TY - JOUR
T1 - Species Distribution Modelling: Contrasting presence-only models with plot abundance data
AU - Gomes, Vitor H.F.
AU - Ijff, Stéphanie D.
AU - Raes, Niels
AU - Amaral, Iêda Leão
AU - Salomão, Rafael P.
AU - Coelho, Luiz De Souza
AU - Matos, Francisca Dionízia De Almeida
AU - Castilho, Carolina V.
AU - Filho, Diogenes De Andrade Lima
AU - López, Dairon Cárdenas
AU - Guevara, Juan Ernesto
AU - Magnusson, William E.
AU - Phillips, Oliver L.
AU - Wittmann, Florian
AU - Carim, Marcelo De Jesus Veiga
AU - Martins, Maria Pires
AU - Irume, Mariana Victória
AU - Sabatier, Daniel
AU - Molino, Jean François
AU - Bánki, Olaf S.
AU - Guimarães, José Renan Da Silva
AU - Pitman, Nigel C.A.
AU - Piedade, Maria Teresa Fernandez
AU - Mendoza, Abel Monteagudo
AU - Luize, Bruno Garcia
AU - Venticinque, Eduardo Martins
AU - de Leão Novo, E.M.M.
AU - Vargas, Percy Núñez
AU - Silva, Thiago Sanna Freire
AU - Manzatto, Angelo Gilberto
AU - Terborgh, John
AU - Reis, Neidiane Farias Costa
AU - Montero, Juan Carlos
AU - Montero, Juan Carlos
AU - Casula, Katia Regina
AU - Marimon, Beatriz S.
AU - Marimon, Ben Hur
AU - Honorio Coronado, Euridice N.
AU - Feldpausch, Ted R.
AU - Duque, Alvaro
AU - Zartman, Charles Eugene
AU - Arboleda, Nicolás Castaño
AU - Killeen, Timothy J.
AU - Mostacedo, Bonifacio
AU - Vasquez, Rodolfo
AU - Schöngart, Jochen
AU - Assis, Rafael L.
AU - Medeiros, Marcelo Brilhante
AU - Simon, Marcelo Fragomeni
AU - Andrade, Ana
AU - Laurance, William F.
AU - Camargo, José Luís
AU - Demarchi, Layon O.
AU - Laurance, Susan G.W.
AU - Farias, Emanuelle De Sousa
AU - Nascimento, Henrique Eduardo Mendonça
AU - Revilla, Juan David Cardenas
AU - Quaresma, Adriano
AU - Costa, Flavia R.C.
AU - Vieira, Ima Célia Guimarães
AU - Cintra, Bruno Barçante Ladvocat
AU - Cintra, Bruno Barçante Ladvocat
AU - Castellanos, Hernán
AU - Brienen, Roel
AU - Stevenson, Pablo R.
AU - Feitosa, Yuri
AU - Duivenvoorden, Joost F.
AU - Aymard, Gerardo A.C.
AU - Mogollón, Hugo F.
AU - Targhetta, Natalia
AU - Comiskey, James A.
AU - Vicentini, Alberto
AU - Lopes, Aline
AU - Damasco, Gabriel
AU - Dávila, Nállarett
AU - García-Villacorta, Roosevelt
AU - Levis, Carolina
AU - Schietti, Juliana
AU - Souza, Priscila
AU - Emilio, Thaise
AU - Alonso, Alfonso
AU - Neill, David
AU - Dallmeier, Francisco
AU - Ferreira, Leandro Valle
AU - Araujo-Murakami, Alejandro
AU - Praia, Daniel
AU - Do Amaral, Dário Dantas
AU - Carvalho, Fernanda Antunes
AU - De Souza, Fernanda Coelho
PY - 2018/1/17
Y1 - 2018/1/17
N2 - 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.
AB - 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.
U2 - 10.1038/s41598-017-18927-1
DO - 10.1038/s41598-017-18927-1
M3 - Article
AN - SCOPUS:85041379841
VL - 8
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
ER -