Pan‐tropical prediction of forest structure from the largest trees

Bastin, Jean François; Rutishauser, Ervan; Kellner, James R.; Saatchi, Sassan; Pélissier, Raphaël; Hérault, Bruno; Slik, Ferry; Bogaert, Jan; Cannière, Charles De; Marshall, Andrew R.; Poulsen, John R.; Alvarez‐Loyayza, Patricia; Andrade, Ana; Angbonga‐Basia, Albert; Araujo‐Murakami, Alejandro; Arroyo, Luzmila; Narayanan, Ayyappan; Azevedo, C. P. de; Bánki, Olaf; Barbier, Nicolas; Barroso, Jorcely; Beeckman, Hans; Bitariho, Robert; Boeckx, Pascal; Boehning-Gaese, Katrin; Brandão, Hilandia; Brearley, Francis Q.; Hockemba, Mireille Breuer Ndoundou; Brienen, Roel; Camargo, José Luís; Campos‐Arceiz, Ahimsa; Cassart, Benoît; Chave, Jérôme; Chazdon, Robin L.; Chuyong, Georges; Clark, David B.; Clark, Connie J.; Condit, Richard; Coronado, Eurídice N. Honorio; Davidar, Priya; Haulleville, Thalès de; Descroix, Laurent; Doucet, Jean‐Louis; Dourdain, Aurélie; Droissart, Vincent; Thomas, Daniel; Espejo, Javier Silva; Espinosa, Santiago; Farwig, Nina; Fayolle, Adeline; Feldpausch, Ted R.; Ferraz, António; Fletcher, Christine; Gajapersad, Krisna; Gillet, Jean‐François; Amaral, Iêda Leão do; Gonmadje, C. H.; Grogan, James; Harris, David J.; Herzog, Sebastián K.; Homeier, Jürgen; Hubau, Wannes; Hubbell, Stephen P.; Hufkens, Koen; Hurtado, Johanna; Kamdem, Narcisse Guy; Kearsley, Elizabeth; Kenfack, David; Kessler, Michael; Labrière, Nicolas; Laumonier, Yves; Laurance, Susan G.; Laurance, William F.; Lewis, Simon L.; Libalah, Moses B.; Ligot, Gauthier; Lloyd, J.; Lovejoy, Thomas Ε.; Malhi, Yadvinder; Marimon, Beatriz Schwantes; Marimon, Ben Hur; Martin, Emanuel H.; Matius, Paulus; Meyer, Victoria; Bautista, Casimero Mendoza; Monteagudo‐Mendoza, Abel; Mtui, Arafat S.; Neill, David; Gutierrez, Germaine Alexander Parada; Pardo, Guido; Parren, Marc; Parthasarathy, N.; Phillips, Oliver L.; Pitman, Nigel C. A.; Ploton, Pierre; Ponette, Quentin; Ramesh, B.R.; Razafimahaimodison, Jean‐Claude; Réjou‐Méchain, Maxime; Rolim, Samir Gonçalves; Saltos, Hugo Romero; Rossi, L. M. B.; Spironello, Wilson Roberto; Rovero, Francesco; Saner, Philippe; Sasaki, Denise; Schulze, Mark; Silveira, Marcos; Singh, James; Sist, Plínio; Sonké, Bonaventure; Soto, José D.; Souza, Cíntia Rodrigues de; Stropp, Juliana; Sullivan, Martin J. P.; Swanepoel, Ben; Steege, Hans ter; Terborgh, John; Texier, Nicolas; Toma, Takeshi; Valencia, Renato; Valenzuela, Luis; Ferreira, Leandro Valle; Valverde, Fernando Cornejo; Andel, Tinde van; Vasque, Rodolfo; Verbeeck, Hans; Pandi, Vivek; Vleminckx, Jason; Vos, Vincent; Wagner, Fabien; Warsudi, Papi Puspa; Wortel, Verginia; Zagt, Roderick; Zébazé, Donatien

doi:10.1111/geb.12803

Cited by 85 publications

(96 citation statements)

References 72 publications

(104 reference statements)

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“…Hence, big-sized trees can be found in any region or sampling plot, but different abiotic and biotic factors may limit their large-diameter threshold (Bastin et al, 2018;Feldpausch et al, 2012). Therefore, in this study, the "big-sized trees effect" may be equally important in both secondary and old-growth forests where top 1% large-diameter, tall-stature, and big-crown trees are highly expected (but having different trees' size threshold and tree age) in naturally heterogenous, species-rich, and structurally complex large-scale tropical forests.…”

Section: Species Richness Top 1% Big-sized Trees and 99% Remaininmentioning

confidence: 99%

“…In tropical forests, stand-level aboveground biomass can be predicted from a few big-sized trees (Slik et al, 2013), and more specifically from 20 large-diameter trees per hectare (Bastin et al, 2018), and top 5% (Bastin et al, 2015) and top 1% large-diameter trees (Lutz et al, 2018). From an ecological theoretical point of view, vegetation quantity (i.e., initial biomass stocks) compared to vegetation quality (i.e., species diversity, functional trait diversity, and trait composition) has a strong positive effect on productivity in natural forests (i.e., the "vegetation quantity hypothesis") because steep biomass build-up during succession overrides more subtle effects of species diversity on forest functioning.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, stand basal area compared to functional trait diversity promotes productivity (Paquette & Messier, 2011), and as such, the response of aboveground biomass productivity to species richness is largely mediated by stand basal area in natural forests (Vilà et al, 2013). In this understanding, the overruling effects of stand basal area, stand density, functional dominance, and initial biomass stock on forest functioning might be attributable to the fact that top 1%-20% largediameter trees largely explain variation in aboveground biomass at local, regional, continental, and global scales (Ali, Lohbeck, et al, 2018;Bastin et al, 2018;Lutz et al, 2018;Malhi et al, 2006;Slik et al, 2013). In this understanding, the overruling effects of stand basal area, stand density, functional dominance, and initial biomass stock on forest functioning might be attributable to the fact that top 1%-20% largediameter trees largely explain variation in aboveground biomass at local, regional, continental, and global scales (Ali, Lohbeck, et al, 2018;Bastin et al, 2018;Lutz et al, 2018;Malhi et al, 2006;Slik et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we were particularly interested to test the "big-sized trees effect" relative to the effect of species richness on aboveground biomass while accounting for the full (see Figure 1a-c) and partial mediation (see Figure 1d) effects of climatic and soil conditions, as well as interactions between big-sized trees attributes and species richness, in natural tropical forests. Here, in addition to other variable ( Figure 1), we use three attributes of the big-sized trees (i.e., top 1% large-diameter, tall-stature, and big-crown) that control some of the main changes in aboveground biomass and nutrients over time and space (Bastin et al, 2018;Chave et al, 2014;Goodman, Phillips, & Baker, 2014;Lutz et al, 2018). Based on the hypothesized paths in the key conceptual model for the "big-sized trees effect" (Figure 1a), we address the following specific research questions.…”

Section: Introductionmentioning

confidence: 99%

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Big‐sized trees overrule remaining trees' attributes and species richness as determinants of aboveground biomass in tropical forests

Ali

Lin

Kong³

et al. 2019

Global Change Biology

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Large‐diameter, tall‐stature, and big‐crown trees are the main stand structures of forests, generally contributing a large fraction of aboveground biomass, and hence play an important role in climate change mitigation strategies. Here, we hypothesized that the effects of large‐diameter, tall‐stature, and big‐crown trees overrule the effects of species richness and remaining trees attributes on aboveground biomass in tropical forests (i.e., we term the “big‐sized trees hypothesis”). Specifically, we assessed the importance of: (a) the “top 1% big‐sized trees effect” relative to species richness; (b) the “99% remaining trees effect” relative to species richness; and (c) the “top 1% big‐sized trees effect” relative to the “99% remaining trees effect” and species richness on aboveground biomass. Using environmental factor and forest inventory datasets from 712 tropical forest plots in Hainan Island of southern China, we tested several structural equation models for disentangling the relative effects of big‐sized trees, remaining trees attributes, and species richness on aboveground biomass, while considering for the full (indirect effects only) and partial (direct and indirect effects) mediation effects of climatic and soil conditions, as well as interactions between species richness and trees attributes. We found that top 1% big‐sized trees attributes strongly increased aboveground biomass (i.e., explained 55%–70% of the accounted variation) compared to species richness (2%–18%) and 99% remaining trees attributes (6%–10%). In addition, species richness increased aboveground biomass indirectly via increasing big‐sized trees but via decreasing remaining trees. Hence, we show that the “big‐sized trees effect” overrides the effects of remaining trees attributes and species richness on aboveground biomass in tropical forests. This study also indicates that big‐sized trees may be more susceptible to atmospheric drought. We argue that the effects of big‐sized trees on species richness and aboveground biomass should be tested for better understanding of the ecological mechanisms underlying forest functioning.

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Section: Species Richness Top 1% Big-sized Trees and 99% Remaininmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Big‐sized trees overrule remaining trees' attributes and species richness as determinants of aboveground biomass in tropical forests

Ali

Lin

Kong³

et al. 2019

Global Change Biology

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“…We therefore expect that studies in which the results are driven by the upper canopy, sun-exposed trees will benefit the most from remote sensing at broad scales. For example, the total amount of biomass in most forests depends strongly on the largest trees and will be less sensitive to potential non-detections of smaller subcanopy trees (Asner et al, 2012, Stegen et al 2011, Bastin et al 2018). The inclusion of RGB data may benefit existing large-scale LiDAR-based studies of tree growth (Caughlin et al, 2016), taxonomy (Féret and Asner, 2012) and disturbance (Garcia Millan and Sanchez-Azofeifa, 2018), since improved individual segmentation will lead to a more accurate matching of individual trees to metadata on taxonomy and health status.…”

Section: Discussionmentioning

confidence: 99%

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Marconi

Bohlman

Zare

et al. 2019

Preprint

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11Tree detection is a fundamental task in remote sensing for forestry and ecosystem 12 ecology applications. While many individual tree segmentation algorithms have been 13 proposed, the development and testing of these algorithms is typically site specific, with 14 few methods evaluated against data from multiple forest types simultaneously. This 15 makes it difficult to determine the generalization of proposed approaches, and limits tree 16 detection at broad scales. Using data from the National Ecological Observatory Network 17 we extend a recently developed semi-supervised deep learning algorithm to include 18 data from a range of forest types, determine whether information from one forest can be 19 used for tree detection in other forests, and explore the potential for building a universal 20 tree detection algorithm. We find that the deep learning approach works well for 21 Discovery Initiative through grant GBMF4563 to E.P. White and by the National Science 464 Foundation through grant 1926542 to E.P. White, S.

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Natural vegetation biomass and the dimension of forest quality in tropical agricultural landscapes

de Toledo,

Pivello,

Perring

et al. 2024

Ecological Applications

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Forest cover has been a pivotal indicator of biological conservation and carrying capacity for wildlife in forest ecoregions. Such a relationship underpins policies focused on the extension of protected lands. Here, we estimate aboveground biomass (AGB) as a proxy for habitat quality in seminatural rural patches and provide a comparison with approaches that only consider forest cover. We hypothesize that recommendations for biological conservation in agricultural landscapes are substantially improved if habitat quality is also taken into account, and thus consider the possibility of forest quality being modulated by land‐use amount, type, and age. We assessed AGB in a densely farmed Brazilian region using a straightforward approach designed to be affordable at large scales, focusing on two expanding and contrasting land uses: sugarcane, and eucalyptus plantations. At a detailed scale, we confirmed through field surveys and AGB estimation using 3D‐multispectral imagery (i.e., AGB = 0.842 × vegetation heightNDVI+1) that AGB variation could be predicted with forest degradation classes that are visually distinguishable with high‐resolution images: 9.33 t ha−1 (90% predictive intervals [PI] = [3.23, 26.97]) in regenerating fields (RF), 31.12 t ha−1 (90% PI = [10.77, 89.90]) in pioneer woods (PW), and 149.04 t ha−1 (90% PI = [51.59, 430.58]) in dense forests (DF). Applying these values to land units sampled across the study region, we found an average land use of 88.5%, together with 11.5% of land set aside for conservation, which reduced AGB to less than 4.2% of its potential (averages of 5.85 t ha−1 in sugarcane‐dominated areas and 6.56 t ha−1 in eucalyptus‐dominated areas, with secondary forests averaging 149.04 t ha−1). This imbalance between forest cover and AGB resulted from forest quality decay, which was similarly severe among land‐use types, ages, and extensions. Therefore, the shortage of trophic resources is likely more critical to wildlife than spatial limitations in vastly deforested tropical ecoregions, where AGB and carbon sinks can be more than doubled just by restoring forests in lands currently spared by agriculture.

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Pan‐tropical prediction of forest structure from the largest trees

Cited by 85 publications

References 72 publications

Big‐sized trees overrule remaining trees' attributes and species richness as determinants of aboveground biomass in tropical forests

Big‐sized trees overrule remaining trees' attributes and species richness as determinants of aboveground biomass in tropical forests

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Natural vegetation biomass and the dimension of forest quality in tropical agricultural landscapes

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