Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis

2008 ◽  
Vol 211 (1-2) ◽  
pp. 233-240 ◽  
Author(s):  
Mathieu Basille ◽  
Clément Calenge ◽  
Éric Marboutin ◽  
Reidar Andersen ◽  
Jean-Michel Gaillard
Keyword(s):  
Factor Analysis ◽  
Habitat Selection ◽  
Multivariate Statistics ◽  
Ecological Niche ◽  
Ecological Niche Factor Analysis ◽  
Niche Factor

Application of ecological-niche factor analysis in habitat assessment of giant pandas

2008 ◽  
Vol 28 (2) ◽  
pp. 821-828 ◽  
Author(s):  
Wang Xuezhi ◽  
Xu Weihua ◽  
Ouyang Zhiyun ◽  
Liu Jianguo ◽  
Xiao Yi ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Ecological Niche ◽  
Habitat Assessment ◽  
Giant Pandas ◽  
Ecological Niche Factor Analysis ◽  
Niche Factor

scholarly journals Diurnal habitat suitability for a Mediterranean steppeland bird, identified by Ecological Niche Factor Analysis

2011 ◽  
Vol 38 (2) ◽  
pp. 152 ◽  
Author(s):  
Stefano Triolo ◽  
Daniela Campobello ◽  
Maurizio Sarà
Keyword(s):  
Factor Analysis ◽  
Habitat Suitability ◽  
Vegetation Cover ◽  
Ecological Niche ◽  
Large Scale ◽  
Ecological Trap ◽  
Habitat Suitability Models ◽  
Ecological Niche Factor Analysis ◽  
Rural Buildings ◽  
Niche Factor

Context The negative effects of agricultural intensification and policies, use of pesticides, fertilisers and mechanised harvesting on several populations of pseudo-steppe birds have increasingly required more detailed and effective habitat suitability models. Distribution models of farmland species are prone to incur recordings of false absence data. Ecological Niche Factor Analysis (ENFA) allows identification of environmental predictors of species distribution by using presence data only. Aims We quantified the diurnal habitat preferences and niche width of one steppe species, the stone curlew (Burhinus oedicnemus), with unfavourable conservations status in a Mediterranean area and reclassified a map with respect to habitat suitability classes according to the resulting distribution model. Methods Ecological Niche Factor Analysis was used with GIS cartography customised with habitat and anthropogenic variables recorded during field surveys carried out in four study plots (~500 ha) and at different spatial scales. Key results The stone curlew selected areas with low vegetation cover, such as fields following artichoke harvesting and tillage, close to rural buildings and unpaved roads. In contrast, the stone curlew avoided areas with high vegetation cover and areas highly disturbed by human-induced fires. The occurrence of natural vegetation was neither preferred nor avoided. The most robust model was based on a large-scale analysis (200 m from the bird location points), according to which the optimal area for stone curlew distribution during its breeding season was restricted to 1% of the entire study area. Conclusions Two uncorrelated factors, ‘marginality’ and ‘tolerance’, described the stone curlew’s niche in the area. The first index indicated selection for habitats that were marginal with respect to those available in the area, whereas the second indicated a species with a medium–wide environmental niche. In particular, the stone curlew occupied a much more restricted niche (low tolerance) in relation to individual variables. The use of customised databases at a large scale of analysis was found to more effectively reveal ecological requirements of this marginal and specialised species. Implications Our results allowed us to indicate practical land management actions for the stone curlew, such as prevention of human-induced fires and increase of pastoral activities. Our results indicated a potentially positive role of little-disturbed service roads along rural buildings in stone curlew distribution, which warrants further research. In addition, studies are needed to verify the presence of an ecological trap in artichoke fields, their preferred habitat. As we showed for the stone curlew, niche analyses conducted at a large scale using customised databases could greatly improve habitat suitability models of farmland species.

scholarly journals Potential distribution of Aquila chrysaetos in Mexico: Implications for conservation

2019 ◽  
Vol 12 (1) ◽  
pp. 33-41
Author(s):  
Maristella D’Addario ◽  
Octavio Monroy-Vilchis ◽  
Martha M Zarco-González ◽  
Dídac Santos-Fita
Keyword(s):  
Factor Analysis ◽  
Protected Areas ◽  
Ecological Niche ◽  
Potential Distribution ◽  
Area Under The Curve ◽  
Support Vector ◽  
Aquila Chrysaetos ◽  
Natural Protected Areas ◽  
Ecological Niche Factor Analysis ◽  
Niche Factor

The golden eagle ( Aquila chrysaetos) has been poorly studied in Mexico. Even though it is listed as threatened in this country, partly because of habitat fragmentation and direct persecution, little is known of its distribution. We assessed the potential distribution of this species in Mexico using ecological niche modelling (ecological niche factor analysis, artificial neural network, genetic algorithm for rule-set production, environmental distance, support vector machine, MaxEnt) and the weighted average ensemble method. The models were evaluated using the area under the curve of the receiver operating characteristics. We applied a threshold of 50% probability to obtain high-suitability areas and considered marginality and specialization calculated by ecological niche factor analysis and the most important variables to the model. We assessed and evaluated the percentage of high-suitability area occurring in all Mexican natural protected areas. The performance of the ensemble model was high (area under the curve = 0.93) and the most important variables contributing to the model were grasslands and tree cover percentage. The resulting high-suitability area is considerably fragmented, it comprises 16% of the country and just 8% of it is located in natural protected areas. We propose some urgent actions and conservation measures to face the main problems that are threatening the species in Mexico.

Exploring habitat use by cheetahs using ecological niche factor analysis

2009 ◽  
Vol 277 (2) ◽  
pp. 141-148 ◽  
Author(s):  
N. Pettorelli ◽  
A. Hilborn ◽  
F. Broekhuis ◽  
S. M. Durant
Keyword(s):  
Factor Analysis ◽  
Habitat Use ◽  
Ecological Niche ◽  
Ecological Niche Factor Analysis ◽  
Niche Factor

Modelling suitable estuarine habitats for Zostera noltii, using Ecological Niche Factor Analysis and Bathymetric LiDAR

2011 ◽  
Vol 94 (2) ◽  
pp. 144-154 ◽  
Author(s):  
Mireia Valle ◽  
Ángel Borja ◽  
Guillem Chust ◽  
Ibon Galparsoro ◽  
Joxe Mikel Garmendia
Keyword(s):  
Factor Analysis ◽  
Ecological Niche ◽  
Zostera Noltii ◽  
Ecological Niche Factor Analysis ◽  
Niche Factor
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