. Treeline research has shown that growing season temperatures limit tree growth at and just below the upper treeline. In the Great Basin, the presence of precisely dated remnant wood above modern treeline shows that the treeline ecotone shifts at centennial timescales tracking long-term changes in climate; in some areas during the Holocene climatic optimum treeline was 100 meters higher than at present. Regional treeline position models built exclusively from climate data may identify characteristics specific to Great Basin treelines and inform future physiological studies, providing a measure of climate sensitivity specific to bristlecone and foxtail pine treelines. This study implements a topoclimatic analysis—using topographic variables to explain patterns in surface temperatures across diverse mountainous terrain—to model the treeline position of three semi-arid bristlecone and/or foxtail pine treelines in the Great Basin as a function of growing season length and mean temperature calculated from in situ measurements. Results indicate: (1) the treeline sites used in this study are similar to other treelines globally, and require a growing season length of between 147–153 days and average temperature ranging from 5.5°C–7.2°C, (2) site-specific treeline position models may be improved through topoclimatic analysis and (3) treeline position in the Great Basin is likely out of equilibrium with the current climate, indicating a possible future upslope shift in treeline position

GEDI launches a new era of biomass inference from space

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission's integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.Additional co-authors: Scott J. Goetz, Hao Tang, Michelle Hofton, Bryan Blair, Scott Luthcke, Lola Fatoyinbo, Alfonso Alonso, Hans-Erik Andersen, Paul Aplin, Timothy R. Baker, Nicolas Barbier, Jean Francois Bastin, Peter Biber, Pascal Boeckx, Jan Bogaert, Luigi Boschetti, Peter Brehm Boucher, Doreen S. Boyd, David F.R.P. Burslem, Sofia Calvo-Rodriguez, Jérôme Chave, Robin L. Chazdon, David B. Clark, Deborah A. Clark, Warren B. Cohen, David A. Coomes, Piermaria Corona, K.C. Cushman, Mark E.J. Cutler, James W. Dalling, Michele Dalponte, Jonathan Dash, Sergio de-Miguel, Songqiu Deng, Peter Woods Ellis, Barend Erasmus, Patrick A.Fekety, Alfredo Fernandez-Landa, Antonio Ferraz, Rico Fischer, Adrian G. Fisher, Antonio García-Abril, Terje Gobakken, Jorg M. Hacker, Marco Heurich, Ross A. Hill, Chris Hopkinson, Huabing Huang, Stephen P. Hubbell, Andrew T. Hudak, Andreas Huth, Benedikt Imbach, Masato Katoh, Elizabeth Kearsley, David Kenfack, Natascha Kljun, Nikolai Knapp, Kamil Král, Martin Krůček, Nicolas Labrière, Simon L. Lewis, Marcos Longo, Richard M. Lucas, Russell Main, Jose A. Manzanera, Rodolfo Vásquez Martínez, Renaud Mathieu, Herve Memiaghe, Victoria Meyer, Abel Monteagudo Mendoza, Alessandra Monerris, Paul Montesano, Felix Morsdorf, Erik Næsset, Laven Naidoo, Reuben Nilus, Michael O’Brien, David A. Orwig, Konstantinos Papathanassiou, Geoffrey Parker, Christopher Philipson, Oliver L. Phillips, Jan Pisek, John R. Poulsen, Hans Pretzsch, Christoph Rüdiger, Sassan Saatchi, Arturo Sanchez-Azofeifa, Nuria Sanchez-Lopez, Robert Scholes, Carlos A. Silva, Marc Simard, Andrew Skidmore, Krzysztof Stereńczak, Mihai Tanase, Chiara Torresan, Ruben Valbuena, Hans Verbeeck, Tomas Vrska, Konrad Wessels, Joanne C. White, Eliakimu Zahabu, Carlo Zgragge

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Fine-scale modeling of bristlecone pine treeline position in the Great Basin, USA

Great Basin bristlecone pine (Pinus longaeva) and foxtail pine (Pinus balfouriana) are valuable paleoclimate resources due to their longevity and climatic sensitivity of their annually-resolved rings. Treeline research has shown that growing season temperatures limit tree growth at and just below the upper treeline. In the Great Basin, the presence of precisely dated remnant wood above modern treeline shows that the treeline ecotone shifts at centennial timescales tracking long-term changes in climate; in some areas during the Holocene climatic optimum treeline was 100 meters higher than at present. Regional treeline position models built exclusively from climate data may identify characteristics specific to Great Basin treelines and inform future physiological studies, providing a measure of climate sensitivity specific to bristlecone and foxtail pine treelines. This study implements a topoclimatic analysis-using topographic variables to explain patterns in surface temperatures across diverse mountainous terrain-to model the treeline position of three semi-arid bristlecone and/or foxtail pine treelines in the Great Basin as a function of growing season length and mean temperature calculated from in situ measurements. Results indicate: (1) the treeline sites used in this study are similar to other treelines globally, and require a growing season length of between 147-153 days and average temperature ranging from 5.5 degrees C-7.2 degrees C, (2) site-specific treeline position models may be improved through topoclimatic analysis and (3) treeline position in the Great Basin is likely out of equilibrium with the current climate, indicating a possible future upslope shift in treeline position.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Cluster analysis and topoclimate modeling to examine bristlecone pine tree-ring growth signals in the Great Basin, USA

Tree rings have long been used to make inferences about the environmental factors that influence tree growth. Great Basin bristlecone pine is a long-lived species and valuable dendroclimatic resource, but often with mixed growth signals; in many cases, not all trees at one location are limited by the same environmental variable. Past work has identified an elevational threshold below the upper treeline above which trees are limited by temperature, and below which trees tend to be moisture limited. This study identifies a similar threshold in terms of temperature instead of elevation through fine-scale topoclimatic modeling, which uses a suite of topographic and temperature-sensor data to predict temperatures across landscapes. We sampled trees near the upper limit of growth at four high-elevation locations in the Great Basin region, USA, and used cluster analysis to find dual-signal patterns in radial growth. We observed dual-signal patterns in ring widths at two of those sites, with the signals mimicking temperature and precipitation patterns. Trees in temperature-sensitive clusters grew in colder areas, while moisture-sensitive cluster trees grew in warmer areas. We found thresholds between temperature- and moisture-sensitivity ranging from 7.4 degrees C to 8 degrees C growing season mean temperature. Our findings allow for a better physiological understanding of bristlecone pine growth, and seek to improve the accuracy of climate reconstructions.National Science Foundation [ATM-1203749]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]