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Selection: with tag pseudo-absences [8 articles] 

 

Integrating biodiversity distribution knowledge: toward a global map of life

  
Trends in Ecology & Evolution, Vol. 27, No. 3. (March 2012), pp. 151-159, https://doi.org/10.1016/j.tree.2011.09.007

Abstract

Global knowledge about the spatial distribution of species is orders of magnitude coarser in resolution than other geographically-structured environmental datasets such as topography or land cover. Yet such knowledge is crucial in deciphering ecological and evolutionary processes and in managing global change. In this review, we propose a conceptual and cyber-infrastructure framework for refining species distributional knowledge that is novel in its ability to mobilize and integrate diverse types of data such that their collective strengths overcome individual weaknesses. The ultimate ...

 

Point process models for presence-only analysis

  
Methods in Ecology and Evolution, Vol. 6, No. 4. (1 April 2015), pp. 366-379, https://doi.org/10.1111/2041-210x.12352

Abstract

[::] Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. [::] In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. [::] Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature ...

 

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

  
Ecography, Vol. 33, No. 1. (1 February 2010), pp. 103-114, https://doi.org/10.1111/j.1600-0587.2009.06039.x

Abstract

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 ...

 

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

  
Ecological Applications, Vol. 19, No. 1. (January 2009), pp. 181-197, https://doi.org/10.1890/07-2153.1

Abstract

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with ...

 

Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology

  
The Annals of Applied Statistics, Vol. 4, No. 3. (September 2010), pp. 1383-1402, https://doi.org/10.1214/10-aoas331

Abstract

Presence-only data, point locations where a species has been recorded as being present, are often used in modeling the distribution of a species as a function of a set of explanatory variables—whether to map species occurrence, to understand its association with the environment, or to predict its response to environmental change. Currently, ecologists most commonly analyze presence-only data by adding randomly chosen “pseudo-absences” to the data such that it can be analyzed using logistic regression, an approach which has weaknesses in ...

 

Novel three-step pseudo-absence selection technique for improved species distribution modelling

  
PLOS ONE, Vol. 8, No. 8. (13 August 2013), e71218, https://doi.org/10.1371/journal.pone.0071218

Abstract

Pseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM’s is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 28

  
(February 2014)
Keywords: inrmm-list-of-tags   power-law   ppm   practice   pre-alpine   pre-print   precaution   precaution-principle   precipitation   precisely-wrong   precursor-research   predation   predator-satiation   predatory-publishers   prediction   prediction-bias   predictive-modelling   predictors   predisposition   premature-optimization   preparedness   preprints   prescribed-burn   presence-absence   presence-only   pressure-volume-curves   pressures   prestoea-montana   pretreatment   prey-predator   pricing   primary-productivity   principal-components-regression   prisoners-dilemma   pristiphora-abietina   probability-vs-possibility   problem-driven   processes   processing   production-rules   productivity   programming   progressive-learning   prolog   proportion   prosopis-alba   prosopis-glandulosa   prosopis-pallida   protected-areas   protected-species   protection   protective-forest   protocol-uncertainty   provenance   provisioning-services   pruning   prunus-avium   prunus-cerasifera   prunus-domestica   prunus-dulcis   prunus-fruticosa   prunus-ilicifolia   prunus-laurocerasus   prunus-mahaleb   prunus-malaheb   prunus-padus   prunus-salicina   prunus-serotina   prunus-spinosa   prunus-spp   prunus-tenella   pseudo-absences   pseudo-random   pseudoaraucaria-spp   pseudolarix-spp   pseudomonas-avellanae   pseudomonas-spp   pseudomonas-syringae   pseudotsuga   pseudotsuga-macrocarpa   pseudotsuga-menziesii   pseudotsuga-spp   psychology   pterocarpus-indicus   pterocarpus-officinalis   pterocarya-pterocarpa   public-domain   publication-bias   publication-delay   publication-errors   publish-or-perish   puccinia-coronata   pull-push-pest-control   pulp   punica-granatum   purdiaea-nutans   pyrenees-region   pyrolysis   pyrus-amygdaliformis   pyrus-browiczii  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

 

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, https://doi.org/10.1016/j.jnc.2010.03.002

Abstract

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 ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/pseudo-absences

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Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
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Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.