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      Warming-induced tree growth may help offset increasing disturbance across the Canadian boreal forest

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          Significance

          Warmer temperatures have the potential to increase productivity in the cold-limited, Canadian boreal forest, but evidence remains controversial. We explored the climatic sensitivity of growth of the six most abundant boreal tree species in North America using an unprecedented network of permanent sample plot records distributed across both Canada and the United States. Our results indicate an overall positive effect of warming on tree growth under several climate change scenarios by midcentury, peaking in the colder, wetter regions of the boreal forest. Despite substantial variations among regions and species, such higher growth rates may help offset some of the negative impacts of projected increases in forest disturbance on future wood supply and carbon sequestration.

          Abstract

          Large projected increases in forest disturbance pose a major threat to future wood fiber supply and carbon sequestration in the cold-limited, Canadian boreal forest ecosystem. Given the large sensitivity of tree growth to temperature, warming-induced increases in forest productivity have the potential to reduce these threats, but research efforts to date have yielded contradictory results attributed to limited data availability, methodological biases, and regional variability in forest dynamics. Here, we apply a machine learning algorithm to an unprecedented network of over 1 million tree growth records (1958 to 2018) from 20,089 permanent sample plots distributed across both Canada and the United States, spanning a 16.5 °C climatic gradient. Fitted models were then used to project the near-term (2050 s time period) growth of the six most abundant tree species in the Canadian boreal forest. Our results reveal a large, positive effect of increasing thermal energy on tree growth for most of the target species, leading to 20.5 to 22.7% projected gains in growth with climate change under RCP 4.5 and 8.5. The magnitude of these gains, which peak in the colder and wetter regions of the boreal forest, suggests that warming-induced growth increases should no longer be considered marginal but may in fact significantly offset some of the negative impacts of projected increases in drought and wildfire on wood supply and carbon sequestration and have major implications on ecological forecasts and the global economy.

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          The representative concentration pathways: an overview

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            A large and persistent carbon sink in the world's forests.

            The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year(-1)) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year(-1) from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year(-1) partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year(-1). Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year(-1), with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.
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              A working guide to boosted regression trees.

              1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                3 January 2023
                10 January 2023
                3 January 2023
                : 120
                : 2
                : e2212780120
                Affiliations
                [1] aFaculty of Forestry and Environmental Management, University of New Brunswick , Fredericton E3B 5A3, Canada
                Author notes
                1To whom correspondence may be addressed. Email: jiejie.wang@ 123456unb.ca .

                Edited by Sandra Lavorel, Grenoble Alpes University, Grenoble, France; received July 29, 2022; accepted November 11, 2022

                Author information
                https://orcid.org/0000-0002-2122-6792
                https://orcid.org/0000-0001-7841-7082
                Article
                202212780
                10.1073/pnas.2212780120
                9926259
                36595673
                ae30d8a5-b4d4-4e6d-9a44-68f9d22f7124
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 29 July 2022
                : 11 November 2022
                Page count
                Pages: 10, Words: 6954
                Funding
                Funded by: Gouvernement du Canada | NRCan | Canadian Forest Service (CFS), FundRef 501100012394;
                Award ID: Forest Change Research Program
                Award Recipient : Anthony R. Taylor
                Funded by: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC), FundRef 501100000038;
                Award ID: 2019-04353
                Award Recipient : Loïc D'Orangeville
                Funded by: New Brunswick Innovation Fund;
                Award ID: 2019-029
                Award Recipient : Loïc D'Orangeville
                Categories
                research-article, Research Article
                eco, Ecology
                414
                Biological Sciences
                Ecology

                climate change,forest permanent sample plots,canadian boreal forest,gains in tree growth,forest disturbance

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