Ecological-Niche Factor Analysis: How to Compute Habitat-Suitability Maps without Absence Data?

Hirzel, Alexandre H.; Hausser, Jacques; Chessel, Daniel; Perrin, Nicolas

doi:10.2307/3071784

Cited by 514 publications

(1,044 citation statements)

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“…During the past decades many kinds of methods developed for SDMs, among which are the genetic algorithm for rule-set prediction (GARP) (Stockwell & Peters 1999)， ecological niche factor analysis (ENFA) (Hirzel et al 2002), and the maximum entropy model (Maxent) (Phillips et al 2006).The traditional analyses, such as GAM and GLM, require both presence and absence data (Gilberto et al 2008). However, absence data is usually unavailable and difficult to identify species existence in certain area if it was not observed (Baldwin 2009).…”

Section: Introductionmentioning

confidence: 99%

Predicting the potential distributions of Senecio vulgaris L. in China

Cheng

2015

Preprint

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Predicting potential distribution for alien plants by species distribution model (SDM, or Ecological Niche Model) using occurrence data and habitat environmental variables plays an important role in management of the invasive risk by an alien plant. Common groundsels (Senecio vulgaris, Asteracea), native in Eurasia and North Africa, has been a cosmopolitan weed in temperature and also listed as one of invasive plants in China. We predict the potential distribution of this species in the world and in China particularly in Maxent (maximum entropy) models by using global occurrence records of S. vulgaris and the associated climate variables. The occurrence data were collected from the online databases, Global Biodiversity Information Facility database (GBIF), Chinese Virtual Herbarium database (CVH), and also from field work in China. The climate variables were download from WorldClim (http://www.worldclim.org). The occurrence records showed that S. vulgaris is present in 16 provinces or regions in north -eastern, south -western, central and north China, and almost not present in southeastern, north -western China. The mapping of S. vulgaris potential distribution is diagonally across China, including the north -eastern, south -western China, and the cool area between the two regions. Analysis of the contribution and importance of climatic factors in the prediction model indicated that S. vulgaris adapts to the climate in humid and cool area in China (annual mean temperature ranges 2.4 -17.5 ℃, and annual precipitation ranges 550 -1500 mm). It is suggested that special attention should be paid to the plain in NE China and Shandong Peninsula, Yungui Plateau, the cool mountain area around Sichuan basin, in western Hubei, southern Shaanxi, Shanxi and around Beijing in order to manage the invasion risk by S. vulgaris. The better performance of the model built by using occurrence data in China than that by using the global data in relation the predict outcome in China imply that it is might be better to use regional data than the global data when predict potential distribution for an alien plant with long invasive history in study area.

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Section: Introductionmentioning

confidence: 99%

Predicting the potential distributions of Senecio vulgaris L. in China

Cheng

2015

Preprint

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“…The common methods of efficiency assessment include ratio analysis, regression analysis, and DEA [69]. One characteristic of DEA is that no prior knowledge of the production function between the input and output attribute data is required; similarly, no relative weight needs to be set for the attribute data [54,[60][61][62][63][64][65][66][67][68][69][70][71][72]. Therefore, DEA is useful for comprehensively assessing the indicators of different types and data patterns; it is widely used in economic science, agricultural economics, public economics, financial economics, and economic policy.…”

Section: Input and Output Indicatorsmentioning

confidence: 99%

“…The inputs were primarily oriented toward natural environmental resources and the ecological environment, including factors such as surface temperature [57][58][59], surface runoff [2,13,[43][44][45][46], habitat quality [13,[40][41][42]60], and water consumption [2,44,61].…”

Section: Input and Output Indicatorsmentioning

confidence: 99%

Application of Environmental Change Efficiency to the Sustainability of Urban Development at the Neighborhood Level

Kuo

Tsou

2015

Sustainability

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This study describes a new assessment model framework, termed the driving forces-pressure-state-impact-policy and pattern (DPSIP) model, for environmental change efficiency in urban land development, based on urban sustainable development and the theory of economic efficiency evaluation. A spatial and measurable efficiency value is defined for environmental changes in urban land development, which provides a comprehensive evaluation index for the efficiency of urban development and its environmental impact. This type of urban interior sustainability is considered new within the context of global environmental changes. We identify nine important indicators to evaluate the relative efficiency of 233 neighborhoods in Tainan, Taiwan. The results indicate that the average environmental change efficiency is 89.44%, which shows clear spatial differentiation. The key indicators affecting the efficiency score are area, population density, location, mixed land uses, the floor area ratio, and the impervious ratio. In the future, urban design can reduce environmental impacts and enhance efficiency values.

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“…Firstly, and because MDE procedures strongly depend on the number of selected predictors (Beaumont et al 2005), the climatic variables considered relevant for species distribution were estimated. The minimum set of climatic variables needed to explain the occurrence of E. iberica was calculated using an ecological-niche factor analysis (ENFA) in the Biomapper package (Hirzel et al 2002). This procedure computes uncorrelated factors that can explain both species marginality (the distance between the species optimum and the average climatic conditions in the study area) and specialisation (the ratio of the ecological variance in the climate of the study area to that associated with the focal species).…”

Section: Current Potential Distributionmentioning

confidence: 99%