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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. Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

Author

Correa, DF, Stevenson, PR, Umaña, MN, Coelho, LDS, Lima Filho, DDA, Salomão, RP, Amaral, ILD, Wittmann, F, Matos, FDDA, Castilho, CV, Phillips, OL, Guevara, JE, Carim, MDJV, Magnusson, WE, Sabatier, D, Molino, JF, Irume, MV, Martins, MP, Guimarães, JRDS, Bánki, OS, Piedade, MTF, Pitman, NCA, Monteagudo Mendoza, A, Ramos, JF, Luize, BG, Novo, EMMDL, Núñez Vargas, P, Silva, TSF, Venticinque, EM, Manzatto, AG, Reis, NFC, Terborgh, JW, Casula, KR, Honorio Coronado, EN, Montero, JC, Schöngart, J, Cárdenas López, D, Costa, FRC, Quaresma, AC, Zartman, CE, Killeen, TJ, Marimon, BS, Marimon-Junior, BH, Vasquez, R, Mostacedo, B, Demarchi, LO, Feldpausch, TR, Assis, RL, Baraloto, C, Engel, J, Petronelli, P, Castellanos, H, Medeiros, MBD, Simon, MF, Andrade, A, Camargo, JL, Laurance, SGW, Laurance, WF, Maniguaje Rincón, L, Schietti, J, Sousa, TR, Farias, EDS, Lopes, MA, Magalhães, JLL, Nascimento, HEM, Queiroz, HLD, Aymard C., GA, Brienen, R, Cardenas Revilla, JD, Vieira, ICG, Cintra, BBL, Feitosa, YO, Duivenvoorden, JF, Mogollón, HF, Araujo-Murakami, A, Ferreira, LV, Lozada, Comiskey, JA, De Toledo, JJ, Damasco, G, Dávila, N, García-Villacorta, R, Lopes, A, Vicentini, A, Draper, FC, Castaño Arboleda, N, Cornejo Valverde, F, Alonso, A, Dallmeier, F, Gomes, VHF, Neill, D, De Aguiar, DPP, Arroyo, L, Carvalho, FA, De Souza, FC, Amaral, DDD, Feeley, KJ, Gribel, R, Pansonato, MP, Barlow, J, Correa, DF [0000-0002-6510-4906], Stevenson, PR [0000-0003-2394-447X], Umaña, MN [0000-0001-5876-7720], Luize, BG [0000-0002-8384-8386], Sousa, TR [0000-0003-0598-4996], Comiskey, JA [0000-0001-6710-1269], Alonso, A [0000-0001-6860-8432], and Apollo - University of Cambridge Repository

Subjects
anemochory, synzoochory, resource-availability hypothesis, hydrochory, flooded forests, dispersal agents, disperser-availability hypothesis, terra-firme forests, endozoochory, Amazonian rain forests
Abstract

Funder: Brazilian Program for Biodiversity Research, Funder: Fundação de Amparo à Pesquisa do Estado do Amazonas; Id: http://dx.doi.org/10.13039/501100004916, Funder: National Institute for Amazonian Biodiversity, Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser‐availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource‐availability hypothesis). Time period: Tree‐inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree‐inventory plots across terra‐firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance‐weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra‐firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser‐availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types.

Published
2023

4. Plant Fluorescence Measured under Two Extreme Lighting Conditions Using a Passive Spectroradiometer

Author

Gualda G, Silva Tsf, R. Bennartz, T. Merrick, J. Rausch, and Jorge Mlsp

Subjects
environmental_sciences, Spectroradiometer, Chemistry, Carbon uptake, Fluorescence, Remote sensing
Abstract

In this study, we evaluated chlorophyll fluorescence (CF) under two extreme illumination conditions at plant scale with a passive spectroradiometer. Fluorescence (F) was estimated by reading directly from radiance spectra of a variety of plants illuminated with light-emitting diode (LED) grow lights in the laboratory. Solar-induced fluorescence (SIF) was estimated from spectral measurements of the same plants under sunlight using the Fraunhofer Line Depth (FLD) method. Chlorophyll fluorescence yield (Fyield) and solar-induced fluorescence yield (SIFyield) were calculated by normalizing F and SIF with absorbed photosynthetically active radiation (APAR). Two approaches to estimating APAR were compared: utilizing white reference spectra and reflected spectra versus white reference spectra combined with the fraction of absorbed photosynthetically active radiation (fPAR) derived from literature. Average F and SIF were different by a factor of approximately twenty-four (F = 0.110 ± 0.038 Wm−2μm−1sr−1 versus SIF = 2.60 ± 1.87 Wm−2μm−1sr−1). In contrast, the average normalized values Fyield and SIFyield were within the margin of error of one another (Fyield = 0.022 ± 0.008 μm−1sr−1 and SIFyield = 0.030 ± 0.020 μm−1sr−1). This study highlights the influence of APAR on CF and the importance of properly accounting for it when estimating yield and demonstrates the ability of two simple and portable experimental setups with a passive instrument to obtain fluorescence metrics.

Published
2017

5. The biogeography of the Amazonian tree flora.

Author

Luize BG, Tuomisto H, Ekelschot R, Dexter KG, Amaral ILD, Coelho LS, Matos FDA, Lima Filho DA, Salomão RP, Wittmann F, Castilho CV, Carim MJV, Guevara JE, Phillips OL, Magnusson WE, Sabatier D, Cardenas Revilla JD, Molino JF, Irume MV, Martins MP, Guimarães JRDS, Ramos JF, Bánki OS, Piedade MTF, Cárdenas López D, Pitman NCA, Demarchi LO, Schöngart J, de Leão Novo EMM, Núñez Vargas P, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Honorio Coronado EN, Mendoza AM, Montero JC, Costa FRC, Feldpausch TR, Quaresma AC, Castaño Arboleda N, Zartman CE, Killeen TJ, Marimon BS, Marimon BH, Vasquez R, Mostacedo B, Assis RL, Baraloto C, do Amaral DD, Engel J, Petronelli P, Castellanos H, de Medeiros MB, Simon MF, Andrade A, Camargo JL, Laurance WF, Laurance SGW, Rincón LM, Schietti J, Sousa TR, Mori GB, Farias ES, Lopes MA, Magalhães JLL, Nascimento HEM, de Queiroz HL, Vasconcelos CC, Aymard C GA, Brienen R, Stevenson PR, Araujo-Murakami A, Cintra BBL, Baker TR, Feitosa YO, Mogollón HF, Duivenvoorden JF, Peres CA, Silman MR, Ferreira LV, Lozada JR, Comiskey JA, de Toledo JJ, Damasco G, Dávila N, Draper FC, García-Villacorta R, Lopes A, Vicentini A, Valverde FC, Alonso A, Arroyo L, Dallmeier F, Gomes VHF, Jimenez EM, Neill D, Peñuela Mora MC, Noronha JC, de Aguiar DPP, Barbosa FR, Bredin YK, Carpanedo RS, Carvalho FA, Souza FC, Feeley KJ, Gribel R, Haugaasen T, Hawes JE, Pansonato MP, Pipoly JJ 3rd, Paredes MR, Rodrigues DJ, Barlow J, Berenguer E, da Silva IB, Ferreira MJ, Ferreira J, Fine PVA, Guedes MC, Levis C, Licona JC, Villa Zegarra BE, Vos VA, Cerón C, Durgante FM, Fonty É, Henkel TW, Householder JE, Huamantupa-Chuquimaco I, Silveira M, Stropp J, Thomas R, Daly D, Milliken W, Molina GP, Pennington T, Vieira ICG, Albuquerque BW, Campelo W, Fuentes A, Klitgaard B, Pena JLM, Tello JS, Vriesendorp C, Chave J, Di Fiore A, Hilário RR, Pereira LO, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Balee W, Barbosa EM, Bonates LCM, Dávila Doza HP, Zárate Gómez R, Gonzales T, Gallardo Gonzales GP, Hoffman B, Junqueira AB, Malhi Y, Miranda IPA, Pinto LFM, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Zent S, Zent EL, Endara MJ, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Monteiro Flores B, Galbraith D, Holmgren M, Kalamandeen M, Lobo G, Torres Montenegro L, Nascimento MT, Oliveira AA, Pombo MM, Ramirez-Angulo H, Rocha M, Scudeller VV, Umaña MN, van der Heijden G, Vilanova Torre E, Vargas TM, Ahuite Reategui MA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Parada GA, Torres-Lezama A, Urrego Giraldo LE, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Fortier RP, Garcia-Cabrera K, Hernandez L, Palacios Cuenca W, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Valderrama Sandoval EH, Valenzuela Gamarra L, Hirota M, Palma-Silva C, and Ter Steege H

Subjects
Brazil, Biodiversity, Forests, Soil chemistry, Geography, Phylogeography, Trees
Abstract

We describe the geographical variation in tree species composition across Amazonian forests and show how environmental conditions are associated with species turnover. Our analyses are based on 2023 forest inventory plots (1 ha) that provide abundance data for a total of 5188 tree species. Within-plot species composition reflected both local environmental conditions (especially soil nutrients and hydrology) and geographical regions. A broader-scale view of species turnover was obtained by interpolating the relative tree species abundances over Amazonia into 47,441 0.1-degree grid cells. Two main dimensions of spatial change in tree species composition were identified. The first was a gradient between western Amazonia at the Andean forelands (with young geology and relatively nutrient-rich soils) and central-eastern Amazonia associated with the Guiana and Brazilian Shields (with more ancient geology and poor soils). The second gradient was between the wet forests of the northwest and the drier forests in southern Amazonia. Isolines linking cells of similar composition crossed major Amazonian rivers, suggesting that tree species distributions are not limited by rivers. Even though some areas of relatively sharp species turnover were identified, mostly the tree species composition changed gradually over large extents, which does not support delimiting clear discrete biogeographic regions within Amazonia., (© 2024. The Author(s).)

Published
2024
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6. Author Correction: One sixth of Amazonian tree diversity is dependent on river floodplains.

Author

Householder JE, Wittmann F, Schöngart J, Piedade MTF, Junk WJ, Latrubesse EM, Quaresma AC, Demarchi LO, de S Lobo G, Aguiar DPP, Assis RL, Lopes A, Parolin P, Leão do Amaral I, Coelho LS, de Almeida Matos FD, Lima Filho DA, Salomão RP, Castilho CV, Guevara-Andino JE, Carim MJV, Phillips OL, Cárdenas López D, Magnusson WE, Sabatier D, Revilla JDC, Molino JF, Irume MV, Martins MP, Guimarães JRDS, Ramos JF, Rodrigues DJ, Bánki OS, Peres CA, Pitman NCA, Hawes JE, Almeida EJ, Barbosa LF, Cavalheiro L, Dos Santos MCV, Luize BG, Novo EMML, Núñez Vargas P, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Costa FRC, Honorio Coronado EN, Monteagudo Mendoza A, Montero JC, Feldpausch TR, Aymard C GA, Baraloto C, Castaño Arboleda N, Engel J, Petronelli P, Zartman CE, Killeen TJ, Rincón LM, Marimon BS, Marimon-Junior BH, Schietti J, Sousa TR, Vasquez R, Mostacedo B, Dantas do Amaral D, Castellanos H, Medeiros MB, Simon MF, Andrade A, Camargo JL, Laurance WF, Laurance SGW, Farias ES, Lopes MA, Magalhães JLL, Mendonça Nascimento HE, Queiroz HL, Brienen R, Stevenson PR, Araujo-Murakami A, Baker TR, Cintra BBL, Feitosa YO, Mogollón HF, Noronha JC, Barbosa FR, de Sá Carpanedo R, Duivenvoorden JF, Silman MR, Ferreira LV, Levis C, Lozada JR, Comiskey JA, Draper FC, Toledo JJ, Damasco G, Dávila N, García-Villacorta R, Vicentini A, Cornejo Valverde F, Alonso A, Arroyo L, Dallmeier F, Gomes VHF, Jimenez EM, Neill D, Peñuela Mora MC, Carvalho FA, Coelho de Souza F, Feeley KJ, Gribel R, Pansonato MP, Ríos Paredes M, Barlow J, Berenguer E, Dexter KG, Ferreira J, Fine PVA, Guedes MC, Huamantupa-Chuquimaco I, Licona JC, Pennington T, Villa Zegarra BE, Vos VA, Cerón C, Fonty É, Henkel TW, Maas P, Pos E, Silveira M, Stropp J, Thomas R, Daly D, Milliken W, Pardo Molina G, Vieira ICG, Albuquerque BW, Campelo W, Emilio T, Fuentes A, Klitgaard B, Marcelo Pena JL, Souza PF, Tello JS, Vriesendorp C, Chave J, Di Fiore A, Hilário RR, Pereira LO, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Balee W, Barbosa EM, Bonates LCM, Doza HPD, Gómez RZ, Gonzales T, Gonzales GPG, Hoffman B, Junqueira AB, Malhi Y, Miranda IPA, Mozombite-Pinto LF, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Zent S, Zent EL, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Flores BM, Galbraith D, Holmgren M, Kalamandeen M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña MN, van der Heijden G, Vilanova Torre E, Ahuite Reategui MA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Parada GA, Torres-Lezama A, Urrego Giraldo LE, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Garcia-Cabrera K, Hernandez L, Palacios Cuenca W, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Valderrama Sandoval EH, Valenzuela Gamarra L, and Ter Steege H

Published
2024
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7. One sixth of Amazonian tree diversity is dependent on river floodplains.

Author

Householder JE, Wittmann F, Schöngart J, Piedade MTF, Junk WJ, Latrubesse EM, Quaresma AC, Demarchi LO, de S Lobo G, Aguiar DPP, Assis RL, Lopes A, Parolin P, Leão do Amaral I, Coelho LS, de Almeida Matos FD, Lima Filho DA, Salomão RP, Castilho CV, Guevara-Andino JE, Carim MJV, Phillips OL, Cárdenas López D, Magnusson WE, Sabatier D, Revilla JDC, Molino JF, Irume MV, Martins MP, Guimarães JRDS, Ramos JF, Rodrigues DJ, Bánki OS, Peres CA, Pitman NCA, Hawes JE, Almeida EJ, Barbosa LF, Cavalheiro L, Dos Santos MCV, Luize BG, Novo EMML, Núñez Vargas P, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Costa FRC, Honorio Coronado EN, Monteagudo Mendoza A, Montero JC, Feldpausch TR, Aymard C GA, Baraloto C, Castaño Arboleda N, Engel J, Petronelli P, Zartman CE, Killeen TJ, Rincón LM, Marimon BS, Marimon-Junior BH, Schietti J, Sousa TR, Vasquez R, Mostacedo B, Dantas do Amaral D, Castellanos H, Medeiros MB, Simon MF, Andrade A, Camargo JL, Laurance WF, Laurance SGW, Farias ES, Lopes MA, Magalhães JLL, Mendonça Nascimento HE, Queiroz HL, Brienen R, Stevenson PR, Araujo-Murakami A, Baker TR, Cintra BBL, Feitosa YO, Mogollón HF, Noronha JC, Barbosa FR, de Sá Carpanedo R, Duivenvoorden JF, Silman MR, Ferreira LV, Levis C, Lozada JR, Comiskey JA, Draper FC, Toledo JJ, Damasco G, Dávila N, García-Villacorta R, Vicentini A, Cornejo Valverde F, Alonso A, Arroyo L, Dallmeier F, Gomes VHF, Jimenez EM, Neill D, Peñuela Mora MC, Carvalho FA, Coelho de Souza F, Feeley KJ, Gribel R, Pansonato MP, Ríos Paredes M, Barlow J, Berenguer E, Dexter KG, Ferreira J, Fine PVA, Guedes MC, Huamantupa-Chuquimaco I, Licona JC, Pennington T, Villa Zegarra BE, Vos VA, Cerón C, Fonty É, Henkel TW, Maas P, Pos E, Silveira M, Stropp J, Thomas R, Daly D, Milliken W, Pardo Molina G, Vieira ICG, Albuquerque BW, Campelo W, Emilio T, Fuentes A, Klitgaard B, Marcelo Pena JL, Souza PF, Tello JS, Vriesendorp C, Chave J, Di Fiore A, Hilário RR, Pereira LO, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Balee W, Barbosa EM, Bonates LCM, Doza HPD, Gómez RZ, Gonzales T, Gonzales GPG, Hoffman B, Junqueira AB, Malhi Y, Miranda IPA, Mozombite-Pinto LF, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Zent S, Zent EL, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Flores BM, Galbraith D, Holmgren M, Kalamandeen M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña MN, van der Heijden G, Vilanova Torre E, Ahuite Reategui MA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Parada GA, Torres-Lezama A, Urrego Giraldo LE, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Garcia-Cabrera K, Hernandez L, Palacios Cuenca W, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Valderrama Sandoval EH, Valenzuela Gamarra L, and Ter Steege H

Subjects
Brazil, Forests, Biodiversity, Trees, Rivers, Floods
Abstract

Amazonia's floodplain system is the largest and most biodiverse on Earth. Although forests are crucial to the ecological integrity of floodplains, our understanding of their species composition and how this may differ from surrounding forest types is still far too limited, particularly as changing inundation regimes begin to reshape floodplain tree communities and the critical ecosystem functions they underpin. Here we address this gap by taking a spatially explicit look at Amazonia-wide patterns of tree-species turnover and ecological specialization of the region's floodplain forests. We show that the majority of Amazonian tree species can inhabit floodplains, and about a sixth of Amazonian tree diversity is ecologically specialized on floodplains. The degree of specialization in floodplain communities is driven by regional flood patterns, with the most compositionally differentiated floodplain forests located centrally within the fluvial network and contingent on the most extraordinary flood magnitudes regionally. Our results provide a spatially explicit view of ecological specialization of floodplain forest communities and expose the need for whole-basin hydrological integrity to protect the Amazon's tree diversity and its function., (© 2024. The Author(s).)

Published
2024
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8. Tree species occurring in Amazonian wetland forests consistently show broader range sizes and niche breadths than trees in upland forests.

Author

Luize BG, Palma-Silva C, Siqueira T, and Silva TSF

Abstract

Generally, species with broad niches also show large range sizes. We investigated the relationship between hydrological niche breadth and geographic range size for Amazonian tree species seeking to understand the role of habitat specialization to Amazonian wetlands and upland forests on the current distribution of tree species. We obtained 571,092 valid occurrence points from GBIF and SpeciesLink to estimate the range size and the niche breadth of 76% of all known Amazonian tree species (5150 tree species). Hydrological niche breadth was measured on different unidimensional axes defined by (1) total annual precipitation; (2) precipitation seasonality; (3) actual evapotranspiration; and (4) water table depth. Geographic range sizes were estimated using alpha-hull adjustments. General linear models were used to relate niche breadth to range size while contrasting tree species occurring and not occurring in wetlands. The hydrological niche breadth of Amazonian tree species varied mostly along the water table depth axis. The average range size for an Amazonian tree species was 751,000 km 2 (median of 154,000 km 2 and standard deviation of 1,550,000 km 2 ). Niche breadth-range size relationships for Amazonian tree species were positive for all models, and the explanatory power of the models improved when including whether a species occurred in wetlands or in terrestrial uplands. Wetland species had steeper positive slopes for the niche breadth-range size relationship, and consistently larger range sizes for a given niche breadth. Amazonian tree species varied strongly in hydrological niche breadth and range size, but most species had narrow niche breadths and range sizes. Our results suggest that the South American riverscape may have been acting as a corridor for species dispersal in the Neotropical lowlands., (© 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Published
2024
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9. 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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10. Overlooking vegetation loss outside forests imperils the Brazilian Cerrado and other non-forest biomes.

Author

da Conceição Bispo P, Picoli MCA, Marimon BS, Marimon Junior BH, Peres CA, Menor IO, Silva DE, de Figueiredo Machado F, Alencar AAC, de Almeida CA, Anderson LO, Aragão LEOC, Breunig FM, Bustamante M, Dalagnol R, Diniz-Filho JAF, Ferreira LG, Ferreira ME, Fisch G, Galvão LS, Giarolla A, Gomes AR, de Marco Junior P, Kuck TN, Lehmann CER, Lemes MR, Liesenberg V, Loyola R, Macedo MN, de Souza Mendes F, do Couto de Miranda S, Morton DC, Moura YM, Oldekop JA, Ramos-Neto MB, Rosan TM, Saatchi S, Sano EE, Segura-Garcia C, Shimbo JZ, Silva TSF, Trevisan DP, Zimbres B, Wiederkehr NC, and Silva-Junior CHL

Subjects
Brazil, Forests, Ecosystem
Published
2024
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11. Mapping density, diversity and species-richness of the Amazon tree flora.

Author

Ter Steege H, Pitman NCA, do Amaral IL, de Souza Coelho L, de Almeida Matos FD, de Andrade Lima Filho D, Salomão RP, Wittmann F, Castilho CV, Guevara JE, Veiga Carim MJ, Phillips OL, Magnusson WE, Sabatier D, Revilla JDC, Molino JF, Irume MV, Martins MP, da Silva Guimarães JR, Ramos JF, Bánki OS, Piedade MTF, Cárdenas López D, Rodrigues DJ, Demarchi LO, Schöngart J, Almeida EJ, Barbosa LF, Cavalheiro L, Dos Santos MCV, Luize BG, de Leão Novo EMM, Vargas PN, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Honorio Coronado EN, Monteagudo Mendoza A, Montero JC, Costa FRC, Feldpausch TR, Quaresma AC, Castaño Arboleda N, Zartman CE, Killeen TJ, Marimon BS, Marimon-Junior BH, Vasquez R, Mostacedo B, Assis RL, Baraloto C, do Amaral DD, Engel J, Petronelli P, Castellanos H, de Medeiros MB, Simon MF, Andrade A, Camargo JL, Laurance WF, Laurance SGW, Maniguaje Rincón L, Schietti J, Sousa TR, de Sousa Farias E, Lopes MA, Magalhães JLL, Nascimento HEM, de Queiroz HL, Aymard C GA, Brienen R, Stevenson PR, Araujo-Murakami A, Baker TR, Cintra BBL, Feitosa YO, Mogollón HF, Duivenvoorden JF, Peres CA, Silman MR, Ferreira LV, Lozada JR, Comiskey JA, Draper FC, de Toledo JJ, Damasco G, García-Villacorta R, Lopes A, Vicentini A, Cornejo Valverde F, Alonso A, Arroyo L, Dallmeier F, Gomes VHF, Jimenez EM, Neill D, Peñuela Mora MC, Noronha JC, de Aguiar DPP, Barbosa FR, Bredin YK, de Sá Carpanedo R, Carvalho FA, de Souza FC, Feeley KJ, Gribel R, Haugaasen T, Hawes JE, Pansonato MP, Ríos Paredes M, Barlow J, Berenguer E, da Silva IB, Ferreira MJ, Ferreira J, Fine PVA, Guedes MC, Levis C, Licona JC, Villa Zegarra BE, Vos VA, Cerón C, Durgante FM, Fonty É, Henkel TW, Householder JE, Huamantupa-Chuquimaco I, Pos E, Silveira M, Stropp J, Thomas R, Daly D, Dexter KG, Milliken W, Molina GP, Pennington T, Vieira ICG, Weiss Albuquerque B, Campelo W, Fuentes A, Klitgaard B, Pena JLM, Tello JS, Vriesendorp C, Chave J, Di Fiore A, Hilário RR, de Oliveira Pereira L, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Balee W, Barbosa EM, de Matos Bonates LC, Dávila Doza HP, Zárate Gómez R, Gonzales T, Gallardo Gonzales GP, Hoffman B, Junqueira AB, Malhi Y, de Andrade Miranda IP, Pinto LFM, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Zent EL, Zent S, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Flores BM, Galbraith D, Holmgren M, Kalamandeen M, Lobo G, Torres Montenegro L, Nascimento MT, Oliveira AA, Pombo MM, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña MN, van der Heijden G, Vilanova Torre E, Reategui MAA, Baider C, Balslev H, Cárdenas S, Casas LF, Endara MJ, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Parada GA, Torres-Lezama A, Urrego Giraldo LE, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Garcia-Cabrera K, Hernandez L, Cuenca WP, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Valderrama Sandoval EH, Gamarra LV, Levesley A, Pickavance G, and Melgaço K

Subjects
Forests, Soil, Temperature, Trees, RNA, Long Noncoding
Abstract

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution., (© 2023. The Author(s).)

Published
2023
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12. More than 10,000 pre-Columbian earthworks are still hidden throughout Amazonia.

Author

Peripato V, Levis C, Moreira GA, Gamerman D, Ter Steege H, Pitman NCA, de Souza JG, Iriarte J, Robinson M, Junqueira AB, Trindade TB, de Almeida FO, Moraes CP, Lombardo U, Tamanaha EK, Maezumi SY, Ometto JPHB, Braga JRG, Campanharo WA, Cassol HLG, Leal PR, de Assis MLR, da Silva AM, Phillips OL, Costa FRC, Flores BM, Hoffman B, Henkel TW, Umaña MN, Magnusson WE, Valderrama Sandoval EH, Barlow J, Milliken W, Lopes MA, Simon MF, van Andel TR, Laurance SGW, Laurance WF, Torres-Lezama A, Assis RL, Molino JF, Mestre M, Hamblin M, Coelho LS, Lima Filho DA, Wittmann F, Salomão RP, Amaral IL, Guevara JE, de Almeida Matos FD, Castilho CV, Carim MJV, Cárdenas López D, Sabatier D, Irume MV, Martins MP, Guimarães JRDS, Bánki OS, Piedade MTF, Ramos JF, Luize BG, Novo EMML, Núñez Vargas P, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Demarchi LO, Honorio Coronado EN, Monteagudo Mendoza A, Montero JC, Schöngart J, Feldpausch TR, Quaresma AC, Aymard C GA, Baraloto C, Castaño Arboleda N, Engel J, Petronelli P, Zartman CE, Killeen TJ, Marimon BS, Marimon-Junior BH, Schietti J, Sousa TR, Vasquez R, Rincón LM, Berenguer E, Ferreira J, Mostacedo B, do Amaral DD, Castellanos H, de Medeiros MB, Andrade A, Camargo JL, Farias ES, Magalhães JLL, Mendonça Nascimento HE, de Queiroz HL, Brienen R, Cardenas Revilla JD, Stevenson PR, Araujo-Murakami A, Barçante Ladvocat Cintra B, Feitosa YO, Barbosa FR, Carpanedo RS, Duivenvoorden JF, de Noronha JDC, Rodrigues DJ, Mogollón HF, Ferreira LV, Householder JE, Lozada JR, Comiskey JA, Draper FC, de Toledo JJ, Damasco G, Dávila N, García-Villacorta R, Lopes A, Cornejo Valverde F, Alonso A, Dallmeier F, Gomes VHF, Jimenez EM, Neill D, Peñuela Mora MC, de Aguiar DPP, Arroyo L, Antunes Carvalho F, Coelho de Souza F, Feeley KJ, Gribel R, Pansonato MP, Ríos Paredes M, Brasil da Silva I, Ferreira MJ, Fine PVA, Fonty É, Guedes MC, Licona JC, Pennington T, Peres CA, Villa Zegarra BE, Parada GA, Pardo Molina G, Vos VA, Cerón C, Maas P, Silveira M, Stropp J, Thomas R, Baker TR, Daly D, Huamantupa-Chuquimaco I, Vieira ICG, Weiss Albuquerque B, Fuentes A, Klitgaard B, Marcelo-Peña JL, Silman MR, Tello JS, Vriesendorp C, Chave J, Di Fiore A, Hilário RR, Phillips JF, Rivas-Torres G, von Hildebrand P, Pereira LO, Barbosa EM, de Matos Bonates LC, Doza HPD, Zárate Gómez R, Gallardo Gonzales GP, Gonzales T, Malhi Y, de Andrade Miranda IP, Mozombite Pinto LF, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Zent EL, Zent S, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Galbraith D, Holmgren M, Kalamandeen M, Lobo G, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, van der Heijden G, Vilanova Torre E, Ahuite Reategui MA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Urrego Giraldo LE, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Garcia-Cabrera K, Hernandez L, Palacios Cuenca W, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Valenzuela Gamarra L, and Aragão LEOC

Subjects
Humans, Brazil, Forests, Archaeology
Abstract

Indigenous societies are known to have occupied the Amazon basin for more than 12,000 years, but the scale of their influence on Amazonian forests remains uncertain. We report the discovery, using LIDAR (light detection and ranging) information from across the basin, of 24 previously undetected pre-Columbian earthworks beneath the forest canopy. Modeled distribution and abundance of large-scale archaeological sites across Amazonia suggest that between 10,272 and 23,648 sites remain to be discovered and that most will be found in the southwest. We also identified 53 domesticated tree species significantly associated with earthwork occurrence probability, likely suggesting past management practices. Closed-canopy forests across Amazonia are likely to contain thousands of undiscovered archaeological sites around which pre-Columbian societies actively modified forests, a discovery that opens opportunities for better understanding the magnitude of ancient human influence on Amazonia and its current state.

Published
2023
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13. Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology.

Author

Pos E, de Souza Coelho L, de Andrade Lima Filho D, Salomão RP, Amaral IL, de Almeida Matos FD, Castilho CV, Phillips OL, Guevara JE, de Jesus Veiga Carim M, López DC, Magnusson WE, Wittmann F, Irume MV, Martins MP, Sabatier D, da Silva Guimarães JR, Molino JF, Bánki OS, Piedade MTF, Pitman NCA, Mendoza AM, Ramos JF, Hawes JE, Almeida EJ, Barbosa LF, Cavalheiro L, Dos Santos MCV, Luize BG, de Leão Novo EMM, Vargas PN, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Coronado ENH, Montero JC, Marimon BS, Marimon-Junior BH, Feldpausch TR, Duque A, Baraloto C, Arboleda NC, Engel J, Petronelli P, Zartman CE, Killeen TJ, Vasquez R, Mostacedo B, Assis RL, Schöngart J, Castellanos H, de Medeiros MB, Simon MF, Andrade A, Camargo JL, Demarchi LO, Laurance WF, Laurance SGW, de Sousa Farias E, Lopes MA, Magalhães JLL, Nascimento HEM, de Queiroz HL, Aymard GAC, Brienen R, Revilla JDC, Costa FRC, Quaresma A, Vieira ICG, Cintra BBL, Stevenson PR, Feitosa YO, Duivenvoorden JF, Mogollón HF, Ferreira LV, Comiskey JA, Draper F, de Toledo JJ, Damasco G, Dávila N, García-Villacorta R, Lopes A, Vicentini A, Noronha JC, Barbosa FR, de Sá Carpanedo R, Emilio T, Levis C, de Jesus Rodrigues D, Schietti J, Souza P, Alonso A, Dallmeier F, Gomes VHF, Lloyd J, Neill D, de Aguiar DPP, Araujo-Murakami A, Arroyo L, Carvalho FA, de Souza FC, do Amaral DD, Feeley KJ, Gribel R, Pansonato MP, Barlow J, Berenguer E, Ferreira J, Fine PVA, Guedes MC, Jimenez EM, Licona JC, Mora MCP, Peres CA, Zegarra BEV, Cerón C, Henkel TW, Maas P, Silveira M, Stropp J, Thomas-Caesar R, Baker TR, Daly D, Dexter KG, Householder JE, Huamantupa-Chuquimaco I, Pennington T, Paredes MR, Fuentes A, Pena JLM, Silman MR, Tello JS, Chave J, Valverde FC, Di Fiore A, Hilário RR, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Barbosa EM, de Matos Bonates LC, Doza HPD, Fonty É, Gómez RZ, Gonzales T, Gonzales GPG, Guillaumet JL, Hoffman B, Junqueira AB, Malhi Y, de Andrade Miranda IP, Pinto LFM, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Vos VA, Zent EL, Zent S, Albuquerque BW, Cano A, Correa DF, Costa JBP, Flores BM, Holmgren M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña MN, van der Heijden G, Torre EV, Vriesendorp C, Wang O, Young KR, Reategui MAA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Torres-Lezama A, Giraldo LEU, Villarroel D, Zagt R, Alexiades MN, Garcia-Cabrera K, Hernandez L, Milliken W, Cuenca WP, Pansini S, Pauletto D, Arevalo FR, Sampaio AF, Sandoval EHV, Gamarra LV, Boenisch G, Kattge J, Kraft N, Levesley A, Melgaço K, Pickavance G, Poorter L, and Ter Steege H

Subjects
Entropy, Forests, Plants, Ecology, Tropical Climate, Ecosystem, Biodiversity
Abstract

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics., (© 2023. The Author(s).)

Published
2023
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14. The novel mangrove environment and composition of the Amazon Delta.

Author

Bernardino AF, Mazzuco ACA, Souza FM, Santos TMT, Sanders CJ, Massone CG, Costa RF, Silva AEB, Ferreira TO, Nóbrega GN, Silva TSF, and Kauffman JB

Subjects
Animals, Soil chemistry, Water, Wetlands, Avicennia, Ecosystem
Abstract

Both freshwater floodplain (várzeas and igapós) forests and brackish-saline mangroves are abundant and well-described ecosystems in Brazil. 1 However, an interesting and unique wetland forest exists in the Amazon Delta where extensive mangroves occur in essentially freshwater tidal environments. Unlike the floodplain forests found upriver, the hydrology of these ecosystems is driven largely by large macro-tides of 4-8 m coupled with the significant freshwater discharge from the Amazon River. We explored these mangroves on the Amazon Delta (00°52' N to 01°41' N) and found surface water salinity to be consistently <5; soil pore water salinity in these mangrove forests ranged from 0 nearest the Amazon mouth to only 5-11 at the coastal margins to the north (01°41' N, 49°55' W). We also recorded a unique mix of mangrove-obligate (Avicennia sp., Rhizophora mangle) and facultative-wetland species (Mauritia flexuosa, Pterocarpus sp.) dominating these forests. This unique mix of plant species and soil porewater chemistry exists even along the coastal strands and active coastlines of the Atlantic Ocean. Part of these unique mangroves have escaped current global satellite mapping efforts, and we estimate that they may add over 180 km 2 (20% increase in mangrove area) within the Amazon Delta. Despite having a unique structure and function, these freshwater-brackish ecosystems likely provide similar ecosystem services to most mangroves worldwide, such as sequestering large quantities of organic carbon, protection of shoreline ecosystems from erosion, and habitats to many terrestrial and aquatic species (monkeys, birds, crabs, and fish)., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2022
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16. Revisiting the drivers of acoustic similarities in tropical anuran assemblages.

Author

Sugai LSM, Llusia D, Siqueira T, and Silva TSF

Subjects
Acoustics, Animals, Anura, Phylogeny, Ecosystem, Wetlands
Abstract

Acoustic signaling is key in mediating mate choice, which directly impacts individual fitness. Because background noise and habitat structure can impair signal transmission, the acoustic space of mixed-species assemblages has long been hypothesized to reflect selective pressures against signal interference and degradation. However, other potential drivers that received far less attention can drive similar outputs on the acoustic space. Phylogenetic niche conservatism and allometric constraints may also modulate species acoustic features, and the acoustic space of communities could be a side-effect of ecological assembly processes involving other traits (e.g., environmental filtering). Additionally, the acoustic space can also reflect the sorting of species relying on public information through extended communication networks. Using an integrative approach, we revisit the potential drivers of the acoustic space by addressing the distribution of acoustic traits, body size, and phylogenetic relatedness in tropical anuran assemblages across gradients of environmental heterogeneity in the Pantanal wetlands. We found the overall acoustic space to be aggregated compared with null expectations, even when accounting for confounding effects of body size. Across assemblages, acoustic and phylogenetic differences were positively related, while acoustic and body size similarities were negatively related, although to a minor extent. We suggest that acoustic partitioning, acoustic adaptation, and allometric constraints play a minor role in shaping the acoustic output of tropical anuran assemblages and that phylogenetic niche conservatism and public information use would influence between-assemblage variation. Our findings highlight an overlooked multivariate nature of the acoustic dimension and underscore the importance of including the ecological context of communities to understand drivers of the acoustic space., (© 2021 by the Ecological Society of America.)

Published
2021
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17. Drivers of assemblage-wide calling activity in tropical anurans and the role of temporal resolution.

Author

Sugai LSM, Silva TSF, Llusia D, and Siqueira T

Subjects
Animals, Biodiversity, Brazil, Phylogeny, Wetlands, Anura, Ecosystem
Abstract

Temporal scale in animal communities is often associated with seasonality, despite the large variation in species activity during a diel cycle. A gap thus remains in understanding the dynamics of short-term activity in animal communities. Here we assessed calling activity of tropical anurans and addressed how species composition varied during night activity in assemblages along gradients of local and landscape environmental heterogeneity. We investigated 39 anuran assemblages in the Pantanal wetlands (Brazil) with passive acoustic monitoring during the peak of one breeding season, and first determined changes in species composition between night periods (early, mid and late) using two temporal resolutions (1- and 3-hr intervals). Then, we addressed the role of habitat structure (local and landscape heterogeneity variables from field-based and remote sensing metrics) and ecological context (species richness and phylogenetic relatedness) in determining changes in species composition (a) between night periods and (b) across days. Nocturnal calling activity of anuran assemblages varied more within the 1-hr resolution than the 3-hr resolution. Differences in species composition between early- and late-night periods were related to local habitat structure and phylogenetic relatedness, while a low variation in compositional changes across days was associated with low-heterogeneous landscapes. None of these relationships were observed using the coarser temporal resolution (3 hr). Our findings on the variation of calling activity in tropical anuran assemblages suggest potential trade-offs mediated by fine-temporal partitioning. Local and landscape heterogeneity may provide conditions for spatial partitioning, while the relatedness among co-signalling species provides cues on the ecological overlap of species with similar requirements. These relationships suggest a role of niche dimensional complementarity on the structuring of these anuran assemblages over fine-temporal scales. We argue that fine-temporal differences between species in breeding activity can influence the outcome of species interaction and thus, addressing temporal scaling issues can improve our understanding of the dynamics of animal communities., (© 2020 British Ecological Society.)

Published
2021
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18. Biased-corrected richness estimates for the Amazonian tree flora.

Author

Ter Steege H, Prado PI, Lima RAF, Pos E, de Souza Coelho L, de Andrade Lima Filho D, Salomão RP, Amaral IL, de Almeida Matos FD, Castilho CV, Phillips OL, Guevara JE, de Jesus Veiga Carim M, Cárdenas López D, Magnusson WE, Wittmann F, Martins MP, Sabatier D, Irume MV, da Silva Guimarães JR, Molino JF, Bánki OS, Piedade MTF, Pitman NCA, Ramos JF, Monteagudo Mendoza A, Venticinque EM, Luize BG, Núñez Vargas P, Silva TSF, de Leão Novo EMM, Reis NFC, Terborgh J, Manzatto AG, Casula KR, Honorio Coronado EN, Montero JC, Duque A, Costa FRC, Castaño Arboleda N, Schöngart J, Zartman CE, Killeen TJ, Marimon BS, Marimon-Junior BH, Vasquez R, Mostacedo B, Demarchi LO, Feldpausch TR, Engel J, Petronelli P, Baraloto C, Assis RL, Castellanos H, Simon MF, de Medeiros MB, Quaresma A, Laurance SGW, Rincón LM, Andrade A, Sousa TR, Camargo JL, Schietti J, Laurance WF, de Queiroz HL, Nascimento HEM, Lopes MA, de Sousa Farias E, Magalhães JLL, Brienen R, Aymard C GA, Revilla JDC, Vieira ICG, Cintra BBL, Stevenson PR, Feitosa YO, Duivenvoorden JF, Mogollón HF, Araujo-Murakami A, Ferreira LV, Lozada JR, Comiskey JA, de Toledo JJ, Damasco G, Dávila N, Lopes A, García-Villacorta R, Draper F, Vicentini A, Cornejo Valverde F, Lloyd J, Gomes VHF, Neill D, Alonso A, Dallmeier F, de Souza FC, Gribel R, Arroyo L, Carvalho FA, de Aguiar DPP, do Amaral DD, Pansonato MP, Feeley KJ, Berenguer E, Fine PVA, Guedes MC, Barlow J, Ferreira J, Villa B, Peñuela Mora MC, Jimenez EM, Licona JC, Cerón C, Thomas R, Maas P, Silveira M, Henkel TW, Stropp J, Paredes MR, Dexter KG, Daly D, Baker TR, Huamantupa-Chuquimaco I, Milliken W, Pennington T, Tello JS, Pena JLM, Peres CA, Klitgaard B, Fuentes A, Silman MR, Di Fiore A, von Hildebrand P, Chave J, van Andel TR, Hilário RR, Phillips JF, Rivas-Torres G, Noronha JC, Prieto A, Gonzales T, de Sá Carpanedo R, Gonzales GPG, Gómez RZ, de Jesus Rodrigues D, Zent EL, Ruschel AR, Vos VA, Fonty É, Junqueira AB, Doza HPD, Hoffman B, Zent S, Barbosa EM, Malhi Y, de Matos Bonates LC, de Andrade Miranda IP, Silva N, Barbosa FR, Vela CIA, Pinto LFM, Rudas A, Albuquerque BW, Umaña MN, Carrero Márquez YA, van der Heijden G, Young KR, Tirado M, Correa DF, Sierra R, Costa JBP, Rocha M, Vilanova Torre E, Wang O, Oliveira AA, Kalamandeen M, Vriesendorp C, Ramirez-Angulo H, Holmgren M, Nascimento MT, Galbraith D, Flores BM, Scudeller VV, Cano A, Ahuite Reategui MA, Mesones I, Baider C, Mendoza C, Zagt R, Urrego Giraldo LE, Ferreira C, Villarroel D, Linares-Palomino R, Farfan-Rios W, Farfan-Rios W, Casas LF, Cárdenas S, Balslev H, Torres-Lezama A, Alexiades MN, Garcia-Cabrera K, Valenzuela Gamarra L, Valderrama Sandoval EH, Ramirez Arevalo F, Hernandez L, Sampaio AF, Pansini S, Palacios Cuenca W, de Oliveira EA, Pauletto D, Levesley A, Melgaço K, and Pickavance G

Subjects
Brazil, Biodiversity, Classification methods, Forests, Rivers, Trees classification
Abstract

Amazonian forests are extraordinarily diverse, but the estimated species richness is very much debated. Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecific spatial aggregation to an extended database of forest plots with up-to-date taxonomy. We show that the species abundance distribution of Amazonia is best approximated by a logseries with aggregated individuals, where aggregation increases with rarity. By averaging several methods to estimate total richness, we confirm that over 15,000 tree species are expected to occur in Amazonia. We also show that using ten times the number of plots would result in an increase to just ~50% of those 15,000 estimated species. To get a more complete sample of all tree species, rigorous field campaigns may be needed but the number of trees in Amazonia will remain an estimate for years to come.

Published
2020
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19. Rarity of monodominance in hyperdiverse Amazonian forests.

Author

Ter Steege H, Henkel TW, Helal N, Marimon BS, Marimon-Junior BH, Huth A, Groeneveld J, Sabatier D, Coelho LS, Filho DAL, Salomão RP, Amaral IL, Matos FDA, Castilho CV, Phillips OL, Guevara JE, Carim MJV, Cárdenas López D, Magnusson WE, Wittmann F, Irume MV, Martins MP, Guimarães JRDS, Molino JF, Bánki OS, Piedade MTF, Pitman NCA, Mendoza AM, Ramos JF, Luize BG, Moraes de Leão Novo EM, Núñez Vargas P, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Honorio Coronado EN, Montero JC, Feldpausch TR, Duque A, Costa FRC, Arboleda NC, Schöngart J, Killeen TJ, Vasquez R, Mostacedo B, Demarchi LO, Assis RL, Baraloto C, Engel J, Petronelli P, Castellanos H, de Medeiros MB, Quaresma A, Simon MF, Andrade A, Camargo JL, Laurance SGW, Laurance WF, Rincón LM, Schietti J, Sousa TR, de Sousa Farias E, Lopes MA, Magalhães JLL, Mendonça Nascimento HE, Lima de Queiroz H, Aymard C GA, Brienen R, Revilla JDC, Vieira ICG, Cintra BBL, Stevenson PR, Feitosa YO, Duivenvoorden JF, Mogollón HF, Araujo-Murakami A, Ferreira LV, Lozada JR, Comiskey JA, de Toledo JJ, Damasco G, Dávila N, Draper F, García-Villacorta R, Lopes A, Vicentini A, Alonso A, Dallmeier F, Gomes VHF, Lloyd J, Neill D, de Aguiar DPP, Arroyo L, Carvalho FA, de Souza FC, do Amaral DD, Feeley KJ, Gribel R, Pansonato MP, Barlow J, Berenguer E, Ferreira J, Fine PVA, Guedes MC, Jimenez EM, Licona JC, Peñuela Mora MC, Villa B, Cerón C, Maas P, Silveira M, Stropp J, Thomas R, Baker TR, Daly D, Dexter KG, Huamantupa-Chuquimaco I, Milliken W, Pennington T, Ríos Paredes M, Fuentes A, Klitgaard B, Pena JLM, Peres CA, Silman MR, Tello JS, Chave J, Cornejo Valverde F, Di Fiore A, Hilário RR, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Noronha JC, Barbosa EM, Barbosa FR, de Matos Bonates LC, Carpanedo RS, Dávila Doza HP, Fonty É, GómeZárate Z R, Gonzales T, Gallardo Gonzales GP, Hoffman B, Junqueira AB, Malhi Y, Miranda IPA, Pinto LFM, Prieto A, Rodrigues DJ, Rudas A, Ruschel AR, Silva N, Vela CIA, Vos VA, Zent EL, Zent S, Weiss Albuquerque B, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Flores BM, Galbraith D, Holmgren M, Kalamandeen M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña Medina MN, van der Heijden G, Vilanova Torre E, Vriesendorp C, Wang O, Young KR, Ahuite Reategui MA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Torres-Lezama A, Giraldo LEU, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Garcia-Cabrera K, Hernandez L, Palacios Cuenca W, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Sandoval EHV, Valenzuela Gamarra L, Levesley A, Pickavance G, and Melgaço K

Abstract

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors.

Published
2019
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20. Massive tree mortality from flood pulse disturbances in Amazonian floodplain forests: The collateral effects of hydropower production.

Author

Resende AF, Schöngart J, Streher AS, Ferreira-Ferreira J, Piedade MTF, and Silva TSF

Subjects
Brazil, Conservation of Natural Resources, Hydrology, Power Plants, Rivers, Trees
Abstract

Large dams built for hydroelectric power generation alter the hydrology of rivers, attenuating the flood pulse downstream of the dam and impacting riparian and floodplain ecosystems. The present work mapped black-water floodplain forests (igapó) downstream of the Balbina Reservoir, which was created between 1983 and 1987 by damming the Uatumã River in the Central Amazon basin. We apply remote sensing methods to detect tree mortality resulting from hydrological changes, based on analysis of 56 ALOS/PALSAR synthetic aperture radar images acquired at different flood levels between 2006 and 2011. Our application of object-based image analysis (OBIA) methods and the random forests supervised classification algorithm yielded an overall accuracy of 87.2%. A total of 9800 km 2 of igapó forests were mapped along the entire river downstream of the dam, but forest mortality was only observed below the first 49 km downstream, after the Morena rapids, along an 80-km river stretch. In total, 12% of the floodplain forest died within this stretch. We also detected that 29% of the remaining living igapó forest may be presently undergoing mortality. Furthermore, this large loss does not include the entirety of lost igapó forests downstream of the dam; areas which are now above current maximum flooding heights are no longer floodable and do not show on our mapping but will likely transition over time to upland forest species composition and dynamics, also characteristic of igapó loss. Our results show that floodplain forests are extremely sensitive to long-term downstream hydrological changes and disturbances resulting from the disruption of the natural flood pulse. Brazilian hydropower regulations should require that Amazon dam operations ensure the simulation of the natural flood-pulse, despite losses in energy production, to preserve the integrity of floodplain forest ecosystems and to mitigate impacts for the riverine populations., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Published
2019
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21. Management impacts on fire occurrence: A comparison of fire regimes of African and South American tropical savannas in different protected areas.

Author

Alvarado ST, Silva TSF, and Archibald S

Subjects
Brazil, Humans, Madagascar, Seasons, Ecosystem, Fires, Grassland
Abstract

Humans can alter fire dynamics in grassland systems by changing fire frequency, fire seasonality and fuel conditions. These changes have effects on vegetation structure and recovery, species composition, and ecosystem function. Understanding how human management can affect fire regimes is vital to detect potential changes in the resilience of plant communities, and to predict vegetation responses to human interventions. We evaluated the fire regimes of two recently protected areas in Madagascar (Ibity and Itremo NPA) and one in Brazil (Serra do Cipó NP) before and after livestock exclusion and fire suppression policies. We compare the pre- and post-management fire history in these areas and analyze differences in terms of total annual burned area, density of ignitions, burn scar size distribution, fire return period and seasonal fire distribution. More than 90% of total park areas were burned at least once during the studied period, for all parks. We observed a significant reduction in the number of ignitions for Ibity NPA and Serra do Cipó NP after livestock exclusion and active fire suppression, but no significant change in total burned area for each protected area. We also observed a seasonal shift in burning, with fires happening later in the fire season (October-November) after management intervention. However, the protected areas in Madagascar had shorter fire return intervals (3.23 and 1.82 years) than those in Brazil (7.91 years). Our results demonstrate that fire exclusion is unattainable, and probably unwarranted in tropical grassland conservation areas, but show how human intervention in fire and vegetation patterns can alter various aspects of the fire regimes. This information can help with formulating realistic and effective fire management policies in these valuable conservation areas., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Published
2018
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22. The tree species pool of Amazonian wetland forests: Which species can assemble in periodically waterlogged habitats?

Author

Luize BG, Magalhães JLL, Queiroz H, Lopes MA, Venticinque EM, Leão de Moraes Novo EM, and Silva TSF

Subjects
Biological Evolution, Survival Analysis, Forests, Trees classification, Wetlands
Abstract

We determined the filtered tree species pool of Amazonian wetland forests, based on confirmed occurrence records, to better understand how tree diversity in wetland environments compares to tree diversity in the entire Amazon region. The tree species pool was determined using data from two main sources: 1) a compilation of published tree species lists plus one unpublished list of our own, derived from tree plot inventories and floristic surveys; 2) queries on botanical collections that include Amazonian flora, curated by herbaria and available through the SpeciesLink digital biodiversity database. We applied taxonomic name resolution and determined sample-based species accumulation curves for both datasets, to estimate sampling effort and predict the expected species richness using Chao's analytical estimators. We report a total of 3 615 valid tree species occurring in Amazonian wetland forests. After surveying almost 70 years of research efforts to inventory the diversity of Amazonian wetland trees, we found that 74% these records were registered in published species lists (2 688 tree species). Tree species richness estimates predicted from either single dataset underestimated the total pooled species richness recorded as occurring in Amazonian wetlands, with only 41% of the species shared by both datasets. The filtered tree species pool of Amazonian wetland forests comprises 53% of the 6 727 tree species taxonomically confirmed for the Amazonian tree flora to date. This large proportion is likely to be the result of significant species interchange among forest habitats within the Amazon region, as well as in situ speciation processes due to strong ecological filtering. The provided tree species pool raises the number of tree species previously reported as occurring in Amazonian wetlands by a factor of 3.2., Competing Interests: The authors have declared that no competing interests exist.

Published
2018
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23. Species Distribution Modelling: Contrasting presence-only models with plot abundance data.

Author

Gomes VHF, IJff SD, Raes N, Amaral IL, Salomão RP, de Souza Coelho L, de Almeida Matos FD, Castilho CV, de Andrade Lima Filho D, López DC, Guevara JE, Magnusson WE, Phillips OL, Wittmann F, de Jesus Veiga Carim M, Martins MP, Irume MV, Sabatier D, Molino JF, Bánki OS, da Silva Guimarães JR, Pitman NCA, Piedade MTF, Mendoza AM, Luize BG, Venticinque EM, de Leão Novo EMM, Vargas PN, Silva TSF, Manzatto AG, Terborgh J, Reis NFC, Montero JC, Casula KR, Marimon BS, Marimon BH, Coronado ENH, Feldpausch TR, Duque A, Zartman CE, Arboleda NC, Killeen TJ, Mostacedo B, Vasquez R, Schöngart J, Assis RL, Medeiros MB, Simon MF, Andrade A, Laurance WF, Camargo JL, Demarchi LO, Laurance SGW, de Sousa Farias E, Nascimento HEM, Revilla JDC, Quaresma A, Costa FRC, Vieira ICG, Cintra BBL, Castellanos H, Brienen R, Stevenson PR, Feitosa Y, Duivenvoorden JF, Aymard C GA, Mogollón HF, Targhetta N, Comiskey JA, Vicentini A, Lopes A, Damasco G, Dávila N, García-Villacorta R, Levis C, Schietti J, Souza P, Emilio T, Alonso A, Neill D, Dallmeier F, Ferreira LV, Araujo-Murakami A, Praia D, do Amaral DD, Carvalho FA, de Souza FC, Feeley K, Arroyo L, Pansonato MP, Gribel R, Villa B, Licona JC, Fine PVA, Cerón C, Baraloto C, Jimenez EM, Stropp J, Engel J, Silveira M, Mora MCP, Petronelli P, Maas P, Thomas-Caesar R, Henkel TW, Daly D, Paredes MR, Baker TR, Fuentes A, Peres CA, Chave J, Pena JLM, Dexter KG, Silman MR, Jørgensen PM, Pennington T, Di Fiore A, Valverde FC, Phillips JF, Rivas-Torres G, von Hildebrand P, van Andel TR, Ruschel AR, Prieto A, Rudas A, Hoffman B, Vela CIA, Barbosa EM, Zent EL, Gonzales GPG, Doza HPD, de Andrade Miranda IP, Guillaumet JL, Pinto LFM, de Matos Bonates LC, Silva N, Gómez RZ, Zent S, Gonzales T, Vos VA, Malhi Y, Oliveira AA, Cano A, Albuquerque BW, Vriesendorp C, Correa DF, Torre EV, van der Heijden G, Ramirez-Angulo H, Ramos JF, Young KR, Rocha M, Nascimento MT, Medina MNU, Tirado M, Wang O, Sierra R, Torres-Lezama A, Mendoza C, Ferreira C, Baider C, Villarroel D, Balslev H, Mesones I, Giraldo LEU, Casas LF, Reategui MAA, Linares-Palomino R, Zagt R, Cárdenas S, Farfan-Rios W, Sampaio AF, Pauletto D, Sandoval EHV, Arevalo FR, Huamantupa-Chuquimaco I, Garcia-Cabrera K, Hernandez L, Gamarra LV, Alexiades MN, Pansini S, Cuenca WP, Milliken W, Ricardo J, Lopez-Gonzalez G, Pos E, and Ter Steege H

Subjects
Brazil, Chrysobalanaceae physiology, Fabaceae physiology, Humans, Polygonaceae physiology, Conservation of Natural Resources statistics & numerical data, Models, Statistical, Plant Dispersal physiology, Trees physiology
Abstract

Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species' area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.

Published
2018
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