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1. 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

2. 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

3. 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, Sanchez, AC, Feldpausch, T, 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, J-R, Malhi, Y, Marimon, BS, Marimon Junior, BH, Metali, F, Moore, S, Nagy, L, Vargas, PN, Pendry, C, Ramírez-Angulo, H, Reitsma, J, Rutishauser, E, Salim, KA, Sonké, B, Sukri, RS, Sunderland, T, Svátek, M, Umunay, PM, Vasquez Martinez, R, Vernimmen, RRE, Vilanova Torre, E, Vleminckx, J, Vos, V, and Phillips, OL

Subjects
Allometry, Sample size, ALLOMETRIC MODELS, DIAMETER, Above-ground biomass estimation, Ecology and Environment, Carbon stocks, Forest structure, Earth and Environmental Sciences, MAP, Vegetatie, Bos- en Landschapsecologie, Vegetation, Forest and Landscape Ecology, EQUATIONS, ABOVEGROUND BIOMASS, Forest inventory
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 conservative threshold of sampling 50 trees per location for height measurement, and including the ten trees with the largest diameter in this sample.

Published
2018

4. The global distribution and drivers of wood density and their impact on forest carbon stocks.

Author

Mo L, Crowther TW, Maynard DS, van den Hoogen J, Ma H, Bialic-Murphy L, Liang J, de-Miguel S, Nabuurs GJ, Reich PB, Phillips OL, Abegg M, Adou Yao YC, Alberti G, Almeyda Zambrano AM, Alvarado BV, Alvarez-Dávila E, Alvarez-Loayza P, Alves LF, Amaral I, 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, Boonman CCF, 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, Engone Obiang NL, Enquist BJ, Eyre TJ, Fandohan AB, Fayle TM, Feldpausch TR, Ferreira LV, Finér L, Fischer M, Fletcher C, Frizzera L, Gamarra JGP, Gianelle D, Glick HB, Harris DJ, Hector A, Hemp A, Hengeveld G, Hérault B, Herbohn JL, Herold M, Hietz P, Hillers A, Honorio Coronado EN, Hui C, Ibanez T, Imai N, Jagodziński AM, Jaroszewicz B, Johannsen VK, Joly CA, Jucker T, Jung I, Karminov V, Kartawinata K, Kearsley E, Kenfack D, Kennard DK, Kepfer-Rojas S, Keppel G, Khan ML, Killeen TJ, Kim HS, Kitayama K, Köhl M, Korjus H, Kraxner F, Kucher D, Laarmann D, Lang M, Lewis SL, Li Y, Lopez-Gonzalez G, Lu H, Lukina NV, Maitner BS, Malhi Y, Marcon E, Marimon BS, Marimon-Junior BH, Marshall AR, Martin EH, McCarthy JK, Meave JA, Melo-Cruz O, Mendoza C, Mendoza-Polo I, Miscicki S, Merow C, Mendoza AM, Moreno VS, Mukul SA, Mundhenk P, Nava-Miranda MG, Neill D, Neldner VJ, Nevenic RV, Ngugi MR, Niklaus PA, Ontikov P, Ortiz-Malavasi E, Pan Y, Paquette A, Parada-Gutierrez A, Parfenova EI, Park M, Parren M, Parthasarathy N, Peri PL, Pfautsch S, Picard N, Piedade MTF, Piotto D, Pitman NCA, Poorter L, Poulsen AD, Poulsen JR, Pretzsch H, Arevalo FR, Restrepo-Correa Z, Richardson SJ, Rodeghiero M, Rolim SG, Roopsind A, Rovero F, Rutishauser E, Saikia P, Salas-Eljatib C, Saner P, Schall P, Schelhaas MJ, Schepaschenko D, Scherer-Lorenzen M, Schmid B, Schöngart J, Searle EB, Seben V, Serra-Diaz JM, Sheil D, Shvidenko AZ, Da Silva AC, Silva-Espejo JE, Silveira M, Singh J, Sist P, Slik F, Sonké B, Sosinski EE Jr, Souza AF, Stereńczak KJ, Svenning JC, Svoboda M, Swanepoel B, Targhetta N, Tchebakova N, Ter Steege H, Thomas R, Tikhonova E, Umunay PM, Usoltsev VA, Valencia R, Valladares F, Van Bodegom PM, van der Plas F, Van Do T, van Nuland ME, Vasquez RM, Verbeeck H, Viana H, Vibrans AC, Vieira S, von Gadow K, Wang HF, Watson JV, Werner GDA, Wittmann F, Woell H, Wortel V, Zagt R, Zawiła-Niedźwiecki T, Zhang C, Zhao X, Zhou M, Zhu ZX, Zo-Bi IC, and Zohner CM

Subjects
Biomass, Wood, Forests, Carbon metabolism, Carbon analysis, Trees growth & development, Trees metabolism
Abstract

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Published
2024
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5. 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, Ahuite Reategui MA, Aiba SI, Albuquerque BW, de Almeida Matos FD, Alonso A, Amani CA, do Amaral DD, do Amaral IL, Andrade A, de Andrade Miranda IP, Angoboy IB, Araujo-Murakami A, Arboleda NC, Arroyo L, Ashton P, Aymard C GA, 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, Boyemba Bosela F, Brambach F, Brienen R, Burslem DFRP, Camargo JL, Campelo W, Cano A, Cárdenas S, Cárdenas López D, de Sá Carpanedo R, Carrero Márquez YA, Carvalho FA, Casas LF, Castellanos H, Castilho CV, Cerón C, Chapman CA, Chave J, Chhang P, Chutipong W, Chuyong GB, Cintra BBL, Clark CJ, Coelho de Souza F, Comiskey JA, Coomes DA, Cornejo Valverde F, Correa DF, Costa FRC, Costa JBP, Couteron P, Culmsee H, Cuni-Sanchez A, Dallmeier F, Damasco G, Dauby G, Dávila N, Dávila Doza HP, De Alban JDT, de Assis RL, De Canniere C, De Haulleville T, de Jesus Veiga Carim M, Demarchi LO, Dexter KG, Di Fiore A, Din HHM, Disney MI, Djiofack BY, Djuikouo MK, Do TV, 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 É, Fredriksson GM, Fuentes A, Galbraith D, Gallardo Gonzales GP, Garcia-Cabrera K, García-Villacorta R, Gomes VHF, Gómez RZ, Gonzales T, Gribel R, Guedes MC, Guevara JE, Hakeem KR, Hall JS, Hamer KC, Hamilton AC, Harris DJ, Harrison RD, Hart TB, Hector A, Henkel TW, Herbohn J, Hockemba MBN, Hoffman B, Holmgren M, Honorio Coronado EN, 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, Kasongo Yakusu E, Katembo JM, Kearsley E, Kenfack D, Kessler M, Khaing TT, Killeen TJ, Kitayama K, Klitgaard B, Labrière N, Laumonier Y, Laurance SGW, Laurance WF, Laurent F, Le TC, Le TT, Leal ME, Leão de Moraes Novo EM, Levesley A, Libalah MB, Licona JC, Lima Filho DA, Lindsell JA, Lopes A, Lopes MA, Lovett JC, Lowe R, Lozada JR, Lu X, Luambua NK, Luize BG, Maas P, Magalhães JLL, Magnusson WE, Mahayani NPD, Makana JR, Malhi Y, Maniguaje Rincón L, Mansor A, Manzatto AG, Marimon BS, Marimon-Junior BH, Marshall AR, Martins MP, Mbayu FM, de Medeiros MB, Mesones I, Metali F, Mihindou V, Millet J, Milliken W, Mogollón HF, Molino JF, Mohd Said MN, Monteagudo Mendoza A, Montero JC, Moore S, Mostacedo B, Mozombite Pinto LF, Mukul SA, Munishi PKT, Nagamasu H, Nascimento HEM, Nascimento MT, Neill D, Nilus R, Noronha JC, Nsenga L, Núñez Vargas P, Ojo L, Oliveira AA, de Oliveira EA, Ondo FE, Palacios Cuenca W, Pansini S, Pansonato MP, Paredes MR, Paudel E, Pauletto D, Pearson RG, Pena JLM, Pennington RT, Peres CA, Permana A, Petronelli P, Peñuela Mora MC, 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 DJ, Rodriguez-Ronderos ME, Rovero F, Rozak AH, Rudas A, Rutishauser E, Sabatier D, Sagang LB, Sampaio AF, Samsoedin I, Satdichanh M, Schietti J, Schöngart J, Scudeller VV, Seuaturien N, Sheil D, Sierra R, Silman MR, Silva TSF, da Silva Guimarães JR, Simo-Droissart M, Simon MF, Sist P, Sousa TR, de Sousa Farias E, de Souza Coelho L, Spracklen DV, Stas SM, Steinmetz R, Stevenson PR, Stropp J, Sukri RS, Sunderland TCH, Suzuki E, Swaine MD, Tang J, 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, Tshibamba Mukendi J, Tumaneng RD, Umaña MN, Umunay PM, Urrego Giraldo LE, Valderrama Sandoval EH, Valenzuela Gamarra L, 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, Vilanova Torre E, Villarroel D, Villa Zegarra BE, Vleminckx J, von Hildebrand P, Vos VA, Vriesendorp C, Webb EL, White LJT, Wich S, Wittmann F, Zagt R, Zang R, Zartman CE, Zemagho L, Zent EL, and Zent S

Subjects
Biodiversity, Africa, Asia, Southeastern, Forests, Trees anatomy & histology, Trees classification, Trees growth & development, Tropical Climate
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., (© 2024. The Author(s).)

Published
2024
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6. Integrated global assessment of the natural forest carbon potential.

Author

Mo L, Zohner CM, Reich PB, Liang J, de Miguel S, Nabuurs GJ, Renner SS, van den Hoogen J, Araza A, Herold M, Mirzagholi L, Ma H, Averill C, Phillips OL, Gamarra JGP, Hordijk I, Routh D, Abegg M, Adou Yao YC, Alberti G, Almeyda Zambrano AM, Alvarado BV, Alvarez-Dávila E, Alvarez-Loayza P, Alves LF, Amaral I, 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, Cazzolla Gatti R, César RG, Cesljar G, Chazdon RL, Chen HYH, Chisholm C, Cho H, Cienciala E, Clark C, Clark D, Colletta GD, Coomes DA, Cornejo Valverde F, Corral-Rivas JJ, Crim PM, Cumming JR, Dayanandan S, de Gasper AL, Decuyper M, Derroire G, DeVries B, Djordjevic I, Dolezal J, Dourdain A, Engone Obiang NL, Enquist BJ, Eyre TJ, Fandohan AB, Fayle TM, Feldpausch TR, Ferreira LV, Finér L, Fischer M, Fletcher C, Frizzera L, Gianelle D, Glick HB, Harris DJ, Hector A, Hemp A, Hengeveld G, Hérault B, Herbohn JL, Hillers A, Honorio Coronado EN, Hui C, Ibanez T, Imai N, Jagodziński AM, Jaroszewicz B, Johannsen VK, Joly CA, Jucker T, Jung I, Karminov V, Kartawinata K, Kearsley E, Kenfack D, Kennard DK, Kepfer-Rojas S, Keppel G, Khan ML, Killeen TJ, Kim HS, Kitayama K, Köhl M, Korjus H, Kraxner F, Kucher D, Laarmann D, Lang M, Lu H, Lukina NV, Maitner BS, Malhi Y, Marcon E, Marimon BS, Marimon-Junior BH, Marshall AR, Martin EH, Meave JA, Melo-Cruz O, Mendoza C, Mendoza-Polo I, Miscicki S, Merow C, Monteagudo Mendoza A, Moreno VS, Mukul SA, Mundhenk P, Nava-Miranda MG, Neill D, Neldner VJ, Nevenic RV, Ngugi MR, Niklaus PA, Oleksyn J, Ontikov P, Ortiz-Malavasi E, Pan Y, Paquette A, Parada-Gutierrez A, Parfenova EI, Park M, Parren M, Parthasarathy N, Peri PL, Pfautsch S, Picard N, Piedade MTF, Piotto D, Pitman NCA, Poulsen AD, Poulsen JR, Pretzsch H, Ramirez Arevalo F, Restrepo-Correa Z, Rodeghiero M, Rolim SG, Roopsind A, Rovero F, Rutishauser E, Saikia P, Salas-Eljatib C, Saner P, Schall P, Schelhaas MJ, Schepaschenko D, Scherer-Lorenzen M, Schmid B, Schöngart J, Searle EB, Seben V, Serra-Diaz JM, Sheil D, Shvidenko AZ, Silva-Espejo JE, Silveira M, Singh J, Sist P, Slik F, Sonké B, Souza AF, Stereńczak KJ, Svenning JC, Svoboda M, Swanepoel B, Targhetta N, Tchebakova N, Ter Steege H, Thomas R, Tikhonova E, Umunay PM, Usoltsev VA, Valencia R, Valladares F, van der Plas F, Van Do T, van Nuland ME, Vasquez RM, Verbeeck H, Viana H, Vibrans AC, Vieira S, von Gadow K, Wang HF, Watson JV, Werner GDA, Wiser SK, Wittmann F, Woell H, Wortel V, Zagt R, Zawiła-Niedźwiecki T, Zhang C, Zhao X, Zhou M, Zhu ZX, Zo-Bi IC, Gann GD, and Crowther TW

Subjects
Biodiversity, Human Activities, Environmental Restoration and Remediation trends, Sustainable Development trends, Global Warming prevention & control, Carbon analysis, Carbon metabolism, Carbon Sequestration, Conservation of Natural Resources statistics & numerical data, Conservation of Natural Resources trends, Forests
Abstract

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1 . Remote-sensing estimates to quantify carbon losses from global forests 2-5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151-363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets., (© 2023. The Author(s).)

Published
2023
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7. The global biogeography of tree leaf form and habit.

Author

Ma H, Crowther TW, Mo L, Maynard DS, Renner SS, van den Hoogen J, Zou Y, Liang J, de-Miguel S, Nabuurs GJ, Reich PB, Niinemets Ü, 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, Cazzolla Gatti R, 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, Engone Obiang NL, 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, Hillers A, Honorio Coronado EN, Hui C, Ibanez TT, Amaral I, Imai N, Jagodziński AM, Jaroszewicz B, Johannsen VK, Joly CA, Jucker T, Jung I, Karminov V, Kartawinata K, Kearsley E, Kenfack D, Kennard DK, Kepfer-Rojas S, Keppel G, Khan ML, Killeen TJ, Kim HS, Kitayama K, Köhl M, Korjus H, Kraxner F, Kucher D, Laarmann D, Lang M, Lewis SL, Lu H, Lukina NV, Maitner BS, Malhi Y, Marcon E, Marimon BS, Marimon-Junior BH, Marshall AR, Martin EH, Meave JA, Melo-Cruz O, Mendoza C, Merow C, Monteagudo Mendoza A, Moreno VS, Mukul SA, Mundhenk P, Nava-Miranda MG, Neill D, Neldner VJ, Nevenic RV, Ngugi MR, Niklaus PA, Oleksyn J, Ontikov P, Ortiz-Malavasi E, Pan Y, Paquette A, Parada-Gutierrez A, Parfenova EI, Park M, Parren M, Parthasarathy N, Peri PL, Pfautsch S, Phillips OL, Picard N, Piedade MTF, Piotto D, Pitman NCA, Mendoza-Polo I, Poulsen AD, Poulsen JR, Pretzsch H, Ramirez Arevalo F, Restrepo-Correa Z, Rodeghiero M, Rolim SG, Roopsind A, Rovero F, Rutishauser E, Saikia P, Salas-Eljatib C, Saner P, Schall P, Schelhaas MJ, Schepaschenko D, Scherer-Lorenzen M, Schmid B, Schöngart J, Searle EB, Seben V, Serra-Diaz JM, Sheil D, Shvidenko AZ, Silva-Espejo JE, Silveira M, Singh J, Sist P, Slik F, Sonké B, Souza AF, Miścicki S, Stereńczak KJ, Svenning JC, Svoboda M, Swanepoel B, Targhetta N, Tchebakova N, Ter Steege H, Thomas R, Tikhonova E, Umunay PM, Usoltsev VA, Valencia R, Valladares F, van der Plas F, Van Do T, van Nuland ME, Vasquez RM, Verbeeck H, Viana H, Vibrans AC, Vieira S, von Gadow K, Wang HF, Watson JV, Werner GDA, Westerlund B, Wiser SK, Wittmann F, Woell H, Wortel V, Zagt R, Zawiła-Niedźwiecki T, Zhang C, Zhao X, Zhou M, Zhu ZX, Zo-Bi IC, and Zohner CM

Subjects
Humans, Forests, Plant Leaves metabolism, Habits, Carbon metabolism, Trees metabolism, Ecosystem
Abstract

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling., (© 2023. The Author(s).)

Published
2023
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8. Author Correction: Native diversity buffers against severity of non-native tree invasions.

Author

Delavaux CS, Crowther TW, Zohner CM, Robmann NM, Lauber T, van den Hoogen J, Kuebbing S, Liang J, de-Miguel S, Nabuurs GJ, Reich PB, 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, 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, Cornejo Valverde F, Corral-Rivas JJ, Crim PM, Cumming JR, Dayanandan S, de Gasper AL, Decuyper M, Derroire G, DeVries B, Djordjevic I, Dolezal J, Dourdain A, Engone Obiang NL, Enquist BJ, Eyre TJ, Fandohan AB, Fayle TM, Feldpausch TR, Ferreira LV, Fischer M, Fletcher C, Frizzera L, Gamarra JGP, Gianelle D, Glick HB, Harris DJ, Hector A, Hemp A, Hengeveld G, Hérault B, Herbohn JL, Herold M, Hillers A, Honorio Coronado EN, Hui C, Ibanez TT, Amaral I, Imai N, Jagodziński AM, Jaroszewicz B, Johannsen VK, Joly CA, Jucker T, Jung I, Karminov V, Kartawinata K, Kearsley E, Kenfack D, Kennard DK, Kepfer-Rojas S, Keppel G, Khan ML, Killeen TJ, Kim HS, Kitayama K, Köhl M, Korjus H, Kraxner F, Laarmann D, Lang M, Lewis SL, Lu H, Lukina NV, Maitner BS, Malhi Y, Marcon E, Marimon BS, Marimon-Junior BH, Marshall AR, Martin EH, Martynenko O, Meave JA, Melo-Cruz O, Mendoza C, Merow C, Mendoza AM, Moreno VS, Mukul SA, Mundhenk P, Nava-Miranda MG, Neill D, Neldner VJ, Nevenic RV, Ngugi MR, Niklaus PA, Oleksyn J, Ontikov P, Ortiz-Malavasi E, Pan Y, Paquette A, Parada-Gutierrez A, Parfenova EI, Park M, Parren M, Parthasarathy N, Peri PL, Pfautsch S, Phillips OL, Picard N, Piedade MTTF, Piotto D, Pitman NCA, Polo I, Poorter L, Poulsen AD, Pretzsch H, Ramirez Arevalo F, Restrepo-Correa Z, Rodeghiero M, Rolim SG, Roopsind A, Rovero F, Rutishauser E, Saikia P, Salas-Eljatib C, Saner P, Schall P, Schepaschenko D, Scherer-Lorenzen M, Schmid B, Schöngart J, Searle EB, Seben V, Serra-Diaz JM, Sheil D, Shvidenko AZ, Silva-Espejo JE, Silveira M, Singh J, Sist P, Slik F, Sonké B, Souza AF, Miscicki S, Stereńczak KJ, Svenning JC, Svoboda M, Swanepoel B, Targhetta N, Tchebakova N, Ter Steege H, Thomas R, Tikhonova E, Umunay PM, Usoltsev VA, Valencia R, Valladares F, van der Plas F, Do TV, van Nuland ME, Vasquez RM, Verbeeck H, Viana H, Vibrans AC, Vieira S, von Gadow K, Wang HF, Watson JV, Werner GDA, Wiser SK, Wittmann F, Woell H, Wortel V, Zagt R, Zawiła-Niedźwiecki T, Zhang C, Zhao X, Zhou M, Zhu ZX, Zo-Bi IC, and Maynard DS

Published
2023
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9. Native diversity buffers against severity of non-native tree invasions.

Author

Delavaux CS, Crowther TW, Zohner CM, Robmann NM, Lauber T, van den Hoogen J, Kuebbing S, Liang J, de-Miguel S, Nabuurs GJ, Reich PB, 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, 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, Cornejo Valverde F, Corral-Rivas JJ, Crim PM, Cumming JR, Dayanandan S, de Gasper AL, Decuyper M, Derroire G, DeVries B, Djordjevic I, Dolezal J, Dourdain A, Engone Obiang NL, Enquist BJ, Eyre TJ, Fandohan AB, Fayle TM, Feldpausch TR, Ferreira LV, Fischer M, Fletcher C, Frizzera L, Gamarra JGP, Gianelle D, Glick HB, Harris DJ, Hector A, Hemp A, Hengeveld G, Hérault B, Herbohn JL, Herold M, Hillers A, Honorio Coronado EN, Hui C, Ibanez TT, Amaral I, Imai N, Jagodziński AM, Jaroszewicz B, Johannsen VK, Joly CA, Jucker T, Jung I, Karminov V, Kartawinata K, Kearsley E, Kenfack D, Kennard DK, Kepfer-Rojas S, Keppel G, Khan ML, Killeen TJ, Kim HS, Kitayama K, Köhl M, Korjus H, Kraxner F, Laarmann D, Lang M, Lewis SL, Lu H, Lukina NV, Maitner BS, Malhi Y, Marcon E, Marimon BS, Marimon-Junior BH, Marshall AR, Martin EH, Martynenko O, Meave JA, Melo-Cruz O, Mendoza C, Merow C, Mendoza AM, Moreno VS, Mukul SA, Mundhenk P, Nava-Miranda MG, Neill D, Neldner VJ, Nevenic RV, Ngugi MR, Niklaus PA, Oleksyn J, Ontikov P, Ortiz-Malavasi E, Pan Y, Paquette A, Parada-Gutierrez A, Parfenova EI, Park M, Parren M, Parthasarathy N, Peri PL, Pfautsch S, Phillips OL, Picard N, Piedade MTTF, Piotto D, Pitman NCA, Polo I, Poorter L, Poulsen AD, Pretzsch H, Ramirez Arevalo F, Restrepo-Correa Z, Rodeghiero M, Rolim SG, Roopsind A, Rovero F, Rutishauser E, Saikia P, Salas-Eljatib C, Saner P, Schall P, Schepaschenko D, Scherer-Lorenzen M, Schmid B, Schöngart J, Searle EB, Seben V, Serra-Diaz JM, Sheil D, Shvidenko AZ, Silva-Espejo JE, Silveira M, Singh J, Sist P, Slik F, Sonké B, Souza AF, Miscicki S, Stereńczak KJ, Svenning JC, Svoboda M, Swanepoel B, Targhetta N, Tchebakova N, Ter Steege H, Thomas R, Tikhonova E, Umunay PM, Usoltsev VA, Valencia R, Valladares F, van der Plas F, Do TV, van Nuland ME, Vasquez RM, Verbeeck H, Viana H, Vibrans AC, Vieira S, von Gadow K, Wang HF, Watson JV, Werner GDA, Wiser SK, Wittmann F, Woell H, Wortel V, Zagt R, Zawiła-Niedźwiecki T, Zhang C, Zhao X, Zhou M, Zhu ZX, Zo-Bi IC, and Maynard DS

Subjects
Databases, Factual, Human Activities, Phylogeny, Rain, Temperature, Biodiversity, Introduced Species statistics & numerical data, Introduced Species trends, Trees classification, Trees physiology, Environment
Abstract

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species 1,2 . Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies 3,4 . Here, leveraging global tree databases 5-7 , we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions., (© 2023. The Author(s).)

Published
2023
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10. The spatial propagation and increasing dominance of Gilbertiodendron dewevrei (Fabaceae) in the eastern Congo basin.

Author

Glick HB, Umunay PM, Makana JR, Tomlin CD, Reuning-Scherer JD, and Gregoire TG

Subjects
Congo, Tropical Climate, Forests, Trees physiology, Fabaceae
Abstract

Though substantial research has been conducted on possible historical, physiological, and symbiotic mechanisms that permit monodominance to occur within tropical lowland rainforests, less is known about the successional rates at which monodominance exerts itself on surrounding forest structures. Here we extend efforts to evaluate the longitudinal dynamics of Gilbertiodendron dewevrei-dominated forest in Central Africa by considering this species' spatial dynamics. Using three 10-ha censused field plots measured across three time periods, we present the first quantitative estimates of the spatial propagation of Gilbertiodendron into adjacent mixed species forest. Using three analytical strategies, we demonstrate that Gilbertiodendron is increasing in dominance and that monodominant forest patches are expanding into the surrounding forest at a statistically significant rate. The rates of successional advance vary by patch and direction, but average 0.31 m year-1, with speeds greatest in the direction of the prevailing winds. We show that the advancement of Gilbertiodendron is significantly slower than documented rates from other forest ecotones across Central Africa. When paired with stress tolerance traits and ectomycorrhizal associations, these findings help to clarify the means by which Gilbertiodendron dewevrei gains dominance in otherwise species-diverse regions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Glick et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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11. Co-limitation towards lower latitudes shapes global forest diversity gradients.

Author

Liang J, Gamarra JGP, Picard N, Zhou M, Pijanowski B, Jacobs DF, Reich PB, Crowther TW, Nabuurs GJ, de-Miguel S, Fang J, Woodall CW, Svenning JC, Jucker T, Bastin JF, Wiser SK, Slik F, Hérault B, Alberti G, Keppel G, Hengeveld GM, Ibisch PL, Silva CA, Ter Steege H, Peri PL, Coomes DA, Searle EB, von Gadow K, Jaroszewicz B, Abbasi AO, Abegg M, Yao YCA, Aguirre-Gutiérrez J, Zambrano AMA, Altman J, Alvarez-Dávila E, Álvarez-González JG, Alves LF, Amani BHK, Amani CA, Ammer C, Ilondea BA, Antón-Fernández C, Avitabile V, Aymard GA, Azihou AF, Baard JA, Baker TR, Balazy R, Bastian ML, Batumike R, Bauters M, Beeckman H, Benu NMH, Bitariho R, Boeckx P, Bogaert J, Bongers F, Bouriaud O, Brancalion PHS, Brandl S, Brearley FQ, Briseno-Reyes J, Broadbent EN, Bruelheide H, Bulte E, Catlin AC, Cazzolla Gatti R, César RG, Chen HYH, Chisholm C, Cienciala E, Colletta GD, Corral-Rivas JJ, Cuchietti A, Cuni-Sanchez A, Dar JA, Dayanandan S, de Haulleville T, Decuyper M, Delabye S, Derroire G, DeVries B, Diisi J, Do TV, Dolezal J, Dourdain A, Durrheim GP, Obiang NLE, Ewango CEN, Eyre TJ, Fayle TM, Feunang LFN, Finér L, Fischer M, Fridman J, Frizzera L, de Gasper AL, Gianelle D, Glick HB, Gonzalez-Elizondo MS, Gorenstein L, Habonayo R, Hardy OJ, Harris DJ, Hector A, Hemp A, Herold M, Hillers A, Hubau W, Ibanez T, Imai N, Imani G, Jagodzinski AM, Janecek S, Johannsen VK, Joly CA, Jumbam B, Kabelong BLPR, Kahsay GA, Karminov V, Kartawinata K, Kassi JN, Kearsley E, Kennard DK, Kepfer-Rojas S, Khan ML, Kigomo JN, Kim HS, Klauberg C, Klomberg Y, Korjus H, Kothandaraman S, Kraxner F, Kumar A, Kuswandi R, Lang M, Lawes MJ, Leite RV, Lentner G, Lewis SL, Libalah MB, Lisingo J, López-Serrano PM, Lu H, Lukina NV, Lykke AM, Maicher V, Maitner BS, Marcon E, Marshall AR, Martin EH, Martynenko O, Mbayu FM, Mbuvi MTE, Meave JA, Merow C, Miscicki S, Moreno VS, Morera A, Mukul SA, Müller JC, Murdjoko A, Nava-Miranda MG, Ndive LE, Neldner VJ, Nevenic RV, Nforbelie LN, Ngoh ML, N'Guessan AE, Ngugi MR, Ngute ASK, Njila ENN, Nyako MC, Ochuodho TO, Oleksyn J, Paquette A, Parfenova EI, Park M, Parren M, Parthasarathy N, Pfautsch S, Phillips OL, Piedade MTF, Piotto D, Pollastrini M, Poorter L, Poulsen JR, Poulsen AD, Pretzsch H, Rodeghiero M, Rolim SG, Rovero F, Rutishauser E, Sagheb-Talebi K, Saikia P, Sainge MN, Salas-Eljatib C, Salis A, Schall P, Schepaschenko D, Scherer-Lorenzen M, Schmid B, Schöngart J, Šebeň V, Sellan G, Selvi F, Serra-Diaz JM, Sheil D, Shvidenko AZ, Sist P, Souza AF, Stereńczak KJ, Sullivan MJP, Sundarapandian S, Svoboda M, Swaine MD, Targhetta N, Tchebakova N, Trethowan LA, Tropek R, Mukendi JT, Umunay PM, Usoltsev VA, Vaglio Laurin G, Valentini R, Valladares F, van der Plas F, Vega-Nieva DJ, Verbeeck H, Viana H, Vibrans AC, Vieira SA, Vleminckx J, Waite CE, Wang HF, Wasingya EK, Wekesa C, Westerlund B, Wittmann F, Wortel V, Zawiła-Niedźwiecki T, Zhang C, Zhao X, Zhu J, Zhu X, Zhu ZX, Zo-Bi IC, and Hui C

Subjects
Soil, Trees, Biodiversity, Forests
Abstract

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2022
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12. Resistance of African tropical forests to an extreme climate anomaly.

Author

Bennett AC, Dargie GC, Cuni-Sanchez A, Tshibamba Mukendi J, Hubau W, Mukinzi JM, Phillips OL, Malhi Y, Sullivan MJP, Cooper DLM, Adu-Bredu S, Affum-Baffoe K, Amani CA, Banin LF, Beeckman H, Begne SK, Bocko YE, Boeckx P, Bogaert J, Brncic T, Chezeaux E, Clark CJ, Daniels AK, de Haulleville T, Djuikouo Kamdem MN, Doucet JL, Evouna Ondo F, Ewango CEN, Feldpausch TR, Foli EG, Gonmadje C, Hall JS, Hardy OJ, Harris DJ, Ifo SA, Jeffery KJ, Kearsley E, Leal M, Levesley A, Makana JR, Mbayu Lukasu F, Medjibe VP, Mihindu V, Moore S, Nssi Begone N, Pickavance GC, Poulsen JR, Reitsma J, Sonké B, Sunderland TCH, Taedoumg H, Talbot J, Tuagben DS, Umunay PM, Verbeeck H, Vleminckx J, White LJT, Woell H, Woods JT, Zemagho L, and Lewis SL

Subjects
Carbon Cycle, Droughts, El Nino-Southern Oscillation, Hot Temperature, Humans, Seasons, Climate Change, Rainforest, Trees growth & development, Tropical Climate
Abstract

The responses of tropical forests to environmental change are critical uncertainties in predicting the future impacts of climate change. The positive phase of the 2015-2016 El Niño Southern Oscillation resulted in unprecedented heat and low precipitation in the tropics with substantial impacts on the global carbon cycle. The role of African tropical forests is uncertain as their responses to short-term drought and temperature anomalies have yet to be determined using on-the-ground measurements. African tropical forests may be particularly sensitive because they exist in relatively dry conditions compared with Amazonian or Asian forests, or they may be more resistant because of an abundance of drought-adapted species. Here, we report responses of structurally intact old-growth lowland tropical forests inventoried within the African Tropical Rainforest Observatory Network (AfriTRON). We use 100 long-term inventory plots from six countries each measured at least twice prior to and once following the 2015-2016 El Niño event. These plots experienced the highest temperatures and driest conditions on record. The record temperature did not significantly reduce carbon gains from tree growth or significantly increase carbon losses from tree mortality, but the record drought did significantly decrease net carbon uptake. Overall, the long-term biomass increase of these forests was reduced due to the El Niño event, but these plots remained a live biomass carbon sink (0.51 ± 0.40 Mg C ha -1 y -1 ) despite extreme environmental conditions. Our analyses, while limited to African tropical forests, suggest they may be more resistant to climatic extremes than Amazonian and Asian forests., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Published
2021
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13. Asynchronous carbon sink saturation in African and Amazonian tropical forests.

Author

Hubau W, Lewis SL, Phillips OL, Affum-Baffoe K, Beeckman H, Cuní-Sanchez A, Daniels AK, Ewango CEN, Fauset S, Mukinzi JM, Sheil D, Sonké B, Sullivan MJP, Sunderland TCH, Taedoumg H, Thomas SC, White LJT, Abernethy KA, Adu-Bredu S, Amani CA, Baker TR, Banin LF, Baya F, Begne SK, Bennett AC, Benedet F, Bitariho R, Bocko YE, Boeckx P, Boundja P, Brienen RJW, Brncic T, Chezeaux E, Chuyong GB, Clark CJ, Collins M, Comiskey JA, Coomes DA, Dargie GC, de Haulleville T, Kamdem MND, Doucet JL, Esquivel-Muelbert A, Feldpausch TR, Fofanah A, Foli EG, Gilpin M, Gloor E, Gonmadje C, Gourlet-Fleury S, Hall JS, Hamilton AC, Harris DJ, Hart TB, Hockemba MBN, Hladik A, Ifo SA, Jeffery KJ, Jucker T, Yakusu EK, Kearsley E, Kenfack D, Koch A, Leal ME, Levesley A, Lindsell JA, Lisingo J, Lopez-Gonzalez G, Lovett JC, Makana JR, Malhi Y, Marshall AR, Martin J, Martin EH, Mbayu FM, Medjibe VP, Mihindou V, Mitchard ETA, Moore S, Munishi PKT, Bengone NN, Ojo L, Ondo FE, Peh KS, Pickavance GC, Poulsen AD, Poulsen JR, Qie L, Reitsma J, Rovero F, Swaine MD, Talbot J, Taplin J, Taylor DM, Thomas DW, Toirambe B, Mukendi JT, Tuagben D, Umunay PM, van der Heijden GMF, Verbeeck H, Vleminckx J, Willcock S, Wöll H, Woods JT, and Zemagho L

Subjects
Africa, Atmosphere chemistry, Biomass, Brazil, Droughts, History, 20th Century, History, 21st Century, Models, Theoretical, Temperature, Carbon Dioxide metabolism, Carbon Sequestration, Forests, Trees metabolism, Tropical Climate
Abstract

Structurally intact tropical forests sequestered about half of the global terrestrial carbon uptake over the 1990s and early 2000s, removing about 15 per cent of anthropogenic carbon dioxide emissions 1-3 . Climate-driven vegetation models typically predict that this tropical forest 'carbon sink' will continue for decades 4,5 . Here we assess trends in the carbon sink using 244 structurally intact African tropical forests spanning 11 countries, compare them with 321 published plots from Amazonia and investigate the underlying drivers of the trends. The carbon sink in live aboveground biomass in intact African tropical forests has been stable for the three decades to 2015, at 0.66 tonnes of carbon per hectare per year (95 per cent confidence interval 0.53-0.79), in contrast to the long-term decline in Amazonian forests 6 . Therefore the carbon sink responses of Earth's two largest expanses of tropical forest have diverged. The difference is largely driven by carbon losses from tree mortality, with no detectable multi-decadal trend in Africa and a long-term increase in Amazonia. Both continents show increasing tree growth, consistent with the expected net effect of rising atmospheric carbon dioxide and air temperature 7-9 . Despite the past stability of the African carbon sink, our most intensively monitored plots suggest a post-2010 increase in carbon losses, delayed compared to Amazonia, indicating asynchronous carbon sink saturation on the two continents. A statistical model including carbon dioxide, temperature, drought and forest dynamics accounts for the observed trends and indicates a long-term future decline in the African sink, whereas the Amazonian sink continues to weaken rapidly. Overall, the uptake of carbon into Earth's intact tropical forests peaked in the 1990s. Given that the global terrestrial carbon sink is increasing in size, independent observations indicating greater recent carbon uptake into the Northern Hemisphere landmass 10 reinforce our conclusion that the intact tropical forest carbon sink has already peaked. This saturation and ongoing decline of the tropical forest carbon sink has consequences for policies intended to stabilize Earth's climate.

Published
2020
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14. The persistence of carbon in the African forest understory.

Author

Hubau W, De Mil T, Van den Bulcke J, Phillips OL, Angoboy Ilondea B, Van Acker J, Sullivan MJP, Nsenga L, Toirambe B, Couralet C, Banin LF, Begne SK, Baker TR, Bourland N, Chezeaux E, Clark CJ, Collins M, Comiskey JA, Cuni-Sanchez A, Deklerck V, Dierickx S, Doucet JL, Ewango CEN, Feldpausch TR, Gilpin M, Gonmadje C, Hall JS, Harris DJ, Hardy OJ, Kamdem MD, Kasongo Yakusu E, Lopez-Gonzalez G, Makana JR, Malhi Y, Mbayu FM, Moore S, Mukinzi J, Pickavance G, Poulsen JR, Reitsma J, Rousseau M, Sonké B, Sunderland T, Taedoumg H, Talbot J, Tshibamba Mukendi J, Umunay PM, Vleminckx J, White LJT, Zemagho L, Lewis SL, and Beeckman H

Subjects
Carbon Cycle, Democratic Republic of the Congo, Time Factors, Trees growth & development, Tropical Climate, Carbon analysis, Carbon Sequestration, Forests, Trees physiology
Abstract

Quantifying carbon dynamics in forests is critical for understanding their role in long-term climate regulation 1-4 . Yet little is known about tree longevity in tropical forests 3,5-8 , a factor that is vital for estimating carbon persistence 3,4 . Here we calculate mean carbon age (the period that carbon is fixed in trees 7 ) in different strata of African tropical forests using (1) growth-ring records with a unique timestamp accurately demarcating 66 years of growth in one site and (2) measurements of diameter increments from the African Tropical Rainforest Observation Network (23 sites). We find that in spite of their much smaller size, in understory trees mean carbon age (74 years) is greater than in sub-canopy (54 years) and canopy (57 years) trees and similar to carbon age in emergent trees (66 years). The remarkable carbon longevity in the understory results from slow and aperiodic growth as an adaptation to limited resource availability 9-11 . Our analysis also reveals that while the understory represents a small share (11%) of the carbon stock 12,13 , it contributes disproportionally to the forest carbon sink (20%). We conclude that accounting for the diversity of carbon age and carbon sequestration among different forest strata is critical for effective conservation management 14-16 and for accurate modelling of carbon cycling 4 .

Published
2019
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15. 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, and Phillips OL

Abstract

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 conservative threshold of sampling 50 trees per location for height measurement, and including the ten trees with the largest diameter in this sample.

Published
2018
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16. Diversity and carbon storage across the tropical forest biome.

Author

Sullivan MJ, 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 LJ, Affum-Baffoe K, Aiba SI, de Almeida EC, de Oliveira EA, Alvarez-Loayza P, Dávila EÁ, Andrade A, Aragão LE, Ashton P, Aymard C GA, Baker TR, Balinga M, Banin LF, Baraloto C, Bastin JF, Berry N, Bogaert J, Bonal D, Bongers F, Brienen R, Camargo JL, Cerón C, Moscoso VC, Chezeaux E, Clark CJ, Pacheco ÁC, Comiskey JA, Valverde FC, Coronado EN, Dargie G, Davies SJ, De Canniere C, Djuikouo K MN, Doucet JL, Erwin TL, Espejo JS, Ewango CE, Fauset S, Feldpausch TR, Herrera R, Gilpin M, Gloor E, Hall JS, Harris DJ, Hart TB, Kartawinata K, Kho LK, Kitayama K, Laurance SG, Laurance WF, Leal ME, Lovejoy T, Lovett JC, Lukasu FM, Makana JR, Malhi Y, Maracahipes L, Marimon BS, Junior BH, Marshall AR, Morandi PS, Mukendi JT, Mukinzi J, Nilus R, Vargas PN, Camacho NC, 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, Silveira M, Slik JW, Steel L, Taedoumg H, Tan S, Terborgh JW, Thomas SC, Toledo M, Umunay PM, Gamarra LV, Vieira IC, Vos VA, Wang O, Willcock S, and Zemagho L

Subjects
Africa, Americas, Asia, Tropical Climate, Biodiversity, Carbon analysis, Forests, Plants chemistry, Plants classification
Abstract

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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17. Corrigendum: Mapping tree density at a global scale.

Author

Crowther TW, Glick HB, Covey KR, Bettigole C, Maynard DS, Thomas SM, Smith JR, Hintler G, Duguid MC, Amatulli G, Tuanmu MN, Jetz W, Salas C, Stam C, Piotto D, Tavani R, Green S, Bruce G, Williams SJ, Wiser SK, Huber MO, Hengeveld GM, Nabuurs GJ, Tikhonova E, Borchardt P, Li CF, Powrie LW, Fischer M, Hemp A, Homeier J, Cho P, Vibrans AC, Umunay PM, Piao SL, Rowe CW, Ashton MS, Crane PR, and Bradford MA

Published
2016
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18. Mapping tree density at a global scale.

Author

Crowther TW, Glick HB, Covey KR, Bettigole C, Maynard DS, Thomas SM, Smith JR, Hintler G, Duguid MC, Amatulli G, Tuanmu MN, Jetz W, Salas C, Stam C, Piotto D, Tavani R, Green S, Bruce G, Williams SJ, Wiser SK, Huber MO, Hengeveld GM, Nabuurs GJ, Tikhonova E, Borchardt P, Li CF, Powrie LW, Fischer M, Hemp A, Homeier J, Cho P, Vibrans AC, Umunay PM, Piao SL, Rowe CW, Ashton MS, Crane PR, and Bradford MA

Subjects
Ecology statistics & numerical data, Ecosystem, Forestry statistics & numerical data, Population Density, Reproducibility of Results, Forests, Geographic Mapping, Trees growth & development
Abstract

The global extent and distribution of forest trees is central to our understanding of the terrestrial biosphere. We provide the first spatially continuous map of forest tree density at a global scale. This map reveals that the global number of trees is approximately 3.04 trillion, an order of magnitude higher than the previous estimate. Of these trees, approximately 1.39 trillion exist in tropical and subtropical forests, with 0.74 trillion in boreal regions and 0.61 trillion in temperate regions. Biome-level trends in tree density demonstrate the importance of climate and topography in controlling local tree densities at finer scales, as well as the overwhelming effect of humans across most of the world. Based on our projected tree densities, we estimate that over 15 billion trees are cut down each year, and the global number of trees has fallen by approximately 46% since the start of human civilization.

Published
2015
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