Suitable climatic habitat changes for Mexican conifers along altitudinal gradients under climatic change scenarios.

The high biodiversity of the Mexican montane forests is concentrated on the Trans-Mexican Volcanic Belt, where several Protected Natural Areas exist. Our study examines the projected changes in suitable climatic habitat for five conifer species that dominate these forests. The species are distributed sequentially in overlapping altitudinal bands: Pinus hartwegii at the upper timberline, followed by Abies religiosa, the overwintering host of the Monarch butterfly at the Monarch Butterfly Biosphere Reserve, P. pseudostrobus, the most important in economic terms, and P. devoniana and P. oocarpa, which are important for resin production and occupy low altitudes where montane conifers merge with tropical dry forests. We fit a bioclimatic model to presence-absence observations for each species using the Random Forests classification tree with ground plot data. The models are driven by normal climatic variables from 1961 to 1990, which represents the reference period for climate-induced vegetation changes. Climate data from an ensemble of 17 general circulation models were run through the classification tree to project current distributions under climates described by the RCP 6.0 watts/m2 scenario for the decades centered on years 2030, 2060 and 2090. The results suggest that, by 2060, the climate niche of each species will occur at elevations that are between 300 to 500 m higher than at present. By 2060, habitat loss could amount to 46-77%, mostly affecting the lower limits of distribution. The two species at the highest elevation, P. hartwegii and A. religiosa, would suffer the greatest losses while, at the lower elevations, P. oocarpa would gain the most niche space. Our results suggest that conifers will require human assistance to migrate altitudinally upward in order to recouple populations with the climates to which they are adapted. Traditional in situ conservation measures are likely to be equivalent to inaction and will therefore be incapable of maintaining current forest compositions.

Use of genetic algorithms to improve the solid waste collection service in an urban area.

Increasing generation of Urban Solid Waste (USW) has become a significant issue in developing countries due to unprecedented population growth and high rates of urbanisation. This issue has exceeded current plans and programs of local governments to manage and dispose of USW. In this study, a Genetic Algorithm for Rule-set Production (GARP) integrated into a Geographic Information System (GIS) was used to find areas with socio-economic conditions that are representative of the generation of USW constituents in such areas. Socio-economic data of selected variables categorised by Basic Geostatistical Areas (BGAs) were taken from the 2000 National Population Census (NPC). USW and additional socio-economic data were collected during two survey campaigns in 1998 and 2004. Areas for sampling of USW were stratified into lower, middle and upper economic strata according to income. Data on USW constituents were analysed using descriptive statistics and Multivariate Analysis. ARC View 3.2 was used to convert the USW data and socio-economic variables to spatial data. Desk-top GARP software was run to generate a spatial model to identify areas with similar socio-economic conditions to those sampled. Results showed that socio-economic variables such as monthly income and education are positively correlated with waste constituents generated. The GARP used in this study revealed BGAs with similar socio-economic conditions to those sampled, where a similar composition of waste constituents generated is expected. Our results may be useful to decrease USW management costs by improving the collection services.