نتایج جستجو برای: enfa

تعداد نتایج: 103  

2001
A. H. Hirzel V. Helfer F. Metral

This paper compares two habitat-suitability assessing methods, the Ecological Niche Factor Analysis (ENFA) and the Generalised Linear Model (GLM), to see how well they cope with three different scenarios. The main difference between these two analyses is that GLM is based on species presence/absence data while ENFA on presence data only. A virtual species was created and then dispatched in a ge...

2002
A. Elizabeth Zaniewski Anthony Lehmann

Identification of areas containing high biological diversity (‘hotspots’) from species presence-only data has become increasingly important in species and ecosystem management when presence/absence data is unavailable. However, as presence-only data sets lack any information on absences and as they suffer from many biases associated with the ad hoc or non-stratified sampling, they are often ass...

2006
LI LI LING BIAN GUIYUN YAN

Malaria is the leading cause of death in Kenya highlands. Malaria is a vector-borne disease and mosquito is the vector that transmits the parasites from infected people to others. Controlling mosquito larval habitats is important in eradicating malaria. Modeling the spatially distributed mosquito larval habitats is challenging. This study explores a mosquito habitat modeling approach, which int...

2003
Brigitte A. Reutter V. Helfer A. H. Hirzel P. Vogel

Aim, Location Although the alpine mouse Apodemus alpicola has been given species status since 1989, no distribution map has ever been constructed for this endemic alpine rodent in Switzerland. Based on redetermined museum material and using the Ecological-Niche Factor Analysis (ENFA), habitat-suitability maps were computed for A. alpicola, and also for the co-occurring A. flavicollis and A. syl...

2014
F. Canovas

1 ENFA analysis using the GUI 1.1 Preparation For the purposes of this example, honeybees data set will be used throughout Data management can be accessed through the menu Data (Figure 1).

2016
Jose Luis Passos Cordeiro José M.V. Fragoso Danielle Crawshaw Luiz Flamarion B. Oliveira

The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outsi...

2004
Alison K. Williams Paul Angermeier Carlyle Brewster Marcella Kelly Dean Stauffer Christopher Zobel Alison K Williams

Databases of species observations from surveys and ad hoc observations are frequently maintained by managers for a particular area. Obtaining information from this type of survey data about the habitat associations of species can be an efficient method of predicting habitat suitability across a landscape. Many multivariate statistical methods have been used to develop models of habitat associat...

2004
Laura Mandleberg

5 Introduction Background 6 Comparison of predictive ability between modelling techniques 9 ‘False’ Absences 11 Study area 11 The harbour porpoise (Phocoena phocoena) 12 Materials and Methods Data collection 13 Estimating position of sightings 14 Track and surveyed grid cells 14 Presence and Absence cells 15 Data partitioning 16 Ecogeographic data 16 1) PCA-based technique 17 PCA-intra-model ev...

Journal: :The Journal of animal ecology 2010
Sarah M Durant Meggan E Craft Charles Foley Katie Hampson Alex L Lobora Maurus Msuha Ernest Eblate John Bukombe John McHetto Nathalie Pettorelli

1. This study utilizes a unique data set covering over 19 000 georeferenced records of species presence collected between 1993 and 2008, to explore the distribution and habitat selectivity of an assemblage of 26 carnivore species in the Serengeti-Ngorongoro landscape in northern Tanzania. 2. Two species, the large-spotted genet and the bushy-tailed mongoose, were documented for the first time w...

Journal: :Ecological Informatics 2016
Juan Sebastian Ulloa Amandine Gasc Phillipe Gaucher Thierry Aubin Maxime Réjou-Méchain Jérôme Sueur

a Institut de Systématique, Évolution, Biodiversité, ISYEB UMR 7205 CNRS-MNHN-UPMC-EPHE, Muséum national d'Histoire naturelle, Sorbonne Universités, Paris, France b CNRS USR 3456 Guyane, Immeuble Le Relais, 2 Avenue Gustave Charlery, Cayenne, France c Equipe Communications Acoustiques, Neuro-PSI, UMR 9197 CNRS-Université Paris-Sud, 91405 Orsay, France d Laboratoire Évolution et Diversité Biolog...

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