Mapping the ecological structure of a territory: a case study in madrid (central spain)

Agar, Pilar Martı́n de; Pablo, Carlos Tomás López de; Pineda, Francisco Díaz

doi:10.1007/bf02471977

Cited by 17 publications

(5 citation statements)

References 20 publications

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“…Similarly to other European Mediterranean areas, Madrid region ( Fig 1 ) experienced an important socio-economic transformation in the last decades. Its ~8028 km 2 occupies ~1.6% of the Spanish territory and contains a northeast-southwest mountain range [ 31 ]. This orographic barrier, together with its further distance to the sea, causes colder winters and warmer summers, and hence higher thermal amplitude (20°C) than coastal Mediterranean regions (10–15°C) [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

Vilar

Gómez²,

Martínez‐Vega

et al. 2016

PLoS ONE

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The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.

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Section: Methodsmentioning

confidence: 99%

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

Vilar

Gómez²,

Martínez‐Vega

et al. 2016

PLoS ONE

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“…But it was treated separately as it has a significant number of patches with nodata (rocky habitats) and crops. TCL 4,8,10,11,12,13,16, and 19 represent a heterogeneous group in which sclerophyllous and shrubs predominate. Other categories, such as crops or conifers, occupy significant percentages of these TCL.…”

Section: Displaying Categories and Uncertaintymentioning

confidence: 99%

“…Two general approaches to landscape classification are identified [13]: human landscape-based classification [14] and biophysical approaches [15,16]. In the present study, a biophysical approach was chosen to identify areas with similar topographic and climatic behavior.…”

Section: Introductionmentioning

confidence: 99%

A Classification Procedure for Mapping Topo-climatic Conditions for Strategic Vegetation Planning

Serra

Cristóbal

Ninyerola

2010

Environ Model Assess

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Environmental classification addresses issues involving the representation and analysis of continuous and variable ecological data. This study creates a methodology to define topo-climatic landscapes (TCL) in the north-west of Catalonia, which is situated in the north-east of the Iberian Peninsula. TCL provide data regarding the ecological behavior of a landscape in terms of its topography, physiognomy, and climate, which are the main drivers of an ecosystem. The variables selected are derived from a variety of different sources, such as remote sensing and climatic atlases. The methodology employed combines unsupervised iterative cluster classification with supervised fuzzy classification. Twenty eight TCL, which can be differentiated in terms of their vegetation physiognomy and vegetation altitudinal range type, were selected for the study area. Furthermore, a hierarchy among the TCL is established which permits the merging of clusters and allows for changes in thematic resolution. By using the topo-climatic landscape map, managers can identify patches with similar environmental conditions and at the same time assess the uncertainty involved in classification.

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“…There are various examples of ecological, bioclimatic or vegetation classifications and maps at various geographical scales for different countries Martin de Agar et al 1995;Ozenda and Borel 2000;Hossell et al 2003), although most of them were not developed with the aim of identifying plant adaptation patterns. One of the scarce studies that considered adaptation was carried out by Vogel et al (2005), who produced a plant adaptation map for the United States merging previous ecoregion and plant hardiness zone maps.…”

Section: Introductionmentioning

confidence: 99%

Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies

Parra‐Quijano

Iriondo

Torres

2011

Genet Resour Crop Evol

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Information on plant adaptation can be very useful in agrobiodiversity studies. Ecogeographical land characterization (ELC) maps constitute a new tool in this direction with great potential. To assess the usefulness of this approach, an ELC map of Spain was created through multivariate methods. Its performance to characterize plant habitat preferences was compared with existing ecological regions and land cover maps. Collecting sites and seed weight from eight plant species were used to test the ELC map. Categories from each map were assigned to accessions using collecting sites. Chi-square tests were applied to test if category frequency distributions for each species followed a distribution proportional to the relative frequency of categories in each map. The tests found significant differences in the eight species studied. Thus, Bonferroni confidence intervals (BCI) classified categories from maps in preferred, neutral or avoided habitats. Seed weight was used as a proxy for plant adaptation. Comparison between observed and expected ranking of BCI and quartile classes in terms of seed weight means, and GLM and post-hoc tests carried out to test the effect of these classes upon seed weight showed consistently better results for the ELC map. Species results and applications of ecogeographic maps in plant genetic resources conservation are discussed.

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Mapping the ecological structure of a territory: a case study in madrid (central spain)

Cited by 17 publications

References 20 publications

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

A Classification Procedure for Mapping Topo-climatic Conditions for Strategic Vegetation Planning

Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies

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