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Selection: Lobo:JM [3 articles] 

Publications by author Lobo:JM.

The uncertain nature of absences and their importance in species distribution modelling

Ecography, Vol. 33, No. 1. (1 February 2010), pp. 103-114,


Species distribution models (SDM) are commonly used to obtain hypotheses on either the realized or the potential distribution of species. The reliability and meaning of these hypotheses depends on the kind of absences included in the training data, the variables used as predictors and the methods employed to parameterize the models. Information about the absence of species from certain localities is usually lacking, so pseudo-absences are often incorporated to the training data. We explore the effect of using different kinds of ...


Seven shortfalls that beset large-scale knowledge of biodiversity

Annual Review of Ecology, Evolution, and Systematics, Vol. 46, No. 1. (2015), pp. 523-549,


Ecologists and evolutionary biologists are increasingly using big-data approaches to tackle questions at large spatial, taxonomic, and temporal scales. However, despite recent efforts to gather two centuries of biodiversity inventories into comprehensive databases, many crucial research questions remain unanswered. Here, we update the concept of knowledge shortfalls and review the tradeoffs between generality and uncertainty. We present seven key shortfalls of current biodiversity data. Four previously proposed shortfalls pinpoint knowledge gaps for species taxonomy (Linnean), distribution (Wallacean), abundance (Prestonian), and evolutionary ...


Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data

Journal for Nature Conservation, Vol. 19, No. 1. (12 January 2011), pp. 1-7,


In the last decade, the application of predictive models of species distribution in ecology, evolution, and conservation biology has increased dramatically. However, limited available data and the lack of reliable absence data have become a major challenge to overcome. At least two approaches have been proposed to generate pseudo-absences; however it is not clear how the number of pseudo-absences created affect model performance. Moreover, the spatial bias in the collecting localities of a species (presence data) may add extra noise to ...

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