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Selection: with tag presence-only [16 articles] 


Integrating biodiversity distribution knowledge: toward a global map of life

Trends in Ecology & Evolution, Vol. 27, No. 3. (March 2012), pp. 151-159,


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 ...


Using citizen science data to estimate climatic niches and species distributions

Basic and Applied Ecology, Vol. 20 (May 2017), pp. 75-85,


Opportunistic citizen data documenting species observations – i.e. observations collected by citizens in a non-standardized way – is becoming increasingly available. In the absence of scientific observations, this data may be a viable alternative for a number of research questions. Here we test the ability of opportunistic species records to provide predictions of the realized distribution of species and if species attributes can act as indicators of the reliability and completeness of these data. We use data for 39 reptile and ...


Spatial distribution of citizen science casuistic observations for different taxonomic groups

Scientific Reports, Vol. 7, No. 1. (16 October 2017),


Opportunistic citizen science databases are becoming an important way of gathering information on species distributions. These data are temporally and spatially dispersed and could have limitations regarding biases in the distribution of the observations in space and/or time. In this work, we test the influence of landscape variables in the distribution of citizen science observations for eight taxonomic groups. We use data collected through a Portuguese citizen science database ( We use a zero-inflated negative binomial regression to model the distribution ...


Point process models for presence-only analysis

Methods in Ecology and Evolution, Vol. 6, No. 4. (1 April 2015), pp. 366-379,


[::] 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,


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,


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 ...


EU-Forest, a high-resolution tree occurrence dataset for Europe

Scientific Data, Vol. 4 (05 January 2017), 160123,


We present EU-Forest, a dataset that integrates and extends by almost one order of magnitude the publicly available information on European tree species distribution. The core of our dataset (~96% of the occurrence records) came from an unpublished, large database harmonising forest plot surveys from National Forest Inventories on an INSPIRE-compliant 1 km×1 km grid. These new data can potentially benefit several disciplines, including forestry, biodiversity conservation, palaeoecology, plant ecology, the bioeconomy, and pest management. ...


  1. Ozanne, C. M. P., et al., 2003. Biodiversity meets the atmosphere: a global view of forest canopies. Science 301, 183-186.
  2. Secretariat of the Convention on Biological Diversity, 2008. The Convention on Biological Diversity
  3. Bengtsson, J., Nilsson, S. G., Franc, A., Menozzi, P., 2000. Biodiversity, disturbances, ecosystem function and management of European forests. Forest Ecology and Management 132 (1), 39-50. , INRMM-MiD:12124487 .
  4. Kerley, G. I. H., Kowalczyk,

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,


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,


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  


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 ( ). ...


Is my species distribution model fit for purpose? Matching data and models to applications

Global Ecology and Biogeography, Vol. 24, No. 3. (February 2015), pp. 276-292,


Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the ...

Visual summary


Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches?

Journal of Vegetation Science, Vol. 22, No. 4. (2011), pp. 647-659,


Questions: How can predictions of potential species distribution derived from presence-only data and different modelling algorithms be compared and evaluated? Where does suitable habitat for Juniperus oxycedrus exist within the study area and which bioclimatic variables prove to be most important in the prediction of J. oxycedrus potential distribution? Location: Central High Atlas, Morocco. Methods: Ecological niche factor analysis (ENFA), maximum entropy approach (MAXENT) and generalized linear models (GLM) were applied to either presence-only data of J. oxycedrus (ENFA and MAXENT) or presence–absence ...


Presence-absence versus presence-only modelling methods for predicting bird habitat suitability

Ecography, Vol. 27, No. 4. (August 2004), pp. 437-448,


Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear ...


A statistical explanation of MaxEnt for ecologists

Diversity and Distributions, Vol. 17, No. 1. (1 January 2011), pp. 43-57,


MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack ...


Classification of Natural and Semi-natural Vegetation

In Vegetation Ecology (07 January 2013), pp. 28-70,


This chapter covers classification of natural and semi-natural vegetation, including classification frameworks, components of classification, project planning and data acquisition, data preparation and integration, community entitation, cluster assessment, community characterization and determination, classification integration, documentation, and future directions and challenges. ...


Novel methods improve prediction of species' distributions from occurrence data

Ecography, Vol. 29, No. 2. (1 April 2006), pp. 129-151,


Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only ...

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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.
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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.