Biodiversity Informatics最新文献_第2页

Presence-only and Presence-absence Data for Comparing Species Distribution Modeling Methods 比较物种分布建模方法的只存在和不存在数据

Biodiversity Informatics Pub Date : 2020-07-22 DOI: 10.17161/bi.v15i2.13384

J. Elith, C. Graham, Roozbeh Valavi, M. Abegg, C. Bruce, Simon Ferrier, A. Ford, A. Guisan, R. Hijmans, F. Huettmann, L. Lohmann, Bette A. Loiselle, C. Moritz, J. Overton, A. Peterson, Steven J. Phillips, K. Richardson, S. Williams, S. Wiser, T. Wohlgemuth, N. Zimmermann

{"title":"Presence-only and Presence-absence Data for Comparing Species Distribution Modeling Methods","authors":"J. Elith, C. Graham, Roozbeh Valavi, M. Abegg, C. Bruce, Simon Ferrier, A. Ford, A. Guisan, R. Hijmans, F. Huettmann, L. Lohmann, Bette A. Loiselle, C. Moritz, J. Overton, A. Peterson, Steven J. Phillips, K. Richardson, S. Williams, S. Wiser, T. Wohlgemuth, N. Zimmermann","doi":"10.17161/bi.v15i2.13384","DOIUrl":"https://doi.org/10.17161/bi.v15i2.13384","url":null,"abstract":"Species distribution models (SDMs) are widely used to predict and study distributions of species. Many different modeling methods and associated algorithms are used and continue to emerge. It is important to understand how different approaches perform, particularly when applied to species occurrence records that were not gathered in structured surveys (e.g. opportunistic records). This need motivated a large-scale, collaborative effort, published in 2006, that aimed to create objective comparisons of algorithm performance. As a benchmark, and to facilitate future comparisons of approaches, here we publish that dataset: point location records for 226 anonymized species from six regions of the world, with accompanying predictor variables in raster (grid) and point formats. A particularly interesting characteristic of this dataset is that independent presence-absence survey data are available for evaluation alongside the presence-only species occurrence data intended for modeling. The dataset is available on Open Science Framework and as an R package and can be used as a benchmark for modeling approaches and for testing new ways to evaluate the accuracy of SDMs.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

General Theory and Good Practices in Ecological Niche Modeling: A Basic Guide 生态位建模的一般理论和良好实践:基本指南

Biodiversity Informatics Pub Date : 2020-04-20 DOI: 10.17161/bi.v15i2.13376

M. Simões, D. Romero-Alvarez, C. Nuñez-Penichet, L. Jiménez, Marlon E. Cobos

引用次数: 30

Can We Infer Species Interactions from Co-occurrence Patterns? A Reply to Peterson et al. (2020) 我们能从共现模式推断物种间的相互作用吗?对Peterson等人(2020)的答复

Biodiversity Informatics Pub Date : 2020-01-31 DOI: 10.17161/bi.v15i1.13402

C. Stephens

引用次数: 2

Some thoughts about the challenge of inferring ecological interactions from spatial data. 关于从空间数据推断生态相互作用的挑战的一些思考。

Biodiversity Informatics Pub Date : 2020-01-31 DOI: 10.17161/bi.v15i1.13302

R. Holt

{"title":"Some thoughts about the challenge of inferring ecological interactions from spatial data.","authors":"R. Holt","doi":"10.17161/bi.v15i1.13302","DOIUrl":"https://doi.org/10.17161/bi.v15i1.13302","url":null,"abstract":"Dr. Luis Escobar asked me to provide a joint review of the submissions by Stephens et al. (2019, this issue) and Peterson et al. (2019, this issue). I pulled thoughts together, but by the time I sent them along, he had received other reviews and made an editorial decision. He felt my perspective might nevertheless warrant publishing as a commentary alongside these two pieces. My review was of the original submissions, which are now appearing with minor, mainly cosmetic changes. I have only lightly edited the text of my review, and added a few additional thoughts and pertinent references. Neither group of authors has seen my commentary, and so I am responsible for any omissions or lapses in interpretation. The protocol developed by Stephens seems to me a potentially valuable exploratory tool in describing patterns of co-occurrence, but I note several potential problems in identifying interactions usingsolely this protocol. I also gently disagree with Peterson et al., who state flatly that co-occurrence data can shed no light at all on interspecific interactions. I suggest there are a number of counter-examples to this claim in the literature. I argue that spatiotemporal data, when available, iprovide a much more powerful tool for discerning interactions, than do staticspatial data. Finally, I use a simple thought experiment to point out that biotic drivers could be playing a key causal role in limitnig distributions, even in equisitlvely accurate SDMs that use only abiotic (scenopoetic) data as input data.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128856779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Can Ecological Interactions be Inferred from Spatial Data? 从空间数据可以推断出生态的相互作用吗?

Biodiversity Informatics Pub Date : 2020-01-31 DOI: 10.17161/bi.v15i1.9815

C. Stephens, C. González-Salazar, M. Villalobos, P. Marquet

{"title":"Can Ecological Interactions be Inferred from Spatial Data?","authors":"C. Stephens, C. González-Salazar, M. Villalobos, P. Marquet","doi":"10.17161/bi.v15i1.9815","DOIUrl":"https://doi.org/10.17161/bi.v15i1.9815","url":null,"abstract":"The characterisation and quantication of ecological interactions, and the construction \u0000of species distributions and their associated ecological niches, is of fundamental \u0000theoretical and practical importance. In this paper we give an overview of a Bayesian \u0000inference framework, developed over the last 10 years, which, using spatial data, offers \u0000a general formalism within which ecological interactions may be characterised and \u0000quantied. Interactions are identied through deviations of the spatial distribution \u0000of co-occurrences of spatial variables relative to a benchmark for the non-interacting \u0000system, and based on a statistical ensemble of spatial cells. The formalism allows for \u0000the integration of both biotic and abiotic factors of arbitrary resolution. We concentrate \u0000on the conceptual and mathematical underpinnings of the formalism, showing \u0000how, using the Naive Bayes approximation, it can be used to not only compare and \u0000contrast the relative contribution from each variable, but also to construct species \u0000distributions and niches based on arbitrary variable type. We show how the formalism \u0000can be used to quantify confounding and therefore help disentangle the complex \u0000causal chains that are present in ecosystems. We also show species distributions and \u0000their associated niches can be used to infer standard \"micro\" ecological interactions, \u0000such as predation and parasitism. We present several representative use cases that \u0000validate our framework, both in terms of being consistent with present knowledge of \u0000a set of known interactions, as well as making and validating predictions about new, \u0000previously unknown interactions in the case of zoonoses.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Co-occurrence Networks do not Support Identification of Biotic Interactions 共生网络不支持生物相互作用的识别

Biodiversity Informatics Pub Date : 2020-01-31 DOI: 10.17161/bi.v15i1.9798

A. Peterson, Jorge Soberón, J. Ramsey, L. Osorio-Olvera

引用次数: 17

Response to Stephens et al. (2019) 对Stephens等人(2019)的回应

Biodiversity Informatics Pub Date : 2020-01-31 DOI: 10.17161/bi.v15i1.12060

A. Peterson, Jorge Soberón, J. Ramsey, L. Osorio-Olvera

引用次数: 0

climateStability: An R package to estimate climate stability from time-slice climatologies 气候稳定性:一个从时间片气候学估计气候稳定性的R包

Biodiversity Informatics Pub Date : 2019-06-03 DOI: 10.17161/BI.V14I0.9786

H. Owens, R. Guralnick

引用次数: 16

Curso modelado de nicho ecológico, version 1.0 生态位建模课程，1.0版

Biodiversity Informatics Pub Date : 2019-05-02 DOI: 10.17161/BI.V14I0.8189

A. Peterson, Robert P. Anderson, Marlon E. Cobos, M. Cuahutle, Angela P. Cuervo-Robayo, L. Escobar, M. Fernández, D. Jiménez-García, A. Lira-Noriega, J. Lobo, Fernando Machado-Stredel, E. Martínez-Meyer, Claudia Nuñez-Penichet, Javier Nori, Luis Osorio-Olvera, M. Rodríguez, Octavio R. Rojas-Soto, Daniel Romero-Álvarez, Jorge Soberón, Sara Varela, Carlos A. Yáñez-Arenas

{"title":"Curso modelado de nicho ecológico, version 1.0","authors":"A. Peterson, Robert P. Anderson, Marlon E. Cobos, M. Cuahutle, Angela P. Cuervo-Robayo, L. Escobar, M. Fernández, D. Jiménez-García, A. Lira-Noriega, J. Lobo, Fernando Machado-Stredel, E. Martínez-Meyer, Claudia Nuñez-Penichet, Javier Nori, Luis Osorio-Olvera, M. Rodríguez, Octavio R. Rojas-Soto, Daniel Romero-Álvarez, Jorge Soberón, Sara Varela, Carlos A. Yáñez-Arenas","doi":"10.17161/BI.V14I0.8189","DOIUrl":"https://doi.org/10.17161/BI.V14I0.8189","url":null,"abstract":"El conjunto de ideas, métodos y programas informáticos que se conoce como “Modelado de Nicho Ecológico” (MNE)—y el relacionado “Modelado de Distribución de Especies” (MDS)—han sido objeto de intensa exploración e investigación en las últimas décadas. A pesar de existir al menos cuatro síntesis publicadas, este campo ha crecido tanto en complejidad, que la formación de nuevos investigadores es difícil. Hasta ahora, dicha formación se ha hecho de manera presencial en cursos organizados por universidades o centros de investigación, de los que hemos formado parte como instructores. Sin embargo, el acceso a este tipo de cursos especializados es restringido, por un lado, porque los cursos no se ofrecen en todas las universidades, y por otro, porque normalmente se imparten en inglés. Para facilitar el acceso a una mayor comunidad de científicos de habla hispana, presentamos un curso en español, completamente digital y de acceso gratuito, que se realizó vía Internet durante 23 semanas consecutivas en 2018. Aunque las barreras intrínsecas al uso de Internet pueden dificultar la accesibilidad a los materiales del curso, hemos usado diversos formatos para la divulgación de los contenidos académicos (video, audio, pdf) con el objetivo de eliminar la mayor parte de estos problemas.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121330501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sample data and training modules for cleaning biodiversity information 清洁生物多样性信息的样本数据和培训模块

Biodiversity Informatics Pub Date : 2018-10-24 DOI: 10.17161/BI.V13I0.7600

M. Cobos, L. Jiménez, C. Nuñez-Penichet, D. Romero-Alvarez, M. Simões

引用次数: 34