1. During the 2009-2010 overwintering season and following a 15-year downward trend, the total area in Mexico occupied by the eastern North American population of overwintering monarch butterflies reached an all-time low. Despite an increase, it remained low in 2010-2011.2. Although the data set is small, the decline in abundance is statistically significant using both linear and exponential regression models.3. Three factors appear to have contributed to reduce monarch abundance: degradation of the forest in the overwintering areas; the loss of breeding habitat in the United States due to the expansion of GM herbicide-resistant crops, with consequent loss of milkweed host plants, as well as continued land development; and severe weather.4. This decline calls into question the long-term survival of the monarchs' migratory phenomenon.Resú men. 1. Durante la temporada invernal 2009-2010, y siguiendo una tendencia a la baja de 15 an˜os, la superficie total ocupada por mariposas monarca en Me´xico, provenientes del este Ame´rica del Norte, llego´a su punto ma´s bajo. A pesar de su incremento, dicha superficie siguio´siendo baja en 2010-2011.2. Aunque que el conjunto de datos disponibles es au´n pequen˜o, esta disminucio´n de la abundancia de mariposas es estadı´sticamente significativa, tanto si se usan modelos de regresio´n lineales como exponenciales.3. Hay tres factores que parecen haber contribuido con esta tendencia de reduc-cio´n del nu´mero de mariposas: la degradacio´n de bosque en las a´reas de invernacio´n en Me´xico; la pe´rdida de ha´bitat de reproduccio´n en los Estados Unidos, debido a la expansio´n de cultivos gene´ticamente modificados resistentes a herbicidas, con la consiguiente pe´rdida de las plantas hospederas de algodoncillo, y por continuos cambios en el uso del suelo no favorables para ellas; y, las recientes condiciones cli-ma´ticas severas.4. Esta disminucio´n hace que nos cuestionemos sobre la posibilidad de supervivencia a largo plazo del feno´meno migratorio de las mariposas monarca.
About 90% of the wildland fires occurred in Southern Europe are caused by human activities. In spite of these figures, the human factor hardly ever appears in the definition of operational fire risk systems due to the difficulty of characterising it. This paper describes two spatially explicit models that predict the probability of fire occurrence due to human causes for their integration into a comprehensive fire risk-mapping methodology. A logistic regression technique at 1 9 1 km grid resolution has been used to obtain these models in the region of Madrid, a highly populated area in the centre of Spain. Socio-economic data were used as predictive variables to spatially represent anthropogenic factors related to fire risk. Historical fire occurrence from 2000 to 2005 was used as the response variable. In order to analyse the effects of the spatial accuracy of the response variable on the model performance (significant variables and classification accuracy), two different models were defined. In the first model, fire ignition points (x, y coordinates) were used as response variable. This model was compared with another one (Kernel model) where the response variable was the density of ignition points and was obtained through a kernel density interpolation technique from fire ignition points randomly located within a 10 9 10 km grid, which is the standard spatial reference unit established by the Spanish Ministry of Environment, Rural and Marine Affairs to report fire location in the national official statistics. Validation of both models was accomplished using an independent set of fire ignition points (years 2006-2007). For the validation, we used the area under the curve (AUC) obtained by a receiver-operating system. The first model performs slightly better with a value of AUC of 0.70 as opposed to 0.67 for the Kernel model. Wildland-urban interface was selected by both models with high relative importance.
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