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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. An integrated pan-tropical biomass map using multiple reference datasets

Author

Avitabile, V, Herold, M, Heuvelink, GBM, Lewis, SL, Phillips, OL, Asner, GP, Armston, J, Ashton, PS, Banin, L, Bayol, N, Berry, NJ, Boeckx, P, de Jong, BHJ, Devries, B, Girardin, CAJ, Kearsley, E, Lindsell, JA, Lopez-Gonzalez, G, Lucas, R, Malhi, Y, Morel, A, Mitchard, ETA, Nagy, L, Qie, L, Quinones, MJ, Ryan, CM, Ferry, SJW, Sunderland, T, Laurin, GV, Gatti, RC, Valentini, R, Verbeeck, H, Wijaya, A, Willcock, S, Avitabile, V, Herold, M, Heuvelink, GBM, Lewis, SL, Phillips, OL, Asner, GP, Armston, J, Ashton, PS, Banin, L, Bayol, N, Berry, NJ, Boeckx, P, de Jong, BHJ, Devries, B, Girardin, CAJ, Kearsley, E, Lindsell, JA, Lopez-Gonzalez, G, Lucas, R, Malhi, Y, Morel, A, Mitchard, ETA, Nagy, L, Qie, L, Quinones, MJ, Ryan, CM, Ferry, SJW, Sunderland, T, Laurin, GV, Gatti, RC, Valentini, R, Verbeeck, H, Wijaya, A, and Willcock, S

Abstract

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha-1 vs. 21 and 28 Mg ha-1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

Published
2016

4. 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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5. 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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6. Drivers of aboveground wood production in a lowland tropical forest of West Africa: teasing apart the roles of tree density, tree diversity, soil phosphorus, and historical logging.

Author

Jucker T, Sanchez AC, Lindsell JA, Allen HD, Amable GS, and Coomes DA

Abstract

Tropical forests currently play a key role in regulating the terrestrial carbon cycle and abating climate change by storing carbon in wood. However, there remains considerable uncertainty as to whether tropical forests will continue to act as carbon sinks in the face of increased pressure from expanding human activities. Consequently, understanding what drives productivity in tropical forests is critical. We used permanent forest plot data from the Gola Rainforest National Park (Sierra Leone) - one of the largest tracts of intact tropical moist forest in West Africa - to explore how (1) stand basal area and tree diversity, (2) past disturbance associated with past logging, and (3) underlying soil nutrient gradients interact to determine rates of aboveground wood production (AWP). We started by statistically modeling the diameter growth of individual trees and used these models to estimate AWP for 142 permanent forest plots. We then used structural equation modeling to explore the direct and indirect pathways which shape rates of AWP. Across the plot network, stand basal area emerged as the strongest determinant of AWP, with densely packed stands exhibiting the fastest rates of AWP. In addition to stand packing density, both tree diversity and soil phosphorus content were also positively related to productivity. By contrast, historical logging activities negatively impacted AWP through the removal of large trees, which contributed disproportionately to productivity. Understanding what determines variation in wood production across tropical forest landscapes requires accounting for multiple interacting drivers - with stand structure, tree diversity, and soil nutrients all playing a key role. Importantly, our results also indicate that logging activities can have a long-lasting impact on a forest's ability to sequester and store carbon, emphasizing the importance of safeguarding old-growth tropical forests.

Published
2016
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7. An integrated pan-tropical biomass map using multiple reference datasets.

Author

Avitabile V, Herold M, Heuvelink GB, Lewis SL, Phillips OL, Asner GP, Armston J, Ashton PS, Banin L, Bayol N, Berry NJ, Boeckx P, de Jong BH, DeVries B, Girardin CA, Kearsley E, Lindsell JA, Lopez-Gonzalez G, Lucas R, Malhi Y, Morel A, Mitchard ET, Nagy L, Qie L, Quinones MJ, Ryan CM, Ferry SJ, Sunderland T, Laurin GV, Gatti RC, Valentini R, Verbeeck H, Wijaya A, and Willcock S

Subjects
Datasets as Topic, Models, Theoretical, Trees, Tropical Climate, Biomass, Maps as Topic
Abstract

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets., (© 2015 John Wiley & Sons Ltd.)

Published
2016
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8. The conservation value of degraded forests for agile gibbons Hylobates agilis.

Author

Lee DC, Powell VJ, and Lindsell JA

Subjects
Animals, Conservation of Natural Resources, Indonesia, Population Density, Vocalization, Animal, Ecosystem, Forests, Hylobates
Abstract

All gibbon species are globally threatened with extinction yet conservation efforts are undermined by a lack of population and ecological data. Agile gibbons (Hylobates agilis) occur in Sumatra, Indonesia and adjacent mainland Southeast Asia. Population densities are known from four sites (three in Sumatra) while little is known about their ability to tolerate habitat degradation. We conducted a survey of agile gibbons in Harapan Rainforest, a lowland forest site in Sumatra. The area has been severely degraded by selective logging and encroachment but is now managed for ecosystem restoration. We used two survey methods: an established point count method for gibbons with some modifications, and straight line transects using auditory detections. Surveys were conducted in the three main forest types prevalent at the site: high, medium, and low canopy cover secondary forests. Mean group density estimates were higher from point counts than from line transects, and tended to be higher in less degraded forests within the study site. We consider points more time efficient and reliable than transects since detectability of gibbons was higher from points per unit effort. We recommend the additional use of Distance sampling methods to account for imperfect detection and provide other recommendations to improve surveys of gibbons. We estimate that the site holds at least 6,070 and as many as 11,360 gibbons. Our results demonstrate that degraded forests can be extremely important for the conservation of agile gibbons and that efforts to protect and restore such sites could contribute significantly to the conservation of the species., (© 2014 Wiley Periodicals, Inc.)

Published
2015
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9. Biodiversity mapping in a tropical West African forest with airborne hyperspectral data.

Author

Vaglio Laurin G, Cheung-Wai Chan J, Chen Q, Lindsell JA, Coomes DA, Guerriero L, Del Frate F, Miglietta F, and Valentini R

Subjects
Artificial Intelligence, Biodiversity, Computer Simulation, Conservation of Natural Resources, Data Collection methods, Regression Analysis, Sierra Leone, Signal Processing, Computer-Assisted, Tropical Climate, Rainforest, Trees
Abstract

Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m(2) in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m(2) resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R(2) = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales.

Published
2014
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