Alien invasive species represent a severe risk to biodiversity. Such is the case of buffel grass (Cenchrus ciliaris L.), a native species of Southern Asia and East Africa, which was introduced to the United States and Mexico for use in improved pasture. Here we present a coarse-grain approach to determine areas where buffel grass can potentially invade in Mexico. Potential species distributions, suitable for an invasion by buffel grass, were obtained through genetic algorithms. We generated the algorithms with databases of herbaria specimens; environmental digital covers of climate, soil texture, and vegetation; and the program called Genetic Algorithm for Rule-Set Prediction. This spatial modeling approach was validated with a case study for the state of Sonora, Mexico, where the occurrence of buffel grass has been proven. The most threatened vegetation types for the specific case of Sonora were desert scrub, mesquite woodlands, and tropical deciduous forest. The model prediction agreed with the field observations recorded in Sonora and allowed us to apply the same procedure to produce a map of the potential sites of buffel grass invasion for Mexico. The areas at risk of invasion mostly occurred in desert scrub, located in the arid and semiarid regions of northern Mexico. This methodology provides an initial baseline for assessment, prevention, and management of alien species that may become invasive under certain environmental conditions. Additionally this modeling approach provides a tool for policy makers to use in making decisions on land-use management practices when alien species are involved. Resumen: Las especies exóticas invasoras representan un riesgo para la biodiversidad. Tal es el caso deCenchrus ciliaris, una especie nativa del sureste de Asia y este deÁfrica, que fue introducida a los Estados Unidos y México como un pasto mejora de pasturas. Presentamos un método de grano grueso para determinaráreas que potencialmente pueden ser invadidas por C. ciliaris en México. Las distribuciones potenciales deáreas susceptibles de invasión por C. ciliaris, se obtuvieron por medio de algoritmos genéticos. Generamos los algoritmos con bases de datos de especímenes de herbario, coberturas ambientales digitales (i. e. clima, textura del suelo y vegetación) y el programa Genetic Algorithm for Rule-Set Prediction. Este método de modelado espacial fue validado con un estudio de caso para el estado de Sonora, México, donde se había probado la ocurrencia de C. ciliaris. Para el caso específico de Sonora, los tipos de vegetación más amenazados
Climate change, habitat loss and fragmentation, invasive species, and resource over-exploitation are among the major factors driving biodiversity loss and the current global change crisis. Maintaining and restoring connectivity throughout fragmented landscapes is key to reduce habitat isolation and mitigate anthropogenic impacts. To date, few connectivity approaches seek to identify corridors along climate gradients and least transformed natural habitats despite its importance to facilitate dispersal of organisms, as species' ranges shift over time to track suitable climates. In this study, we identified least-cost climatic corridors in Mexico between 2027 old-growth vegetation patches incorporating evapotranspiration as climatic variable, Euclidean distances, and human impact. We identified old-growth vegetation patches using the land use and vegetation map of 2011 (scale 1:250 000) by the National Institute of Statistics and Geography (INEGI). Moreover, we calculated a human impact index based on the theoretical framework of the Global Biodiversity Model (Alkemade et al. 2009) but adapted for Mexico (Mexbio, Kolb 2016), and includes the impact of land use, road infrastructure and fragmentation based on the land use and vegetation map of 2011 and a road map by the Mexican Institute of Transportation. We modeled corridors for a baseline period (1980-2009) and under three future time periods (2015-2039, 2045-2069 and 2075-2099), corresponding to four Global Circulation Models (MPI-ESM-LR, GFDL-CM3, HADGEM2-ES and CNRMCM5) each under two emission scenarios (RCP 4.5 and 8.5) The historical and future evapotranspiration values were calculated using the climate surfaces from Cuervo-Robayo et al. 2019 and from the Center of Atmospheric Sciences of the National Autonomous University of Mexico*1, respectively. The historical and future evapotranspiration values were calculated using the climate surfaces from Cuervo-Robayo et al. 2019 and from the Center of Atmospheric Sciences of the National Autonomous University of Mexico, respectively. We used the Turc evapotranspiration equation (Turc 1954) to estimate actual evapotranspiration. Least cost climatic corridors using future climate projections were used to test the assumption that climatic gradients are maintained in the future. We then prioritized climatic corridors using a multicriteria analysis guided by expert knowledge, incorporating factors such as indicators of human impact, vulnerability and exposure to climate change, and priority sites for biodiversity conservation and restoration. On average, more than 4,500 least cost climatic corridors were identified for each scenario. There is a high spatial coincidence in the geographical location of current and future climatic corridors (overlap > 90%). Fewer corridors were identified in the northern part of the country where natural vegetation is less fragmented, whereas in central and southern Mexico landscape fragmentation is greater, resulting in an increased number of corridors (Fig. 1). The use of open spatial data was key in identifying climatic corridors in order to support decision-making. The results provide a spatial guide to implement conservation and restoration actions to promote connectivity, in particular among climatic stable areas, thus supporting the achievement of Aichi Targets and Sustainable Development Goals. Also, it informs multiple stakeholders and sectors in land-use planning decisions and to promote the alignment of existing incentives to reduce habitat loss, degradation and fragmentation in key areas needed to maintain and recover landscape connectivity in the face of global change.
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