Dataset Information

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

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.

SUBMITTER: Gomes VHF

PROVIDER: S-EPMC5772443 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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

Gomes Vitor H F VHF IJff Stéphanie D SD Raes Niels N Amaral Iêda Leão IL Salomão Rafael P RP de Souza Coelho Luiz L de Almeida Matos Francisca Dionízia FD Castilho Carolina V CV de Andrade Lima Filho Diogenes D López Dairon Cárdenas DC Guevara Juan Ernesto JE Magnusson William E WE Phillips Oliver L OL Wittmann Florian F de Jesus Veiga Carim Marcelo M Martins Maria Pires MP Irume Mariana Victória MV Sabatier Daniel D Molino Jean-François JF Bánki Olaf S OS da Silva Guimarães José Renan JR Pitman Nigel C A NCA Piedade Maria Teresa Fernandez MTF Mendoza Abel Monteagudo AM Luize Bruno Garcia BG Venticinque Eduardo Martins EM de Leão Novo Evlyn Márcia Moraes EMM Vargas Percy Núñez PN Silva Thiago Sanna Freire TSF Manzatto Angelo Gilberto AG Terborgh John J Reis Neidiane Farias Costa NFC Montero Juan Carlos JC Casula Katia Regina KR Marimon Beatriz S BS Marimon Ben-Hur BH Coronado Euridice N Honorio ENH Feldpausch Ted R TR Duque Alvaro A Zartman Charles Eugene CE Arboleda Nicolás Castaño NC Killeen Timothy J TJ Mostacedo Bonifacio B Vasquez Rodolfo R Schöngart Jochen J Assis Rafael L RL Medeiros Marcelo Brilhante MB Simon Marcelo Fragomeni MF Andrade Ana A Laurance William F WF Camargo José Luís JL Demarchi Layon O LO Laurance Susan G W SGW de Sousa Farias Emanuelle E Nascimento Henrique Eduardo Mendonça HEM Revilla Juan David Cardenas JDC Quaresma Adriano A Costa Flavia R C FRC Vieira Ima Célia Guimarães ICG Cintra Bruno Barçante Ladvocat BBL Castellanos Hernán H Brienen Roel R Stevenson Pablo R PR Feitosa Yuri Y Duivenvoorden Joost F JF Aymard C Gerardo A GA Mogollón Hugo F HF Targhetta Natalia N Comiskey James A JA Vicentini Alberto A Lopes Aline A Damasco Gabriel G Dávila Nállarett N García-Villacorta Roosevelt R Levis Carolina C Schietti Juliana J Souza Priscila P Emilio Thaise T Alonso Alfonso A Neill David D Dallmeier Francisco F Ferreira Leandro Valle LV Araujo-Murakami Alejandro A Praia Daniel D do Amaral Dário Dantas DD Carvalho Fernanda Antunes FA de Souza Fernanda Coelho FC Feeley Kenneth K Arroyo Luzmila L Pansonato Marcelo Petratti MP Gribel Rogerio R Villa Boris B Licona Juan Carlos JC Fine Paul V A PVA Cerón Carlos C Baraloto Chris C Jimenez Eliana M EM Stropp Juliana J Engel Julien J Silveira Marcos M Mora Maria Cristina Peñuela MCP Petronelli Pascal P Maas Paul P Thomas-Caesar Raquel R Henkel Terry W TW Daly Doug D Paredes Marcos Ríos MR Baker Tim R TR Fuentes Alfredo A Peres Carlos A CA Chave Jerome J Pena Jose Luis Marcelo JLM Dexter Kyle G KG Silman Miles R MR Jørgensen Peter Møller PM Pennington Toby T Di Fiore Anthony A Valverde Fernando Cornejo FC Phillips Juan Fernando JF Rivas-Torres Gonzalo G von Hildebrand Patricio P van Andel Tinde R TR Ruschel Ademir R AR Prieto Adriana A Rudas Agustín A Hoffman Bruce B Vela César I A CIA Barbosa Edelcilio Marques EM Zent Egleé L EL Gonzales George Pepe Gallardo GPG Doza Hilda Paulette Dávila HPD de Andrade Miranda Ires Paula IP Guillaumet Jean-Louis JL Pinto Linder Felipe Mozombite LFM de Matos Bonates Luiz Carlos LC Silva Natalino N Gómez Ricardo Zárate RZ Zent Stanford S Gonzales Therany T Vos Vincent A VA Malhi Yadvinder Y Oliveira Alexandre A AA Cano Angela A Albuquerque Bianca Weiss BW Vriesendorp Corine C Correa Diego Felipe DF Torre Emilio Vilanova EV van der Heijden Geertje G Ramirez-Angulo Hirma H Ramos José Ferreira JF Young Kenneth R KR Rocha Maira M Nascimento Marcelo Trindade MT Medina Maria Natalia Umaña MNU Tirado Milton M Wang Ophelia O Sierra Rodrigo R Torres-Lezama Armando A Mendoza Casimiro C Ferreira Cid C Baider Cláudia C Villarroel Daniel D Balslev Henrik H Mesones Italo I Giraldo Ligia Estela Urrego LEU Casas Luisa Fernanda LF Reategui Manuel Augusto Ahuite MAA Linares-Palomino Reynaldo R Zagt Roderick R Cárdenas Sasha S Farfan-Rios William W Sampaio Adeilza Felipe AF Pauletto Daniela D Sandoval Elvis H Valderrama EHV Arevalo Freddy Ramirez FR Huamantupa-Chuquimaco Isau I Garcia-Cabrera Karina K Hernandez Lionel L Gamarra Luis Valenzuela LV Alexiades Miguel N MN Pansini Susamar S Cuenca Walter Palacios WP Milliken William W Ricardo Joana J Lopez-Gonzalez Gabriela G Pos Edwin E Ter Steege Hans H

Scientific reports 20180117 1

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 ...[more]

PMID: 29343741

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Project description:BackgroundInterest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity.ResultsWe analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov's model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov's model also differentiates the animal species and is in good agreement with the first and second order Hill's numbers, that are common evenness indexes simply based on the fraction of individuals per species.ConclusionsWe conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov's model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.

Dataset Information

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

Publications

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

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