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1. Positive feedbacks and alternative stable states in forest leaf types

Author: Zou, Y, Zohner, CM, Averill, C, Ma, H, Merder, J, Berdugo, M, Bialic-Murphy, L, Mo, L, Brun, P, Zimmermann, NE, Liang, J, de-Miguel, S, Nabuurs, GJ, Reich, PB, Niinements, U, Dahlgren, J, Kändler, G, Ratcliffe, S, Ruiz-Benito, P, de Zavala, MA, Crowther, TW, Abegg, M, Adou Yao, YC, Alberti, G, Almeyda Zambrano, AM, Alvarado, BV, Alvarez-Dávila, E, Alvarez-Loayza, P, Alves, LF, Ammer, C, Antón-Fernández, C, Araujo-Murakami, A, Arroyo, L, Avitabile, V, Aymard, GA, Baker, TR, Bałazy, R, Banki, O, Barroso, JG, Bastian, ML, Bastin, JF, Birigazzi, L, Birnbaum, P, Bitariho, R, Boeckx, P, Bongers, F, Bouriaud, O, Brancalion, PHS, Brandl, S, Brearley, FQ, Brienen, R, Broadbent, EN, Bruelheide, H, Bussotti, F, Gatti, RC, César, RG, Cesljar, G, Chazdon, R, Chen, HYH, Chisholm, C, Cho, H, Cienciala, E, Clark, C, Clark, D, Colletta, GD, Coomes, DA, Valverde, FC, Corral-Rivas, JJ, Crim, PM, Cumming, JR, Dayanandan, S, de Gasper, AL, Decuyper, M, Derroire, G, DeVries, B, Djordjevic, I, Dolezal, J, Dourdain, A, Obiang, NLE, Enquist, BJ, Eyre, TJ, Fandohan, AB, Fayle, TM, Feldpausch, TR, Ferreira, LV, Finér, L, Fischer, M, Fletcher, C, Fridman, J, Frizzera, L, Gamarra, JGP, Gianelle, D, Glick, HB, Harris, DJ, Hector, A, Hemp, A, Hengeveld, G, Hérault, B, Herbohn, JL, Herold, M, Zou, Y, Zohner, CM, Averill, C, Ma, H, Merder, J, Berdugo, M, Bialic-Murphy, L, Mo, L, Brun, P, Zimmermann, NE, Liang, J, de-Miguel, S, Nabuurs, GJ, Reich, PB, Niinements, U, Dahlgren, J, Kändler, G, Ratcliffe, S, Ruiz-Benito, P, de Zavala, MA, Crowther, TW, Abegg, M, Adou Yao, YC, Alberti, G, Almeyda Zambrano, AM, Alvarado, BV, Alvarez-Dávila, E, Alvarez-Loayza, P, Alves, LF, Ammer, C, Antón-Fernández, C, Araujo-Murakami, A, Arroyo, L, Avitabile, V, Aymard, GA, Baker, TR, Bałazy, R, Banki, O, Barroso, JG, Bastian, ML, Bastin, JF, Birigazzi, L, Birnbaum, P, Bitariho, R, Boeckx, P, Bongers, F, Bouriaud, O, Brancalion, PHS, Brandl, S, Brearley, FQ, Brienen, R, Broadbent, EN, Bruelheide, H, Bussotti, F, Gatti, RC, César, RG, Cesljar, G, Chazdon, R, Chen, HYH, Chisholm, C, Cho, H, Cienciala, E, Clark, C, Clark, D, Colletta, GD, Coomes, DA, Valverde, FC, Corral-Rivas, JJ, Crim, PM, Cumming, JR, Dayanandan, S, de Gasper, AL, Decuyper, M, Derroire, G, DeVries, B, Djordjevic, I, Dolezal, J, Dourdain, A, Obiang, NLE, Enquist, BJ, Eyre, TJ, Fandohan, AB, Fayle, TM, Feldpausch, TR, Ferreira, LV, Finér, L, Fischer, M, Fletcher, C, Fridman, J, Frizzera, L, Gamarra, JGP, Gianelle, D, Glick, HB, Harris, DJ, Hector, A, Hemp, A, Hengeveld, G, Hérault, B, Herbohn, JL, and Herold, M
Abstract: The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4–43% higher growth rates, 14–17% higher survival rates and 4–7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.
Published: 2024

2. Consistent patterns of common species across tropical tree communities

Author: Cooper, DLM, Lewis, SL, Sullivan, MJP, Prado, PI, ter Steege, H, Barbier, N, Slik, F, Sonké, B, Ewango, CEN, Adu-Bredu, S, Affum-Baffoe, K, de Aguiar, DPP, Reategui, MAA, Aiba, SI, Albuquerque, BW, Matos, FDD, Alonso, A, Amani, CA, do Amaral, DD, do Amaral, IL, Andrade, A, Miranda, IPD, Angoboy, IB, Araujo-Murakami, A, Arboleda, NC, Arroyo, L, Ashton, P, Aymard, CGA, Baider, C, Baker, TR, Balinga, MPB, Balslev, H, Banin, LF, Bánki, OS, Baraloto, C, Barbosa, EM, Barbosa, FR, Barlow, J, Bastin, JF, Beeckman, H, Begne, S, Bengone, NN, Berenguer, E, Berry, N, Bitariho, R, Boeckx, P, Bogaert, J, Bonyoma, B, Boundja, P, Bourland, N, Bosela, FB, Brambach, F, Brienen, R, Burslem, DFRP, Camargo, JL, Campelo, W, Cano, A, Cárdenas, S, López, DC, Carpanedo, RD, Márquez, YAC, Carvalho, FA, Casas, LF, Castellanos, H, Castilho, CV, Cerón, C, Chapman, CA, Chave, J, Chhang, P, Chutipong, W, Chuyong, GB, Cintra, BBL, Clark, CJ, de Souza, FC, Comiskey, JA, Coomes, DA, Valverde, FC, Correa, DF, Costa, FRC, Costa, JBP, Couteron, P, Culmsee, H, Cuni-Sanchez, A, Dallmeier, F, Damasco, G, Dauby, G, Dávila, N, Doza, HPD, De Alban, JDT, de Assis, RL, De Canniere, C, De Haulleville, T, Carim, MDV, Demarchi, LO, Dexter, KG, Di Fiore, A, Din, HHM, Disney, MI, Djiofack, BY, Djuikouo, MNK, Van Do, T, Doucet, JL, Draper, FC, Droissart, V, Duivenvoorden, JF, Engel, J, Estienne, V, Farfan-Rios, W, Fauset, S, Feeley, KJ, Feitosa, YO, Feldpausch, TR, Ferreira, C, Ferreira, J, Ferreira, LV, Fletcher, CD, Flores, BM, Fofanah, A, Foli, EG, Fonty, E, Fredriksson, GM, Fuentes, A, Galbraith, D, Gonzales, GPG, Garcia-Cabrera, K, García-Villacorta, R, Gomes, VHF, Gómez, RZ, Gonzales, T, Gribel, R, Guedes, MC, Guevara, JE, Hakeem, KR, Hall, JS, Hamer, KC, Harrison, RD, Harris, DJ, Hart, TB, Hector, A, Henkel, TW, Herbohn, J, Hockemba, MBN, Hoffman, B, Holmgren, M, Coronado, ENH, Huamantupa-Chuquimaco, I, Hubau, W, Imai, N, Irume, MV, Jansen, PA, Jeffery, KJ, Jimenez, EM, Jucker, T, Junqueira, AB, Kalamandeen, M, Kamdem, NG, Kartawinata, K, Yakusu, EK, Katembo, JM, Kearsley, E, Kenfack, D, Kessler, M, Khaing, TT, Killeen, TJ, Kitayama, K, Klitgaard, B, Labriere, N, Laumonier, Y, Laurance, SGW, Laurance, WF, Laurent, F, Le, TC, Leal, ME, Novo, EMLD, Levesley, A, Libalah, MB, Licona, JC, Lima, DD, Lindsell, JA, Lopes, A, Lopes, MA, Lovett, JC, Lowe, R, Lozada, JR, Lu, XH, Luambua, NK, Luize, BG, Maas, P, Magalhaes, JLL, Magnusson, WE, Mahayani, NPD, Makana, JR, Malhi, Y, Rincón, LM, Mansor, A, Manzatto, AG, Marimon, BS, Marimon, BH Jr, Marshall, AR, Martins, MP, Mbayu, FM, de Medeiros, MB, Mesones, I, Metali, F, Mihindou, V, Millet, J, Milliken, W, Mogollon, HF, Molino, JF, Said, MNM, Mendoza, AM, Montero, JC, Moore, S, Mostacedo, B, Pinto, LFM, Mukul, SA, Munishi, PKT, Nagamasu, H, Nascimento, HEM, Nascimento, MT, Neill, D, Nilus, R, Noronha, JC, Nsenga, L, Vargas, PN, Ojo, L, Oliveira, AA, de Oliveira, EA, Ondo, FE, Cuenca, WP, Pansini, S, Pansonato, MP, Paredes, MR, Paudel, E, Pauletto, D, Pearson, RG, Pena, JLM, Pennington, RT, Peres, CA, Permana, A, Petronelli, P, Mora, MCP, Phillips, JF, Phillips, OL, Pickavance, G, Piedade, MTF, Pitman, NCA, Ploton, P, Popelier, A, Poulsen, JR, Prieto, A, Primack, RB, Priyadi, H, Qie, L, Quaresma, AC, de Queiroz, HL, Ramirez-Angulo, H, Ramos, JF, Reis, NFC, Reitsma, J, Revilla, JDC, Riutta, T, Rivas-Torres, G, Robiansyah, I, Rocha, M, Rodrigues, DD, Rodriguez-Ronderos, ME, Rovero, F, Rozak, AH, Rudas, A, Rutishauser, E, Sabatier, D, Sagang, L, Sampaio, AF, Samsoedin, I, Satdichanh, M, Schietti, J, Schöngart, J, Scudeller, VV, Seuaturien, N, Sheil, D, Sierra, R, Silman, MR, Silva, TSF, Guimaraes, JRD, Simo-Droissart, M, Simon, MF, Sist, P, Sousa, TR, Farias, ED, Coelho, LD, Spracklen, DV, Stas, SM, Steinmetz, R, Stevenson, PR, Stropp, J, Sukri, RS, Sunderland, TCH, Suzuki, E, Swaine, MD, Tang, JW, Taplin, J, Taylor, DM, Tello, JS, Terborgh, J, Texier, N, Theilade, I, Thomas, DW, Thomas, R, Thomas, SC, Tirado, M, Toirambe, B, de Toledo, JJ, Tomlinson, KW, Torres-Lezama, A, Tran, HD, Mukendi, JT, Tumaneng, RD, Umaña, MN, Umunay, PM, Giraldo, LEU, Sandoval, EHV, Gamarra, LV, Van Andel, TR, van de Bult, M, van de Pol, J, van der Heijden, G, Vasquez, R, Vela, CIA, Venticinque, EM, Verbeeck, H, Veridiano, RKA, Vicentini, A, Vieira, ICG, Torre, EV, Villarroel, D, Zegarra, BEV, Vleminckx, J, von Hildebrand, P, Vos, VA, Vriesendorp, C, Webb, EL, White, LJT, Wich, S, Wittmann, F, Zagt, R, Zang, RG, Zartman, CE, Zemagho, L, Zent, EL, Zent, S, Cooper, DLM, Lewis, SL, Sullivan, MJP, Prado, PI, ter Steege, H, Barbier, N, Slik, F, Sonké, B, Ewango, CEN, Adu-Bredu, S, Affum-Baffoe, K, de Aguiar, DPP, Reategui, MAA, Aiba, SI, Albuquerque, BW, Matos, FDD, Alonso, A, Amani, CA, do Amaral, DD, do Amaral, IL, Andrade, A, Miranda, IPD, Angoboy, IB, Araujo-Murakami, A, Arboleda, NC, Arroyo, L, Ashton, P, Aymard, CGA, Baider, C, Baker, TR, Balinga, MPB, Balslev, H, Banin, LF, Bánki, OS, Baraloto, C, Barbosa, EM, Barbosa, FR, Barlow, J, Bastin, JF, Beeckman, H, Begne, S, Bengone, NN, Berenguer, E, Berry, N, Bitariho, R, Boeckx, P, Bogaert, J, Bonyoma, B, Boundja, P, Bourland, N, Bosela, FB, Brambach, F, Brienen, R, Burslem, DFRP, Camargo, JL, Campelo, W, Cano, A, Cárdenas, S, López, DC, Carpanedo, RD, Márquez, YAC, Carvalho, FA, Casas, LF, Castellanos, H, Castilho, CV, Cerón, C, Chapman, CA, Chave, J, Chhang, P, Chutipong, W, Chuyong, GB, Cintra, BBL, Clark, CJ, de Souza, FC, Comiskey, JA, Coomes, DA, Valverde, FC, Correa, DF, Costa, FRC, Costa, JBP, Couteron, P, Culmsee, H, Cuni-Sanchez, A, Dallmeier, F, Damasco, G, Dauby, G, Dávila, N, Doza, HPD, De Alban, JDT, de Assis, RL, De Canniere, C, De Haulleville, T, Carim, MDV, Demarchi, LO, Dexter, KG, Di Fiore, A, Din, HHM, Disney, MI, Djiofack, BY, Djuikouo, MNK, Van Do, T, Doucet, JL, Draper, FC, Droissart, V, Duivenvoorden, JF, Engel, J, Estienne, V, Farfan-Rios, W, Fauset, S, Feeley, KJ, Feitosa, YO, Feldpausch, TR, Ferreira, C, Ferreira, J, Ferreira, LV, Fletcher, CD, Flores, BM, Fofanah, A, Foli, EG, Fonty, E, Fredriksson, GM, Fuentes, A, Galbraith, D, Gonzales, GPG, Garcia-Cabrera, K, García-Villacorta, R, Gomes, VHF, Gómez, RZ, Gonzales, T, Gribel, R, Guedes, MC, Guevara, JE, Hakeem, KR, Hall, JS, Hamer, KC, Harrison, RD, Harris, DJ, Hart, TB, Hector, A, Henkel, TW, Herbohn, J, Hockemba, MBN, Hoffman, B, Holmgren, M, Coronado, ENH, Huamantupa-Chuquimaco, I, Hubau, W, Imai, N, Irume, MV, Jansen, PA, Jeffery, KJ, Jimenez, EM, Jucker, T, Junqueira, AB, Kalamandeen, M, Kamdem, NG, Kartawinata, K, Yakusu, EK, Katembo, JM, Kearsley, E, Kenfack, D, Kessler, M, Khaing, TT, Killeen, TJ, Kitayama, K, Klitgaard, B, Labriere, N, Laumonier, Y, Laurance, SGW, Laurance, WF, Laurent, F, Le, TC, Leal, ME, Novo, EMLD, Levesley, A, Libalah, MB, Licona, JC, Lima, DD, Lindsell, JA, Lopes, A, Lopes, MA, Lovett, JC, Lowe, R, Lozada, JR, Lu, XH, Luambua, NK, Luize, BG, Maas, P, Magalhaes, JLL, Magnusson, WE, Mahayani, NPD, Makana, JR, Malhi, Y, Rincón, LM, Mansor, A, Manzatto, AG, Marimon, BS, Marimon, BH Jr, Marshall, AR, Martins, MP, Mbayu, FM, de Medeiros, MB, Mesones, I, Metali, F, Mihindou, V, Millet, J, Milliken, W, Mogollon, HF, Molino, JF, Said, MNM, Mendoza, AM, Montero, JC, Moore, S, Mostacedo, B, Pinto, LFM, Mukul, SA, Munishi, PKT, Nagamasu, H, Nascimento, HEM, Nascimento, MT, Neill, D, Nilus, R, Noronha, JC, Nsenga, L, Vargas, PN, Ojo, L, Oliveira, AA, de Oliveira, EA, Ondo, FE, Cuenca, WP, Pansini, S, Pansonato, MP, Paredes, MR, Paudel, E, Pauletto, D, Pearson, RG, Pena, JLM, Pennington, RT, Peres, CA, Permana, A, Petronelli, P, Mora, MCP, Phillips, JF, Phillips, OL, Pickavance, G, Piedade, MTF, Pitman, NCA, Ploton, P, Popelier, A, Poulsen, JR, Prieto, A, Primack, RB, Priyadi, H, Qie, L, Quaresma, AC, de Queiroz, HL, Ramirez-Angulo, H, Ramos, JF, Reis, NFC, Reitsma, J, Revilla, JDC, Riutta, T, Rivas-Torres, G, Robiansyah, I, Rocha, M, Rodrigues, DD, Rodriguez-Ronderos, ME, Rovero, F, Rozak, AH, Rudas, A, Rutishauser, E, Sabatier, D, Sagang, L, Sampaio, AF, Samsoedin, I, Satdichanh, M, Schietti, J, Schöngart, J, Scudeller, VV, Seuaturien, N, Sheil, D, Sierra, R, Silman, MR, Silva, TSF, Guimaraes, JRD, Simo-Droissart, M, Simon, MF, Sist, P, Sousa, TR, Farias, ED, Coelho, LD, Spracklen, DV, Stas, SM, Steinmetz, R, Stevenson, PR, Stropp, J, Sukri, RS, Sunderland, TCH, Suzuki, E, Swaine, MD, Tang, JW, Taplin, J, Taylor, DM, Tello, JS, Terborgh, J, Texier, N, Theilade, I, Thomas, DW, Thomas, R, Thomas, SC, Tirado, M, Toirambe, B, de Toledo, JJ, Tomlinson, KW, Torres-Lezama, A, Tran, HD, Mukendi, JT, Tumaneng, RD, Umaña, MN, Umunay, PM, Giraldo, LEU, Sandoval, EHV, Gamarra, LV, Van Andel, TR, van de Bult, M, van de Pol, J, van der Heijden, G, Vasquez, R, Vela, CIA, Venticinque, EM, Verbeeck, H, Veridiano, RKA, Vicentini, A, Vieira, ICG, Torre, EV, Villarroel, D, Zegarra, BEV, Vleminckx, J, von Hildebrand, P, Vos, VA, Vriesendorp, C, Webb, EL, White, LJT, Wich, S, Wittmann, F, Zagt, R, Zang, RG, Zartman, CE, Zemagho, L, Zent, EL, and Zent, S
Abstract: Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations 1–6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories 7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.
Published: 2024

3. Consistent patterns of common species across tropical tree communities

Author: Sub Ecology and Biodiversity, Ecology and Biodiversity, Cooper, DLM, Lewis, SL, Sullivan, MJP, Prado, PI, ter Steege, H, Barbier, N, Slik, F, Sonké, B, Ewango, CEN, Adu-Bredu, S, Affum-Baffoe, K, de Aguiar, DPP, Reategui, MAA, Aiba, SI, Albuquerque, BW, Matos, FDD, Alonso, A, Amani, CA, do Amaral, DD, do Amaral, IL, Andrade, A, Miranda, IPD, Angoboy, IB, Araujo-Murakami, A, Arboleda, NC, Arroyo, L, Ashton, P, Aymard, CGA, Baider, C, Baker, TR, Balinga, MPB, Balslev, H, Banin, LF, Bánki, OS, Baraloto, C, Barbosa, EM, Barbosa, FR, Barlow, J, Bastin, JF, Beeckman, H, Begne, S, Bengone, NN, Berenguer, E, Berry, N, Bitariho, R, Boeckx, P, Bogaert, J, Bonyoma, B, Boundja, P, Bourland, N, Bosela, FB, Brambach, F, Brienen, R, Burslem, DFRP, Camargo, JL, Campelo, W, Cano, A, Cárdenas, S, López, DC, Carpanedo, RD, Márquez, YAC, Carvalho, FA, Casas, LF, Castellanos, H, Castilho, CV, Cerón, C, Chapman, CA, Chave, J, Chhang, P, Chutipong, W, Chuyong, GB, Cintra, BBL, Clark, CJ, de Souza, FC, Comiskey, JA, Coomes, DA, Valverde, FC, Correa, DF, Costa, FRC, Costa, JBP, Couteron, P, Culmsee, H, Cuni-Sanchez, A, Dallmeier, F, Damasco, G, Dauby, G, Dávila, N, Doza, HPD, De Alban, JDT, de Assis, RL, De Canniere, C, De Haulleville, T, Carim, MDV, Demarchi, LO, Dexter, KG, Di Fiore, A, Din, HHM, Disney, MI, Djiofack, BY, Djuikouo, MNK, Van Do, T, Doucet, JL, Draper, FC, Droissart, V, Duivenvoorden, JF, Engel, J, Estienne, V, Farfan-Rios, W, Fauset, S, Feeley, KJ, Feitosa, YO, Feldpausch, TR, Ferreira, C, Ferreira, J, Ferreira, LV, Fletcher, CD, Flores, BM, Fofanah, A, Foli, EG, Fonty, E, Fredriksson, GM, Fuentes, A, Galbraith, D, Gonzales, GPG, Garcia-Cabrera, K, García-Villacorta, R, Gomes, VHF, Gómez, RZ, Gonzales, T, Gribel, R, Guedes, MC, Guevara, JE, Hakeem, KR, Hall, JS, Hamer, KC, Harrison, RD, Harris, DJ, Hart, TB, Hector, A, Henkel, TW, Herbohn, J, Hockemba, MBN, Hoffman, B, Holmgren, M, Coronado, ENH, Huamantupa-Chuquimaco, I, Hubau, W, Imai, N, Irume, MV, Jansen, PA, Jeffery, KJ, Jimenez, EM, Jucker, T, Junqueira, AB, Kalamandeen, M, Kamdem, NG, Kartawinata, K, Yakusu, EK, Katembo, JM, Kearsley, E, Kenfack, D, Kessler, M, Khaing, TT, Killeen, TJ, Kitayama, K, Klitgaard, B, Labriere, N, Laumonier, Y, Laurance, SGW, Laurance, WF, Laurent, F, Le, TC, Leal, ME, Novo, EMLD, Levesley, A, Libalah, MB, Licona, JC, Lima, DD, Lindsell, JA, Lopes, A, Lopes, MA, Lovett, JC, Lowe, R, Lozada, JR, Lu, XH, Luambua, NK, Luize, BG, Maas, P, Magalhaes, JLL, Magnusson, WE, Mahayani, NPD, Makana, JR, Malhi, Y, Rincón, LM, Mansor, A, Manzatto, AG, Marimon, BS, Marimon, BH Jr, Marshall, AR, Martins, MP, Mbayu, FM, de Medeiros, MB, Mesones, I, Metali, F, Mihindou, V, Millet, J, Milliken, W, Mogollon, HF, Molino, JF, Said, MNM, Mendoza, AM, Montero, JC, Moore, S, Mostacedo, B, Pinto, LFM, Mukul, SA, Munishi, PKT, Nagamasu, H, Nascimento, HEM, Nascimento, MT, Neill, D, Nilus, R, Noronha, JC, Nsenga, L, Vargas, PN, Ojo, L, Oliveira, AA, de Oliveira, EA, Ondo, FE, Cuenca, WP, Pansini, S, Pansonato, MP, Paredes, MR, Paudel, E, Pauletto, D, Pearson, RG, Pena, JLM, Pennington, RT, Peres, CA, Permana, A, Petronelli, P, Mora, MCP, Phillips, JF, Phillips, OL, Pickavance, G, Piedade, MTF, Pitman, NCA, Ploton, P, Popelier, A, Poulsen, JR, Prieto, A, Primack, RB, Priyadi, H, Qie, L, Quaresma, AC, de Queiroz, HL, Ramirez-Angulo, H, Ramos, JF, Reis, NFC, Reitsma, J, Revilla, JDC, Riutta, T, Rivas-Torres, G, Robiansyah, I, Rocha, M, Rodrigues, DD, Rodriguez-Ronderos, ME, Rovero, F, Rozak, AH, Rudas, A, Rutishauser, E, Sabatier, D, Sagang, L, Sampaio, AF, Samsoedin, I, Satdichanh, M, Schietti, J, Schöngart, J, Scudeller, VV, Seuaturien, N, Sheil, D, Sierra, R, Silman, MR, Silva, TSF, Guimaraes, JRD, Simo-Droissart, M, Simon, MF, Sist, P, Sousa, TR, Farias, ED, Coelho, LD, Spracklen, DV, Stas, SM, Steinmetz, R, Stevenson, PR, Stropp, J, Sukri, RS, Sunderland, TCH, Suzuki, E, Swaine, MD, Tang, JW, Taplin, J, Taylor, DM, Tello, JS, Terborgh, J, Texier, N, Theilade, I, Thomas, DW, Thomas, R, Thomas, SC, Tirado, M, Toirambe, B, de Toledo, JJ, Tomlinson, KW, Torres-Lezama, A, Tran, HD, Mukendi, JT, Tumaneng, RD, Umaña, MN, Umunay, PM, Giraldo, LEU, Sandoval, EHV, Gamarra, LV, Van Andel, TR, van de Bult, M, van de Pol, J, van der Heijden, G, Vasquez, R, Vela, CIA, Venticinque, EM, Verbeeck, H, Veridiano, RKA, Vicentini, A, Vieira, ICG, Torre, EV, Villarroel, D, Zegarra, BEV, Vleminckx, J, von Hildebrand, P, Vos, VA, Vriesendorp, C, Webb, EL, White, LJT, Wich, S, Wittmann, F, Zagt, R, Zang, RG, Zartman, CE, Zemagho, L, Zent, EL, Zent, S, Sub Ecology and Biodiversity, Ecology and Biodiversity, Cooper, DLM, Lewis, SL, Sullivan, MJP, Prado, PI, ter Steege, H, Barbier, N, Slik, F, Sonké, B, Ewango, CEN, Adu-Bredu, S, Affum-Baffoe, K, de Aguiar, DPP, Reategui, MAA, Aiba, SI, Albuquerque, BW, Matos, FDD, Alonso, A, Amani, CA, do Amaral, DD, do Amaral, IL, Andrade, A, Miranda, IPD, Angoboy, IB, Araujo-Murakami, A, Arboleda, NC, Arroyo, L, Ashton, P, Aymard, CGA, Baider, C, Baker, TR, Balinga, MPB, Balslev, H, Banin, LF, Bánki, OS, Baraloto, C, Barbosa, EM, Barbosa, FR, Barlow, J, Bastin, JF, Beeckman, H, Begne, S, Bengone, NN, Berenguer, E, Berry, N, Bitariho, R, Boeckx, P, Bogaert, J, Bonyoma, B, Boundja, P, Bourland, N, Bosela, FB, Brambach, F, Brienen, R, Burslem, DFRP, Camargo, JL, Campelo, W, Cano, A, Cárdenas, S, López, DC, Carpanedo, RD, Márquez, YAC, Carvalho, FA, Casas, LF, Castellanos, H, Castilho, CV, Cerón, C, Chapman, CA, Chave, J, Chhang, P, Chutipong, W, Chuyong, GB, Cintra, BBL, Clark, CJ, de Souza, FC, Comiskey, JA, Coomes, DA, Valverde, FC, Correa, DF, Costa, FRC, Costa, JBP, Couteron, P, Culmsee, H, Cuni-Sanchez, A, Dallmeier, F, Damasco, G, Dauby, G, Dávila, N, Doza, HPD, De Alban, JDT, de Assis, RL, De Canniere, C, De Haulleville, T, Carim, MDV, Demarchi, LO, Dexter, KG, Di Fiore, A, Din, HHM, Disney, MI, Djiofack, BY, Djuikouo, MNK, Van Do, T, Doucet, JL, Draper, FC, Droissart, V, Duivenvoorden, JF, Engel, J, Estienne, V, Farfan-Rios, W, Fauset, S, Feeley, KJ, Feitosa, YO, Feldpausch, TR, Ferreira, C, Ferreira, J, Ferreira, LV, Fletcher, CD, Flores, BM, Fofanah, A, Foli, EG, Fonty, E, Fredriksson, GM, Fuentes, A, Galbraith, D, Gonzales, GPG, Garcia-Cabrera, K, García-Villacorta, R, Gomes, VHF, Gómez, RZ, Gonzales, T, Gribel, R, Guedes, MC, Guevara, JE, Hakeem, KR, Hall, JS, Hamer, KC, Harrison, RD, Harris, DJ, Hart, TB, Hector, A, Henkel, TW, Herbohn, J, Hockemba, MBN, Hoffman, B, Holmgren, M, Coronado, ENH, Huamantupa-Chuquimaco, I, Hubau, W, Imai, N, Irume, MV, Jansen, PA, Jeffery, KJ, Jimenez, EM, Jucker, T, Junqueira, AB, Kalamandeen, M, Kamdem, NG, Kartawinata, K, Yakusu, EK, Katembo, JM, Kearsley, E, Kenfack, D, Kessler, M, Khaing, TT, Killeen, TJ, Kitayama, K, Klitgaard, B, Labriere, N, Laumonier, Y, Laurance, SGW, Laurance, WF, Laurent, F, Le, TC, Leal, ME, Novo, EMLD, Levesley, A, Libalah, MB, Licona, JC, Lima, DD, Lindsell, JA, Lopes, A, Lopes, MA, Lovett, JC, Lowe, R, Lozada, JR, Lu, XH, Luambua, NK, Luize, BG, Maas, P, Magalhaes, JLL, Magnusson, WE, Mahayani, NPD, Makana, JR, Malhi, Y, Rincón, LM, Mansor, A, Manzatto, AG, Marimon, BS, Marimon, BH Jr, Marshall, AR, Martins, MP, Mbayu, FM, de Medeiros, MB, Mesones, I, Metali, F, Mihindou, V, Millet, J, Milliken, W, Mogollon, HF, Molino, JF, Said, MNM, Mendoza, AM, Montero, JC, Moore, S, Mostacedo, B, Pinto, LFM, Mukul, SA, Munishi, PKT, Nagamasu, H, Nascimento, HEM, Nascimento, MT, Neill, D, Nilus, R, Noronha, JC, Nsenga, L, Vargas, PN, Ojo, L, Oliveira, AA, de Oliveira, EA, Ondo, FE, Cuenca, WP, Pansini, S, Pansonato, MP, Paredes, MR, Paudel, E, Pauletto, D, Pearson, RG, Pena, JLM, Pennington, RT, Peres, CA, Permana, A, Petronelli, P, Mora, MCP, Phillips, JF, Phillips, OL, Pickavance, G, Piedade, MTF, Pitman, NCA, Ploton, P, Popelier, A, Poulsen, JR, Prieto, A, Primack, RB, Priyadi, H, Qie, L, Quaresma, AC, de Queiroz, HL, Ramirez-Angulo, H, Ramos, JF, Reis, NFC, Reitsma, J, Revilla, JDC, Riutta, T, Rivas-Torres, G, Robiansyah, I, Rocha, M, Rodrigues, DD, Rodriguez-Ronderos, ME, Rovero, F, Rozak, AH, Rudas, A, Rutishauser, E, Sabatier, D, Sagang, L, Sampaio, AF, Samsoedin, I, Satdichanh, M, Schietti, J, Schöngart, J, Scudeller, VV, Seuaturien, N, Sheil, D, Sierra, R, Silman, MR, Silva, TSF, Guimaraes, JRD, Simo-Droissart, M, Simon, MF, Sist, P, Sousa, TR, Farias, ED, Coelho, LD, Spracklen, DV, Stas, SM, Steinmetz, R, Stevenson, PR, Stropp, J, Sukri, RS, Sunderland, TCH, Suzuki, E, Swaine, MD, Tang, JW, Taplin, J, Taylor, DM, Tello, JS, Terborgh, J, Texier, N, Theilade, I, Thomas, DW, Thomas, R, Thomas, SC, Tirado, M, Toirambe, B, de Toledo, JJ, Tomlinson, KW, Torres-Lezama, A, Tran, HD, Mukendi, JT, Tumaneng, RD, Umaña, MN, Umunay, PM, Giraldo, LEU, Sandoval, EHV, Gamarra, LV, Van Andel, TR, van de Bult, M, van de Pol, J, van der Heijden, G, Vasquez, R, Vela, CIA, Venticinque, EM, Verbeeck, H, Veridiano, RKA, Vicentini, A, Vieira, ICG, Torre, EV, Villarroel, D, Zegarra, BEV, Vleminckx, J, von Hildebrand, P, Vos, VA, Vriesendorp, C, Webb, EL, White, LJT, Wich, S, Wittmann, F, Zagt, R, Zang, RG, Zartman, CE, Zemagho, L, Zent, EL, and Zent, S
Published: 2024

4. The number of tree species on Earth

Author: Gatti, RC, Reich, PB, Gamarra, JGP, Crowther, T, Hui, C, Morera, A, Bastin, JF, de-Miguel, S, Nabuurs, GJ, Svenning, JC, Serra-Diaz, JM, Merow, C, Enquist, B, Kamenetsky, M, Lee, J, Zhu, J, Fang, J, Jacobs, DF, Pijanowski, B, Banerjee, A, Giaquinto, RA, Alberti, G, Zambrano, AMA, Alvarez-Davila, E, Araujo-Murakami, A, Avitabile, V, Aymard, GA, Balazy, R, Baraloto, C, Barroso, JG, Bastian, ML, Birnbaum, P, Bitariho, R, Bogaert, J, Bongers, F, Bouriaud, O, Brancalion, PHS, Brearley, FQ, Broadbent, EN, Bussotti, F, da Silva, WC, César, RG, Češljar, G, Moscoso, VC, Chen, HYH, Cienciala, E, Clark, CJ, Coomes, DA, Dayanandan, S, Decuyper, M, Dee, LE, Del Aguila Pasquel, J, Derroire, G, Djuikouo, MNK, van Do, T, Dolezal, J, Đorđevic, I, Engel, J, Fayle, TM, Feldpausch, TR, Fridman, JK, Harris, DJ, Hemp, A, Hengeveld, G, Herault, B, Herold, M, Ibanez, T, Jagodzinski, AM, Jaroszewicz, B, Jeffery, KJ, Johannsen, VK, Jucker, T, Kangur, A, Karminov, VN, Kartawinata, K, Kennard, DK, Kepfer-Rojas, S, Keppel, G, Khan, ML, Khare, PK, Kileen, TJ, Kim, HS, Korjus, H, Kumar, A, Laarmann, D, Labrière, N, Lang, M, Lewis, SL, Lukina, N, Maitner, BS, Malhi, Y, Marshall, AR, Martynenko, OV, Monteagudo Mendoza, AL, Ontikov, PV, Ortiz-Malavasi, E, Pallqui Camacho, NC, Paquette, A, Park, M, Gatti, RC, Reich, PB, Gamarra, JGP, Crowther, T, Hui, C, Morera, A, Bastin, JF, de-Miguel, S, Nabuurs, GJ, Svenning, JC, Serra-Diaz, JM, Merow, C, Enquist, B, Kamenetsky, M, Lee, J, Zhu, J, Fang, J, Jacobs, DF, Pijanowski, B, Banerjee, A, Giaquinto, RA, Alberti, G, Zambrano, AMA, Alvarez-Davila, E, Araujo-Murakami, A, Avitabile, V, Aymard, GA, Balazy, R, Baraloto, C, Barroso, JG, Bastian, ML, Birnbaum, P, Bitariho, R, Bogaert, J, Bongers, F, Bouriaud, O, Brancalion, PHS, Brearley, FQ, Broadbent, EN, Bussotti, F, da Silva, WC, César, RG, Češljar, G, Moscoso, VC, Chen, HYH, Cienciala, E, Clark, CJ, Coomes, DA, Dayanandan, S, Decuyper, M, Dee, LE, Del Aguila Pasquel, J, Derroire, G, Djuikouo, MNK, van Do, T, Dolezal, J, Đorđevic, I, Engel, J, Fayle, TM, Feldpausch, TR, Fridman, JK, Harris, DJ, Hemp, A, Hengeveld, G, Herault, B, Herold, M, Ibanez, T, Jagodzinski, AM, Jaroszewicz, B, Jeffery, KJ, Johannsen, VK, Jucker, T, Kangur, A, Karminov, VN, Kartawinata, K, Kennard, DK, Kepfer-Rojas, S, Keppel, G, Khan, ML, Khare, PK, Kileen, TJ, Kim, HS, Korjus, H, Kumar, A, Laarmann, D, Labrière, N, Lang, M, Lewis, SL, Lukina, N, Maitner, BS, Malhi, Y, Marshall, AR, Martynenko, OV, Monteagudo Mendoza, AL, Ontikov, PV, Ortiz-Malavasi, E, Pallqui Camacho, NC, Paquette, A, and Park, M
Abstract: One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.
Published: 2022

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

Author: Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C
Abstract: 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 AG
Published: 2022

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

Author: Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C
Published: 2022

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

Author: Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C
Abstract: 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 AG
Published: 2022

8. Taking the pulse of Earth's tropical forests using networks of highly distributed plots

Author: Blundo, C, Carilla, J, Grau, R, Malizia, A, Malizia, L, Osinaga-Acosta, O, Bird, M, Bradford, M, Catchpole, D, Ford, A, Graham, A, Hilbert, D, Kemp, J, Laurance, S, Laurance, W, Ishida, FY, Marshall, A, Waite, C, Woell, H, Bastin, JF, Bauters, M, Beeckman, H, Boeckx, P, Bogaert, J, De Canniere, C, de Haulleville, T, Doucet, JL, Hardy, O, Hubau, W, Kearsley, E, Verbeeck, H, Vleminckx, J, Brewer, SW, Alarcón, A, Araujo-Murakami, A, Arets, E, Arroyo, L, Chavez, E, Fredericksen, T, Villaroel, RG, Sibauty, GG, Killeen, T, Licona, JC, Lleigue, J, Mendoza, C, Murakami, S, Gutierrez, AP, Pardo, G, Peña-Claros, M, Poorter, L, Toledo, M, Cayo, JV, Viscarra, LJ, Vos, V, Ahumada, J, Almeida, E, Almeida, J, de Oliveira, EA, da Cruz, WA, de Oliveira, AA, Carvalho, FA, Obermuller, FA, Andrade, A, Vieira, SA, Aquino, AC, Aragão, L, Araújo, AC, Assis, MA, Gomes, JAMA, Baccaro, F, de Camargo, PB, Barni, P, Barroso, J, Bernacci, LC, Bordin, K, de Medeiros, MB, Broggio, I, Camargo, JL, Cardoso, D, Carniello, MA, Rochelle, ALC, Castilho, C, Castro, AAJF, Castro, W, Ribeiro, SC, Costa, F, de Oliveira, RC, Coutinho, I, Cunha, J, da Costa, L, da Costa Ferreira, L, da Costa Silva, R, da Graça Zacarias Simbine, M, de Andrade Kamimura, V, de Lima, HC, de Oliveira Melo, L, de Queiroz, L, de Sousa Lima, JR, do Espírito Santo, M, Blundo, C, Carilla, J, Grau, R, Malizia, A, Malizia, L, Osinaga-Acosta, O, Bird, M, Bradford, M, Catchpole, D, Ford, A, Graham, A, Hilbert, D, Kemp, J, Laurance, S, Laurance, W, Ishida, FY, Marshall, A, Waite, C, Woell, H, Bastin, JF, Bauters, M, Beeckman, H, Boeckx, P, Bogaert, J, De Canniere, C, de Haulleville, T, Doucet, JL, Hardy, O, Hubau, W, Kearsley, E, Verbeeck, H, Vleminckx, J, Brewer, SW, Alarcón, A, Araujo-Murakami, A, Arets, E, Arroyo, L, Chavez, E, Fredericksen, T, Villaroel, RG, Sibauty, GG, Killeen, T, Licona, JC, Lleigue, J, Mendoza, C, Murakami, S, Gutierrez, AP, Pardo, G, Peña-Claros, M, Poorter, L, Toledo, M, Cayo, JV, Viscarra, LJ, Vos, V, Ahumada, J, Almeida, E, Almeida, J, de Oliveira, EA, da Cruz, WA, de Oliveira, AA, Carvalho, FA, Obermuller, FA, Andrade, A, Vieira, SA, Aquino, AC, Aragão, L, Araújo, AC, Assis, MA, Gomes, JAMA, Baccaro, F, de Camargo, PB, Barni, P, Barroso, J, Bernacci, LC, Bordin, K, de Medeiros, MB, Broggio, I, Camargo, JL, Cardoso, D, Carniello, MA, Rochelle, ALC, Castilho, C, Castro, AAJF, Castro, W, Ribeiro, SC, Costa, F, de Oliveira, RC, Coutinho, I, Cunha, J, da Costa, L, da Costa Ferreira, L, da Costa Silva, R, da Graça Zacarias Simbine, M, de Andrade Kamimura, V, de Lima, HC, de Oliveira Melo, L, de Queiroz, L, de Sousa Lima, JR, and do Espírito Santo, M
Abstract: Tropical forests are the most diverse and productive ecosystems on Earth. While better understanding of these forests is critical for our collective future, until quite recently efforts to measure and monitor them have been largely disconnected. Networking is essential to discover the answers to questions that transcend borders and the horizons of funding agencies. Here we show how a global community is responding to the challenges of tropical ecosystem research with diverse teams measuring forests tree-by-tree in thousands of long-term plots. We review the major scientific discoveries of this work and show how this process is changing tropical forest science. Our core approach involves linking long-term grassroots initiatives with standardized protocols and data management to generate robust scaled-up results. By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots.net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change. By leveraging a remarkably old technology, plot networks are sparking a very modern revolution in tropical forest sc
Published: 2021

9. Taking the pulse of Earth's tropical forests using networks of highly distributed plots

Author: Blundo, C, Carilla, J, Grau, R, Malizia, A, Malizia, L, Osinaga-Acosta, O, Bird, M, Bradford, M, Catchpole, D, Ford, A, Graham, A, Hilbert, D, Kemp, J, Laurance, S, Laurance, W, Ishida, FY, Marshall, A, Waite, C, Woell, H, Bastin, JF, Bauters, M, Beeckman, H, Boeckx, P, Bogaert, J, De Canniere, C, de Haulleville, T, Doucet, JL, Hardy, O, Hubau, W, Kearsley, E, Verbeeck, H, Vleminckx, J, Brewer, SW, Alarcón, A, Araujo-Murakami, A, Arets, E, Arroyo, L, Chavez, E, Fredericksen, T, Villaroel, RG, Sibauty, GG, Killeen, T, Licona, JC, Lleigue, J, Mendoza, C, Murakami, S, Gutierrez, AP, Pardo, G, Peña-Claros, M, Poorter, L, Toledo, M, Cayo, JV, Viscarra, LJ, Vos, V, Ahumada, J, Almeida, E, Almeida, J, de Oliveira, EA, da Cruz, WA, de Oliveira, AA, Carvalho, FA, Obermuller, FA, Andrade, A, Vieira, SA, Aquino, AC, Aragão, L, Araújo, AC, Assis, MA, Gomes, JAMA, Baccaro, F, de Camargo, PB, Barni, P, Barroso, J, Bernacci, LC, Bordin, K, de Medeiros, MB, Broggio, I, Camargo, JL, Cardoso, D, Carniello, MA, Rochelle, ALC, Castilho, C, Castro, AAJF, Castro, W, Ribeiro, SC, Costa, F, de Oliveira, RC, Coutinho, I, Cunha, J, da Costa, L, da Costa Ferreira, L, da Costa Silva, R, da Graça Zacarias Simbine, M, de Andrade Kamimura, V, de Lima, HC, de Oliveira Melo, L, de Queiroz, L, de Sousa Lima, JR, do Espírito Santo, M, Blundo, C, Carilla, J, Grau, R, Malizia, A, Malizia, L, Osinaga-Acosta, O, Bird, M, Bradford, M, Catchpole, D, Ford, A, Graham, A, Hilbert, D, Kemp, J, Laurance, S, Laurance, W, Ishida, FY, Marshall, A, Waite, C, Woell, H, Bastin, JF, Bauters, M, Beeckman, H, Boeckx, P, Bogaert, J, De Canniere, C, de Haulleville, T, Doucet, JL, Hardy, O, Hubau, W, Kearsley, E, Verbeeck, H, Vleminckx, J, Brewer, SW, Alarcón, A, Araujo-Murakami, A, Arets, E, Arroyo, L, Chavez, E, Fredericksen, T, Villaroel, RG, Sibauty, GG, Killeen, T, Licona, JC, Lleigue, J, Mendoza, C, Murakami, S, Gutierrez, AP, Pardo, G, Peña-Claros, M, Poorter, L, Toledo, M, Cayo, JV, Viscarra, LJ, Vos, V, Ahumada, J, Almeida, E, Almeida, J, de Oliveira, EA, da Cruz, WA, de Oliveira, AA, Carvalho, FA, Obermuller, FA, Andrade, A, Vieira, SA, Aquino, AC, Aragão, L, Araújo, AC, Assis, MA, Gomes, JAMA, Baccaro, F, de Camargo, PB, Barni, P, Barroso, J, Bernacci, LC, Bordin, K, de Medeiros, MB, Broggio, I, Camargo, JL, Cardoso, D, Carniello, MA, Rochelle, ALC, Castilho, C, Castro, AAJF, Castro, W, Ribeiro, SC, Costa, F, de Oliveira, RC, Coutinho, I, Cunha, J, da Costa, L, da Costa Ferreira, L, da Costa Silva, R, da Graça Zacarias Simbine, M, de Andrade Kamimura, V, de Lima, HC, de Oliveira Melo, L, de Queiroz, L, de Sousa Lima, JR, and do Espírito Santo, M
Abstract: Tropical forests are the most diverse and productive ecosystems on Earth. While better understanding of these forests is critical for our collective future, until quite recently efforts to measure and monitor them have been largely disconnected. Networking is essential to discover the answers to questions that transcend borders and the horizons of funding agencies. Here we show how a global community is responding to the challenges of tropical ecosystem research with diverse teams measuring forests tree-by-tree in thousands of long-term plots. We review the major scientific discoveries of this work and show how this process is changing tropical forest science. Our core approach involves linking long-term grassroots initiatives with standardized protocols and data management to generate robust scaled-up results. By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots.net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change. By leveraging a remarkably old technology, plot networks are sparking a very modern revolution in tropical forest sc
Published: 2021

10. The global abundance of tree palms

Author: Muscarella, R, Muscarella, R, Emilio, T, Phillips, OL, Lewis, SL, Slik, F, Baker, WJ, Couvreur, TLP, Eiserhardt, WL, Svenning, JC, Affum-Baffoe, K, Aiba, SI, de Almeida, EC, de Almeida, SS, de Oliveira, EA, Álvarez-Dávila, E, Alves, LF, Alvez-Valles, CM, Carvalho, FA, Guarin, FA, Andrade, A, Aragão, LEOC, Murakami, AA, Arroyo, L, Ashton, PS, Corredor, GAA, Baker, TR, de Camargo, PB, Barlow, J, Bastin, JF, Bengone, NN, Berenguer, E, Berry, N, Blanc, L, Böhning-Gaese, K, Bonal, D, Bongers, F, Bradford, M, Brambach, F, Brearley, FQ, Brewer, SW, Camargo, JLC, Campbell, DG, Castilho, CV, Castro, W, Catchpole, D, Cerón Martínez, CE, Chen, S, Chhang, P, Cho, P, Chutipong, W, Clark, C, Collins, M, Comiskey, JA, Medina, MNC, Costa, FRC, Culmsee, H, David-Higuita, H, Davidar, P, del Aguila-Pasquel, J, Derroire, G, Di Fiore, A, Van Do, T, Doucet, JL, Dourdain, A, Drake, DR, Ensslin, A, Erwin, T, Ewango, CEN, Ewers, RM, Fauset, S, Feldpausch, TR, Ferreira, J, Ferreira, LV, Fischer, M, Franklin, J, Fredriksson, GM, Gillespie, TW, Gilpin, M, Gonmadje, C, Gunatilleke, AUN, Hakeem, KR, Hall, JS, Hamer, KC, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Herault, B, Pizango, CGH, Coronado, ENH, Hubau, W, Hussain, MS, Ibrahim, FH, Imai, N, Joly, CA, Joseph, S, Anitha, K, Kartawinata, K, Kassi, J, Killeen, TJ, Muscarella, R, Muscarella, R, Emilio, T, Phillips, OL, Lewis, SL, Slik, F, Baker, WJ, Couvreur, TLP, Eiserhardt, WL, Svenning, JC, Affum-Baffoe, K, Aiba, SI, de Almeida, EC, de Almeida, SS, de Oliveira, EA, Álvarez-Dávila, E, Alves, LF, Alvez-Valles, CM, Carvalho, FA, Guarin, FA, Andrade, A, Aragão, LEOC, Murakami, AA, Arroyo, L, Ashton, PS, Corredor, GAA, Baker, TR, de Camargo, PB, Barlow, J, Bastin, JF, Bengone, NN, Berenguer, E, Berry, N, Blanc, L, Böhning-Gaese, K, Bonal, D, Bongers, F, Bradford, M, Brambach, F, Brearley, FQ, Brewer, SW, Camargo, JLC, Campbell, DG, Castilho, CV, Castro, W, Catchpole, D, Cerón Martínez, CE, Chen, S, Chhang, P, Cho, P, Chutipong, W, Clark, C, Collins, M, Comiskey, JA, Medina, MNC, Costa, FRC, Culmsee, H, David-Higuita, H, Davidar, P, del Aguila-Pasquel, J, Derroire, G, Di Fiore, A, Van Do, T, Doucet, JL, Dourdain, A, Drake, DR, Ensslin, A, Erwin, T, Ewango, CEN, Ewers, RM, Fauset, S, Feldpausch, TR, Ferreira, J, Ferreira, LV, Fischer, M, Franklin, J, Fredriksson, GM, Gillespie, TW, Gilpin, M, Gonmadje, C, Gunatilleke, AUN, Hakeem, KR, Hall, JS, Hamer, KC, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Herault, B, Pizango, CGH, Coronado, ENH, Hubau, W, Hussain, MS, Ibrahim, FH, Imai, N, Joly, CA, Joseph, S, Anitha, K, Kartawinata, K, Kassi, J, and Killeen, TJ
Abstract: Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location: Tropical and subtropical moist forests. Time period: Current. Major taxa studied: Palms (Arecaceae). Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the magnitude and direction of the effect require additional work. Conclusions: Tree palms are not only quintessentially tropical, but they are also overwh
Published: 2020

11. The global abundance of tree palms

Author: Muscarella, R, McGeoch, Melodie1, Muscarella, R, Emilio, T, Phillips, OL, Lewis, SL, Slik, F, Baker, WJ, Couvreur, TLP, Eiserhardt, WL, Svenning, JC, Affum-Baffoe, K, Aiba, SI, de Almeida, EC, de Almeida, SS, de Oliveira, EA, Álvarez-Dávila, E, Alves, LF, Alvez-Valles, CM, Carvalho, FA, Guarin, FA, Andrade, A, Aragão, LEOC, Murakami, AA, Arroyo, L, Ashton, PS, Corredor, GAA, Baker, TR, de Camargo, PB, Barlow, J, Bastin, JF, Bengone, NN, Berenguer, E, Berry, N, Blanc, L, Böhning-Gaese, K, Bonal, D, Bongers, F, Bradford, M, Brambach, F, Brearley, FQ, Brewer, SW, Camargo, JLC, Campbell, DG, Castilho, CV, Castro, W, Catchpole, D, Cerón Martínez, CE, Chen, S, Chhang, P, Cho, P, Chutipong, W, Clark, C, Collins, M, Comiskey, JA, Medina, MNC, Costa, FRC, Culmsee, H, David-Higuita, H, Davidar, P, del Aguila-Pasquel, J, Derroire, G, Di Fiore, A, Van Do, T, Doucet, JL, Dourdain, A, Drake, DR, Ensslin, A, Erwin, T, Ewango, CEN, Ewers, RM, Fauset, S, Feldpausch, TR, Ferreira, J, Ferreira, LV, Fischer, M, Franklin, J, Fredriksson, GM, Gillespie, TW, Gilpin, M, Gonmadje, C, Gunatilleke, AUN, Hakeem, KR, Hall, JS, Hamer, KC, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Herault, B, Pizango, CGH, Coronado, ENH, Hubau, W, Hussain, MS, Ibrahim, FH, Imai, N, Joly, CA, Joseph, S, Anitha, K, Kartawinata, K, Kassi, J, Killeen, TJ, Muscarella, R, McGeoch, Melodie1, Muscarella, R, Emilio, T, Phillips, OL, Lewis, SL, Slik, F, Baker, WJ, Couvreur, TLP, Eiserhardt, WL, Svenning, JC, Affum-Baffoe, K, Aiba, SI, de Almeida, EC, de Almeida, SS, de Oliveira, EA, Álvarez-Dávila, E, Alves, LF, Alvez-Valles, CM, Carvalho, FA, Guarin, FA, Andrade, A, Aragão, LEOC, Murakami, AA, Arroyo, L, Ashton, PS, Corredor, GAA, Baker, TR, de Camargo, PB, Barlow, J, Bastin, JF, Bengone, NN, Berenguer, E, Berry, N, Blanc, L, Böhning-Gaese, K, Bonal, D, Bongers, F, Bradford, M, Brambach, F, Brearley, FQ, Brewer, SW, Camargo, JLC, Campbell, DG, Castilho, CV, Castro, W, Catchpole, D, Cerón Martínez, CE, Chen, S, Chhang, P, Cho, P, Chutipong, W, Clark, C, Collins, M, Comiskey, JA, Medina, MNC, Costa, FRC, Culmsee, H, David-Higuita, H, Davidar, P, del Aguila-Pasquel, J, Derroire, G, Di Fiore, A, Van Do, T, Doucet, JL, Dourdain, A, Drake, DR, Ensslin, A, Erwin, T, Ewango, CEN, Ewers, RM, Fauset, S, Feldpausch, TR, Ferreira, J, Ferreira, LV, Fischer, M, Franklin, J, Fredriksson, GM, Gillespie, TW, Gilpin, M, Gonmadje, C, Gunatilleke, AUN, Hakeem, KR, Hall, JS, Hamer, KC, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Herault, B, Pizango, CGH, Coronado, ENH, Hubau, W, Hussain, MS, Ibrahim, FH, Imai, N, Joly, CA, Joseph, S, Anitha, K, Kartawinata, K, Kassi, J, and Killeen, TJ
Abstract: Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location: Tropical and subtropical moist forests. Time period: Current. Major taxa studied: Palms (Arecaceae). Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the magnitude and direction of the effect require additional work. Conclusions: Tree palms are not only quintessentially tropical, but they are also overwh
Published: 2020

12. Correction for Slik et al., Phylogenetic classification of the world’s tropical forests

Author: Slik, JWF, Slik, JWF, Franklin, J, Arroyo-Rodríguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, SI, Alves, LF, Anitha, K, Avella, A, Mora, F, Aymard, GAC, Báez, S, Balvanera, P, Bastian, ML, Bastin, JF, Bellingham, PJ, Van Den Berg, E, Da Conceição Bispo, P, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, FQ, Brown, S, Chai, SL, Chazdon, RL, Chen, S, Chhang, P, Chuyong, G, Ewango, C, Coronado, IM, Cristóbal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, HS, Davidar, P, DeWalt, SJ, DIn, H, Drake, DR, Duque, A, Durigan, G, Eichhorn, K, Eler, ES, Enoki, T, Ensslin, A, Fandohan, AB, Farwig, N, Feeley, KJ, Fischer, M, Forshed, O, Garcia, QS, Garkoti, SC, Gillespie, TW, Gillet, JF, Gonmadje, C, Granzow-De La Cerda, I, Griffith, DM, Grogan, J, Hakeem, KR, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Homeier, J, Hussain, MS, Ibarra-Manríquez, G, Hanum, IF, Imai, N, Jansen, PA, Joly, CA, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, DL, Kessler, M, Killeen, TJ, Kooyman, RM, Laumonier, Y, Laurance, SG, Laurance, WF, Lawes, MJ, Letcher, SG, Lindsell, J, Lovett, J, Lozada, J, Lu, X, Lykke, AM, Bin Mahmud, K, Mahayani, NPD, Mansor, A, Marshall, AR, Martin, EH, Matos, DCL, Meave, JA, Melo, FPL, Mendoza, ZHA, Metali, F, Slik, JWF, Slik, JWF, Franklin, J, Arroyo-Rodríguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, SI, Alves, LF, Anitha, K, Avella, A, Mora, F, Aymard, GAC, Báez, S, Balvanera, P, Bastian, ML, Bastin, JF, Bellingham, PJ, Van Den Berg, E, Da Conceição Bispo, P, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, FQ, Brown, S, Chai, SL, Chazdon, RL, Chen, S, Chhang, P, Chuyong, G, Ewango, C, Coronado, IM, Cristóbal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, HS, Davidar, P, DeWalt, SJ, DIn, H, Drake, DR, Duque, A, Durigan, G, Eichhorn, K, Eler, ES, Enoki, T, Ensslin, A, Fandohan, AB, Farwig, N, Feeley, KJ, Fischer, M, Forshed, O, Garcia, QS, Garkoti, SC, Gillespie, TW, Gillet, JF, Gonmadje, C, Granzow-De La Cerda, I, Griffith, DM, Grogan, J, Hakeem, KR, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Homeier, J, Hussain, MS, Ibarra-Manríquez, G, Hanum, IF, Imai, N, Jansen, PA, Joly, CA, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, DL, Kessler, M, Killeen, TJ, Kooyman, RM, Laumonier, Y, Laurance, SG, Laurance, WF, Lawes, MJ, Letcher, SG, Lindsell, J, Lovett, J, Lozada, J, Lu, X, Lykke, AM, Bin Mahmud, K, Mahayani, NPD, Mansor, A, Marshall, AR, Martin, EH, Matos, DCL, Meave, JA, Melo, FPL, Mendoza, ZHA, and Metali, F
Abstract: Correction for "Phylogenetic classification of the world's tropical forests, " by J. W. Ferry Slik, Janet Franklin, Víctor Arroyo- Rodríguez, Richard Field, Salomon Aguilar, Nikolay Aguirre, Jorge Ahumada, Shin-Ichiro Aiba, Luciana F. Alves, Anitha K, Andres Avella, Francisco Mora, Gerardo A. Aymard C., Selene B?ez, Patricia Balvanera, Meredith L. Bastian, Jean-François Bastin, Peter J. Bellingham, Eduardo van den Berg, Polyanna da Conceição Bispo, Pascal Boeckx, Katrin Boehning-Gaese, Frans Bongers, Brad Boyle, Fabian Brambach, Francis Q. Brearley, Sandra Brown, Shauna-Lee Chai, Robin L. Chazdon, Shengbin Chen, Phourin Chhang, George Chuyong, Corneille Ewango, Indiana M. Coronado, Jurgi Crist?bal-Azkarate, Heike Culmsee, Kipiro Damas, H. S. Dattaraja, Priya Davidar, Saara J. DeWalt, Hazimah Din, Donald R. Drake, Alvaro Duque, Giselda Durigan, Karl Eichhorn, Eduardo Schmidt Eler, Tsutomu Enoki, Andreas Ensslin, Adand? Belarmain Fandohan, Nina Farwig, Kenneth J. Feeley, Markus Fischer, Olle Forshed, Queila Souza Garcia, Satish Chandra Garkoti, Thomas W. Gillespie, Jean- Francois Gillet, Christelle Gonmadje, Iñigo Granzow-de la Cerda, Daniel M. Griffith, James Grogan, Khalid Rehman Hakeem, David J. Harris, Rhett D. Harrison, Andy Hector, Andreas Hemp, J?rgen Homeier, M. Shah Hussain, Guillermo Ibarra-Manríquez, I. Faridah Hanum, Nobuo Imai, Patrick A. Jansen, Carlos Alfredo Joly, Shijo Joseph, Kuswata Kartawinata, Elizabeth Kearsley, Daniel L. Kelly, Michael Kessler, Timothy J. Killeen, Robert M. Kooyman, Yves Laumonier, Susan G. Laurance, William F. Laurance, Michael J. Lawes, Susan G. Letcher, Jeremy Lindsell, Jon Lovett, Jose Lozada, Xinghui Lu, Anne Mette Lykke, Khairil Bin Mahmud, Ni Putu Diana Mahayani, Asyraf Mansor, Andrew R. Marshall, Emanuel H. Martin, Darley Calderado Leal Matos, Jorge A.Meave, Felipe P. L. Melo, Zhofre Huberto Aguirre Mendoza, Faizah Metali, Vincent P. Medjibe, Jean Paul Metzger, Thiago Metzker, D. Mohandass, Miguel A. Munguía-Rosas, Rodri
Published: 2018

13. Pan-tropical prediction of forest structure from the largest trees

Author: Bastin, JF, Rutishauser, E, Kellner, JR, Saatchi, S, Pélissier, R, Hérault, B, Slik, F, Bogaert, J, De Cannière, C, Marshall, AR, Poulsen, J, Alvarez-Loyayza, P, Andrade, A, Angbonga-Basia, A, Araujo-Murakami, A, Arroyo, L, Ayyappan, N, de Azevedo, CP, Banki, O, Barbier, N, Barroso, JG, Beeckman, H, Bitariho, R, Boeckx, P, Boehning-Gaese, K, Brandão, H, Brearley, FQ, Breuer Ndoundou Hockemba, M, Brienen, R, Camargo, JLC, Campos-Arceiz, A, Cassart, B, Chave, J, Chazdon, R, Chuyong, G, Clark, DB, Clark, CJ, Condit, R, Honorio Coronado, EN, Davidar, P, de Haulleville, T, Descroix, L, Doucet, JL, Dourdain, A, Droissart, V, Duncan, T, Silva Espejo, J, Espinosa, S, Farwig, N, Fayolle, A, Feldpausch, TR, Ferraz, A, Fletcher, C, Gajapersad, K, Gillet, JF, Amaral, ILD, Gonmadje, C, Grogan, J, Harris, D, Herzog, SK, Homeier, J, Hubau, W, Hubbell, SP, Hufkens, K, Hurtado, J, Kamdem, NG, Kearsley, E, Kenfack, D, Kessler, M, Labrière, N, Laumonier, Y, Laurance, S, Laurance, WF, Lewis, SL, Libalah, MB, Ligot, G, Lloyd, J, Lovejoy, TE, Malhi, Y, Marimon, BS, Marimon Junior, BH, Martin, EH, Matius, P, Bastin, JF, Rutishauser, E, Kellner, JR, Saatchi, S, Pélissier, R, Hérault, B, Slik, F, Bogaert, J, De Cannière, C, Marshall, AR, Poulsen, J, Alvarez-Loyayza, P, Andrade, A, Angbonga-Basia, A, Araujo-Murakami, A, Arroyo, L, Ayyappan, N, de Azevedo, CP, Banki, O, Barbier, N, Barroso, JG, Beeckman, H, Bitariho, R, Boeckx, P, Boehning-Gaese, K, Brandão, H, Brearley, FQ, Breuer Ndoundou Hockemba, M, Brienen, R, Camargo, JLC, Campos-Arceiz, A, Cassart, B, Chave, J, Chazdon, R, Chuyong, G, Clark, DB, Clark, CJ, Condit, R, Honorio Coronado, EN, Davidar, P, de Haulleville, T, Descroix, L, Doucet, JL, Dourdain, A, Droissart, V, Duncan, T, Silva Espejo, J, Espinosa, S, Farwig, N, Fayolle, A, Feldpausch, TR, Ferraz, A, Fletcher, C, Gajapersad, K, Gillet, JF, Amaral, ILD, Gonmadje, C, Grogan, J, Harris, D, Herzog, SK, Homeier, J, Hubau, W, Hubbell, SP, Hufkens, K, Hurtado, J, Kamdem, NG, Kearsley, E, Kenfack, D, Kessler, M, Labrière, N, Laumonier, Y, Laurance, S, Laurance, WF, Lewis, SL, Libalah, MB, Ligot, G, Lloyd, J, Lovejoy, TE, Malhi, Y, Marimon, BS, Marimon Junior, BH, Martin, EH, and Matius, P
Abstract: © 2018 John Wiley & Sons Ltd Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan-tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot- and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.
Published: 2018

14. Field methods for sampling tree height for tropical forest biomass estimation

Author: Sullivan, MJP, Lewis, SL, Hubau, W, Qie, L, Baker, TR, Banin, LF, Chave, J, Cuni-Sanchez, A, Feldpausch, TR, Lopez-Gonzalez, G, Arets, E, Ashton, P, Bastin, JF, Berry, NJ, Bogaert, J, Boot, R, Brearley, FQ, Brienen, R, Burslem, DFRP, de Canniere, C, Chudomelová, M, Dančák, M, Ewango, C, Hédl, R, Lloyd, J, Makana, JR, Malhi, Y, Marimon, BS, Junior, BHM, Metali, F, Moore, S, Nagy, L, Vargas, PN, Pendry, CA, Ramírez-Angulo, H, Reitsma, J, Rutishauser, E, Salim, KA, Sonké, B, Sukri, RS, Sunderland, T, Svátek, M, Umunay, PM, Martinez, RV, Vernimmen, RRE, Torre, EV, Vleminckx, J, Vos, V, Phillips, OL, Sullivan, MJP, Lewis, SL, Hubau, W, Qie, L, Baker, TR, Banin, LF, Chave, J, Cuni-Sanchez, A, Feldpausch, TR, Lopez-Gonzalez, G, Arets, E, Ashton, P, Bastin, JF, Berry, NJ, Bogaert, J, Boot, R, Brearley, FQ, Brienen, R, Burslem, DFRP, de Canniere, C, Chudomelová, M, Dančák, M, Ewango, C, Hédl, R, Lloyd, J, Makana, JR, Malhi, Y, Marimon, BS, Junior, BHM, Metali, F, Moore, S, Nagy, L, Vargas, PN, Pendry, CA, Ramírez-Angulo, H, Reitsma, J, Rutishauser, E, Salim, KA, Sonké, B, Sukri, RS, Sunderland, T, Svátek, M, Umunay, PM, Martinez, RV, Vernimmen, RRE, Torre, EV, Vleminckx, J, Vos, V, and Phillips, OL
Abstract: © 2018 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. Quantifying the relationship between tree diameter and height is a key component of efforts to estimate biomass and carbon stocks in tropical forests. Although substantial site-to-site variation in height–diameter allometries has been documented, the time consuming nature of measuring all tree heights in an inventory plot means that most studies do not include height, or else use generic pan-tropical or regional allometric equations to estimate height. Using a pan-tropical dataset of 73 plots where at least 150 trees had in-field ground-based height measurements, we examined how the number of trees sampled affects the performance of locally derived height–diameter allometries, and evaluated the performance of different methods for sampling trees for height measurement. Using cross-validation, we found that allometries constructed with just 20 locally measured values could often predict tree height with lower error than regional or climate-based allometries (mean reduction in prediction error = 0.46 m). The predictive performance of locally derived allometries improved with sample size, but with diminishing returns in performance gains when more than 40 trees were sampled. Estimates of stand-level biomass produced using local allometries to estimate tree height show no over- or under-estimation bias when compared with biomass estimates using field measured heights. We evaluated five strategies to sample trees for height measurement, and found that sampling strategies that included measuring the heights of the ten largest diameter trees in a plot outperformed (in terms of resulting in local height–diameter models with low height prediction error) entirely random or diameter size-class stratified approaches. Our results indicate that even limited sampling of heights can be used to refine height–diameter allometries. We recommend aiming for a
Published: 2018

15. Phylogenetic classification of the world's tropical forests

Author: Slik, JWF, Franklin, J, Arroyo-Rodríguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, SI, Alves, LF, Anitha, K, Avella, A, Mora, F, Aymard, GAC, Báez, S, Balvanera, P, Bastian, ML, Bastin, JF, Bellingham, PJ, Van Den Berg, E, Da Conceição Bispo, P, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, FQ, Brown, S, Chai, SL, Chazdon, RL, Chen, S, Chhang, P, Chuyong, G, Ewango, C, Coronado, IM, Cristóbal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, HS, Davidar, P, DeWalt, SJ, DIn, H, Drake, DR, Duque, A, Durigan, G, Eichhorn, K, Eler, ES, Enoki, T, Ensslin, A, Fandohan, AB, Farwig, N, Feeley, KJ, Fischer, M, Forshed, O, Garcia, QS, Garkoti, SC, Gillespie, TW, Gillet, JF, Gonmadje, C, Granzow-De La Cerda, I, Griffith, DM, Grogan, J, Hakeem, KR, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Homeier, J, Hussain, MS, Ibarra-Manríquez, G, Hanum, IF, Imai, N, Jansen, PA, Joly, CA, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, DL, Kessler, M, Killeen, TJ, Kooyman, RM, Laumonier, Y, Laurance, SG, Laurance, WF, Lawes, MJ, Letcher, SG, Lindsell, J, Lovett, J, Lozada, J, Lu, X, Lykke, AM, Bin Mahmud, K, Mahayani, NPD, Slik, JWF, Franklin, J, Arroyo-Rodríguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, SI, Alves, LF, Anitha, K, Avella, A, Mora, F, Aymard, GAC, Báez, S, Balvanera, P, Bastian, ML, Bastin, JF, Bellingham, PJ, Van Den Berg, E, Da Conceição Bispo, P, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, FQ, Brown, S, Chai, SL, Chazdon, RL, Chen, S, Chhang, P, Chuyong, G, Ewango, C, Coronado, IM, Cristóbal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, HS, Davidar, P, DeWalt, SJ, DIn, H, Drake, DR, Duque, A, Durigan, G, Eichhorn, K, Eler, ES, Enoki, T, Ensslin, A, Fandohan, AB, Farwig, N, Feeley, KJ, Fischer, M, Forshed, O, Garcia, QS, Garkoti, SC, Gillespie, TW, Gillet, JF, Gonmadje, C, Granzow-De La Cerda, I, Griffith, DM, Grogan, J, Hakeem, KR, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Homeier, J, Hussain, MS, Ibarra-Manríquez, G, Hanum, IF, Imai, N, Jansen, PA, Joly, CA, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, DL, Kessler, M, Killeen, TJ, Kooyman, RM, Laumonier, Y, Laurance, SG, Laurance, WF, Lawes, MJ, Letcher, SG, Lindsell, J, Lovett, J, Lozada, J, Lu, X, Lykke, AM, Bin Mahmud, K, and Mahayani, NPD
Abstract: © 2017 IEEE. Knowledge about the biogeographic affinities of the world's tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world's tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northernhemisphere forests.
Published: 2018

16. Phylogenetic classification of the world's tropical forests

Author: Slik, JWF, Franklin, J, Arroyo-Rodríguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, SI, Alves, LF, Anitha, K, Avella, A, Mora, F, Aymard, GAC, Báez, S, Balvanera, P, Bastian, ML, Bastin, JF, Bellingham, PJ, Van Den Berg, E, Da Conceição Bispo, P, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, FQ, Brown, S, Chai, SL, Chazdon, RL, Chen, S, Chhang, P, Chuyong, G, Ewango, C, Coronado, IM, Cristóbal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, HS, Davidar, P, DeWalt, SJ, DIn, H, Drake, DR, Duque, A, Durigan, G, Eichhorn, K, Eler, ES, Enoki, T, Ensslin, A, Fandohan, AB, Farwig, N, Feeley, KJ, Fischer, M, Forshed, O, Garcia, QS, Garkoti, SC, Gillespie, TW, Gillet, JF, Gonmadje, C, Granzow-De La Cerda, I, Griffith, DM, Grogan, J, Hakeem, KR, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Homeier, J, Hussain, MS, Ibarra-Manríquez, G, Hanum, IF, Imai, N, Jansen, PA, Joly, CA, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, DL, Kessler, M, Killeen, TJ, Kooyman, RM, Laumonier, Y, Laurance, SG, Laurance, WF, Lawes, MJ, Letcher, SG, Lindsell, J, Lovett, J, Lozada, J, Lu, X, Lykke, AM, Bin Mahmud, K, Mahayani, NPD, Slik, JWF, Franklin, J, Arroyo-Rodríguez, V, Field, R, Aguilar, S, Aguirre, N, Ahumada, J, Aiba, SI, Alves, LF, Anitha, K, Avella, A, Mora, F, Aymard, GAC, Báez, S, Balvanera, P, Bastian, ML, Bastin, JF, Bellingham, PJ, Van Den Berg, E, Da Conceição Bispo, P, Boeckx, P, Boehning-Gaese, K, Bongers, F, Boyle, B, Brambach, F, Brearley, FQ, Brown, S, Chai, SL, Chazdon, RL, Chen, S, Chhang, P, Chuyong, G, Ewango, C, Coronado, IM, Cristóbal-Azkarate, J, Culmsee, H, Damas, K, Dattaraja, HS, Davidar, P, DeWalt, SJ, DIn, H, Drake, DR, Duque, A, Durigan, G, Eichhorn, K, Eler, ES, Enoki, T, Ensslin, A, Fandohan, AB, Farwig, N, Feeley, KJ, Fischer, M, Forshed, O, Garcia, QS, Garkoti, SC, Gillespie, TW, Gillet, JF, Gonmadje, C, Granzow-De La Cerda, I, Griffith, DM, Grogan, J, Hakeem, KR, Harris, DJ, Harrison, RD, Hector, A, Hemp, A, Homeier, J, Hussain, MS, Ibarra-Manríquez, G, Hanum, IF, Imai, N, Jansen, PA, Joly, CA, Joseph, S, Kartawinata, K, Kearsley, E, Kelly, DL, Kessler, M, Killeen, TJ, Kooyman, RM, Laumonier, Y, Laurance, SG, Laurance, WF, Lawes, MJ, Letcher, SG, Lindsell, J, Lovett, J, Lozada, J, Lu, X, Lykke, AM, Bin Mahmud, K, and Mahayani, NPD
Abstract: © 2017 IEEE. Knowledge about the biogeographic affinities of the world's tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world's tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northernhemisphere forests.
Published: 2018

17. Field methods for sampling tree height for tropical forest biomass estimation

Author: Sullivan, MJP, Lewis, SL, Hubau, W, Qie, L, Baker, TR, Banin, LF, Chave, J, Cuni-Sanchez, A, Feldpausch, TR, Lopez-Gonzalez, G, Arets, E, Ashton, P, Bastin, JF, Berry, NJ, Bogaert, J, Boot, R, Brearley, FQ, Brienen, R, Burslem, DFRP, de Canniere, C, Chudomelová, M, Dančák, M, Ewango, C, Hédl, R, Lloyd, J, Makana, JR, Malhi, Y, Marimon, BS, Junior, BHM, Metali, F, Moore, S, Nagy, L, Vargas, PN, Pendry, CA, Ramírez-Angulo, H, Reitsma, J, Rutishauser, E, Salim, KA, Sonké, B, Sukri, RS, Sunderland, T, Svátek, M, Umunay, PM, Martinez, RV, Vernimmen, RRE, Torre, EV, Vleminckx, J, Vos, V, Phillips, OL, Sullivan, MJP, Lewis, SL, Hubau, W, Qie, L, Baker, TR, Banin, LF, Chave, J, Cuni-Sanchez, A, Feldpausch, TR, Lopez-Gonzalez, G, Arets, E, Ashton, P, Bastin, JF, Berry, NJ, Bogaert, J, Boot, R, Brearley, FQ, Brienen, R, Burslem, DFRP, de Canniere, C, Chudomelová, M, Dančák, M, Ewango, C, Hédl, R, Lloyd, J, Makana, JR, Malhi, Y, Marimon, BS, Junior, BHM, Metali, F, Moore, S, Nagy, L, Vargas, PN, Pendry, CA, Ramírez-Angulo, H, Reitsma, J, Rutishauser, E, Salim, KA, Sonké, B, Sukri, RS, Sunderland, T, Svátek, M, Umunay, PM, Martinez, RV, Vernimmen, RRE, Torre, EV, Vleminckx, J, Vos, V, and Phillips, OL
Abstract: © 2018 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. Quantifying the relationship between tree diameter and height is a key component of efforts to estimate biomass and carbon stocks in tropical forests. Although substantial site-to-site variation in height–diameter allometries has been documented, the time consuming nature of measuring all tree heights in an inventory plot means that most studies do not include height, or else use generic pan-tropical or regional allometric equations to estimate height. Using a pan-tropical dataset of 73 plots where at least 150 trees had in-field ground-based height measurements, we examined how the number of trees sampled affects the performance of locally derived height–diameter allometries, and evaluated the performance of different methods for sampling trees for height measurement. Using cross-validation, we found that allometries constructed with just 20 locally measured values could often predict tree height with lower error than regional or climate-based allometries (mean reduction in prediction error = 0.46 m). The predictive performance of locally derived allometries improved with sample size, but with diminishing returns in performance gains when more than 40 trees were sampled. Estimates of stand-level biomass produced using local allometries to estimate tree height show no over- or under-estimation bias when compared with biomass estimates using field measured heights. We evaluated five strategies to sample trees for height measurement, and found that sampling strategies that included measuring the heights of the ten largest diameter trees in a plot outperformed (in terms of resulting in local height–diameter models with low height prediction error) entirely random or diameter size-class stratified approaches. Our results indicate that even limited sampling of heights can be used to refine height–diameter allometries. We recommend aiming for a
Published: 2018

18. Pan-tropical prediction of forest structure from the largest trees

Author: Bastin, JF, Rutishauser, E, Kellner, JR, Saatchi, S, Pélissier, R, Hérault, B, Slik, F, Bogaert, J, De Cannière, C, Marshall, AR, Poulsen, J, Alvarez-Loyayza, P, Andrade, A, Angbonga-Basia, A, Araujo-Murakami, A, Arroyo, L, Ayyappan, N, de Azevedo, CP, Banki, O, Barbier, N, Barroso, JG, Beeckman, H, Bitariho, R, Boeckx, P, Boehning-Gaese, K, Brandão, H, Brearley, FQ, Breuer Ndoundou Hockemba, M, Brienen, R, Camargo, JLC, Campos-Arceiz, A, Cassart, B, Chave, J, Chazdon, R, Chuyong, G, Clark, DB, Clark, CJ, Condit, R, Honorio Coronado, EN, Davidar, P, de Haulleville, T, Descroix, L, Doucet, JL, Dourdain, A, Droissart, V, Duncan, T, Silva Espejo, J, Espinosa, S, Farwig, N, Fayolle, A, Feldpausch, TR, Ferraz, A, Fletcher, C, Gajapersad, K, Gillet, JF, Amaral, ILD, Gonmadje, C, Grogan, J, Harris, D, Herzog, SK, Homeier, J, Hubau, W, Hubbell, SP, Hufkens, K, Hurtado, J, Kamdem, NG, Kearsley, E, Kenfack, D, Kessler, M, Labrière, N, Laumonier, Y, Laurance, S, Laurance, WF, Lewis, SL, Libalah, MB, Ligot, G, Lloyd, J, Lovejoy, TE, Malhi, Y, Marimon, BS, Marimon Junior, BH, Martin, EH, Matius, P, Bastin, JF, Rutishauser, E, Kellner, JR, Saatchi, S, Pélissier, R, Hérault, B, Slik, F, Bogaert, J, De Cannière, C, Marshall, AR, Poulsen, J, Alvarez-Loyayza, P, Andrade, A, Angbonga-Basia, A, Araujo-Murakami, A, Arroyo, L, Ayyappan, N, de Azevedo, CP, Banki, O, Barbier, N, Barroso, JG, Beeckman, H, Bitariho, R, Boeckx, P, Boehning-Gaese, K, Brandão, H, Brearley, FQ, Breuer Ndoundou Hockemba, M, Brienen, R, Camargo, JLC, Campos-Arceiz, A, Cassart, B, Chave, J, Chazdon, R, Chuyong, G, Clark, DB, Clark, CJ, Condit, R, Honorio Coronado, EN, Davidar, P, de Haulleville, T, Descroix, L, Doucet, JL, Dourdain, A, Droissart, V, Duncan, T, Silva Espejo, J, Espinosa, S, Farwig, N, Fayolle, A, Feldpausch, TR, Ferraz, A, Fletcher, C, Gajapersad, K, Gillet, JF, Amaral, ILD, Gonmadje, C, Grogan, J, Harris, D, Herzog, SK, Homeier, J, Hubau, W, Hubbell, SP, Hufkens, K, Hurtado, J, Kamdem, NG, Kearsley, E, Kenfack, D, Kessler, M, Labrière, N, Laumonier, Y, Laurance, S, Laurance, WF, Lewis, SL, Libalah, MB, Ligot, G, Lloyd, J, Lovejoy, TE, Malhi, Y, Marimon, BS, Marimon Junior, BH, Martin, EH, and Matius, P
Abstract: © 2018 John Wiley & Sons Ltd Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan-tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot- and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.
Published: 2018

19. Diversity and carbon storage across the tropical forest biome

Author: Sullivan, MJP, Talbot, J, Lewis, SL, Phillips, OL, Qie, L, Begne, SK, Chave, J, Cuni-Sanchez, A, Hubau, W, Lopez-Gonzalez, G, Miles, L, Monteagudo-Mendoza, A, Sonké, B, Sunderland, T, Ter Steege, H, White, LJT, Affum-Baffoe, K, Aiba, SI, De Almeida, EC, De Oliveira, EA, Alvarez-Loayza, P, Dávila, EÁ, Andrade, A, Aragão, LEOC, Ashton, P, Aymard, GA, Baker, TR, Balinga, M, Banin, LF, Baraloto, C, Bastin, JF, Berry, N, Bogaert, J, Bonal, D, Bongers, F, Brienen, R, Camargo, JLC, Cerón, C, Moscoso, VC, Chezeaux, E, Clark, CJ, Pacheco, ÁC, Comiskey, JA, Valverde, FC, Coronado, ENH, Dargie, G, Davies, SJ, De Canniere, C, Djuikouo, MN, Doucet, JL, Erwin, TL, Espejo, JS, Ewango, CEN, Fauset, S, Feldpausch, TR, Herrera, R, Gilpin, M, Gloor, E, Hall, JS, Harris, DJ, Hart, TB, Kartawinata, K, Kho, LK, Kitayama, K, Laurance, SGW, Laurance, WF, Leal, ME, Lovejoy, T, Lovett, JC, Lukasu, FM, Makana, JR, Malhi, Y, Maracahipes, L, Marimon, BS, Junior, BHM, Marshall, AR, Morandi, PS, Mukendi, JT, Mukinzi, J, Nilus, R, Vargas, PN, Camacho, NCP, Pardo, G, Peña-Claros, M, Pétronelli, P, Pickavance, GC, Poulsen, AD, Poulsen, JR, Primack, RB, Priyadi, H, Quesada, CA, Reitsma, J, Réjou-Méchain, M, Restrepo, Z, Rutishauser, E, Salim, KA, Salomão, RP, Samsoedin, I, Sheil, D, Sierra, R, Sullivan, MJP, Talbot, J, Lewis, SL, Phillips, OL, Qie, L, Begne, SK, Chave, J, Cuni-Sanchez, A, Hubau, W, Lopez-Gonzalez, G, Miles, L, Monteagudo-Mendoza, A, Sonké, B, Sunderland, T, Ter Steege, H, White, LJT, Affum-Baffoe, K, Aiba, SI, De Almeida, EC, De Oliveira, EA, Alvarez-Loayza, P, Dávila, EÁ, Andrade, A, Aragão, LEOC, Ashton, P, Aymard, GA, Baker, TR, Balinga, M, Banin, LF, Baraloto, C, Bastin, JF, Berry, N, Bogaert, J, Bonal, D, Bongers, F, Brienen, R, Camargo, JLC, Cerón, C, Moscoso, VC, Chezeaux, E, Clark, CJ, Pacheco, ÁC, Comiskey, JA, Valverde, FC, Coronado, ENH, Dargie, G, Davies, SJ, De Canniere, C, Djuikouo, MN, Doucet, JL, Erwin, TL, Espejo, JS, Ewango, CEN, Fauset, S, Feldpausch, TR, Herrera, R, Gilpin, M, Gloor, E, Hall, JS, Harris, DJ, Hart, TB, Kartawinata, K, Kho, LK, Kitayama, K, Laurance, SGW, Laurance, WF, Leal, ME, Lovejoy, T, Lovett, JC, Lukasu, FM, Makana, JR, Malhi, Y, Maracahipes, L, Marimon, BS, Junior, BHM, Marshall, AR, Morandi, PS, Mukendi, JT, Mukinzi, J, Nilus, R, Vargas, PN, Camacho, NCP, Pardo, G, Peña-Claros, M, Pétronelli, P, Pickavance, GC, Poulsen, AD, Poulsen, JR, Primack, RB, Priyadi, H, Quesada, CA, Reitsma, J, Réjou-Méchain, M, Restrepo, Z, Rutishauser, E, Salim, KA, Salomão, RP, Samsoedin, I, Sheil, D, and Sierra, R
Abstract: © The Author(s) 2017. Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-Tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.
Published: 2017

20. Diversity and carbon storage across the tropical forest biome

Author: Sullivan, MJP, Talbot, J, Lewis, SL, Phillips, OL, Qie, L, Begne, SK, Chave, J, Cuni-Sanchez, A, Hubau, W, Lopez-Gonzalez, G, Miles, L, Monteagudo-Mendoza, A, Sonké, B, Sunderland, T, Ter Steege, H, White, LJT, Affum-Baffoe, K, Aiba, SI, De Almeida, EC, De Oliveira, EA, Alvarez-Loayza, P, Dávila, EÁ, Andrade, A, Aragão, LEOC, Ashton, P, Aymard, GA, Baker, TR, Balinga, M, Banin, LF, Baraloto, C, Bastin, JF, Berry, N, Bogaert, J, Bonal, D, Bongers, F, Brienen, R, Camargo, JLC, Cerón, C, Moscoso, VC, Chezeaux, E, Clark, CJ, Pacheco, ÁC, Comiskey, JA, Valverde, FC, Coronado, ENH, Dargie, G, Davies, SJ, De Canniere, C, Djuikouo, MN, Doucet, JL, Erwin, TL, Espejo, JS, Ewango, CEN, Fauset, S, Feldpausch, TR, Herrera, R, Gilpin, M, Gloor, E, Hall, JS, Harris, DJ, Hart, TB, Kartawinata, K, Kho, LK, Kitayama, K, Laurance, SGW, Laurance, WF, Leal, ME, Lovejoy, T, Lovett, JC, Lukasu, FM, Makana, JR, Malhi, Y, Maracahipes, L, Marimon, BS, Junior, BHM, Marshall, AR, Morandi, PS, Mukendi, JT, Mukinzi, J, Nilus, R, Vargas, PN, Camacho, NCP, Pardo, G, Peña-Claros, M, Pétronelli, P, Pickavance, GC, Poulsen, AD, Poulsen, JR, Primack, RB, Priyadi, H, Quesada, CA, Reitsma, J, Réjou-Méchain, M, Restrepo, Z, Rutishauser, E, Salim, KA, Salomão, RP, Samsoedin, I, Sheil, D, Sierra, R, Sullivan, MJP, Talbot, J, Lewis, SL, Phillips, OL, Qie, L, Begne, SK, Chave, J, Cuni-Sanchez, A, Hubau, W, Lopez-Gonzalez, G, Miles, L, Monteagudo-Mendoza, A, Sonké, B, Sunderland, T, Ter Steege, H, White, LJT, Affum-Baffoe, K, Aiba, SI, De Almeida, EC, De Oliveira, EA, Alvarez-Loayza, P, Dávila, EÁ, Andrade, A, Aragão, LEOC, Ashton, P, Aymard, GA, Baker, TR, Balinga, M, Banin, LF, Baraloto, C, Bastin, JF, Berry, N, Bogaert, J, Bonal, D, Bongers, F, Brienen, R, Camargo, JLC, Cerón, C, Moscoso, VC, Chezeaux, E, Clark, CJ, Pacheco, ÁC, Comiskey, JA, Valverde, FC, Coronado, ENH, Dargie, G, Davies, SJ, De Canniere, C, Djuikouo, MN, Doucet, JL, Erwin, TL, Espejo, JS, Ewango, CEN, Fauset, S, Feldpausch, TR, Herrera, R, Gilpin, M, Gloor, E, Hall, JS, Harris, DJ, Hart, TB, Kartawinata, K, Kho, LK, Kitayama, K, Laurance, SGW, Laurance, WF, Leal, ME, Lovejoy, T, Lovett, JC, Lukasu, FM, Makana, JR, Malhi, Y, Maracahipes, L, Marimon, BS, Junior, BHM, Marshall, AR, Morandi, PS, Mukendi, JT, Mukinzi, J, Nilus, R, Vargas, PN, Camacho, NCP, Pardo, G, Peña-Claros, M, Pétronelli, P, Pickavance, GC, Poulsen, AD, Poulsen, JR, Primack, RB, Priyadi, H, Quesada, CA, Reitsma, J, Réjou-Méchain, M, Restrepo, Z, Rutishauser, E, Salim, KA, Salomão, RP, Samsoedin, I, Sheil, D, and Sierra, R
Abstract: © The Author(s) 2017. Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-Tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.
Published: 2017

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20 results on '"Bastin JF"'

1. Positive feedbacks and alternative stable states in forest leaf types

2. Consistent patterns of common species across tropical tree communities

3. Consistent patterns of common species across tropical tree communities

4. The number of tree species on Earth

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

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

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

8. Taking the pulse of Earth's tropical forests using networks of highly distributed plots

9. Taking the pulse of Earth's tropical forests using networks of highly distributed plots

10. The global abundance of tree palms

11. The global abundance of tree palms

12. Correction for Slik et al., Phylogenetic classification of the world’s tropical forests

13. Pan-tropical prediction of forest structure from the largest trees

14. Field methods for sampling tree height for tropical forest biomass estimation

15. Phylogenetic classification of the world's tropical forests

16. Phylogenetic classification of the world's tropical forests

17. Field methods for sampling tree height for tropical forest biomass estimation

18. Pan-tropical prediction of forest structure from the largest trees

19. Diversity and carbon storage across the tropical forest biome

20. Diversity and carbon storage across the tropical forest biome

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