El complejo de páramos Guantiva-La Rusia (GLA) se encuentra ubicado en el flanco occidental de la Cordillera Oriental de los Andes colombianos, en 23 municipios de los departamentos de Boyacá y Santander. Incluye los páramos de Duitama, Pan de Azúcar, Guanentá-Alto Río Fonce, Belén, Cruz Colorada, Santa Rosa, Cerinza, Carnicerías y Guata, Consuelo, Güina, Onzaga, Tutazá, Guantiva, Sativanorte, Sativasur, Susacón y Soatá, entre otros. Abarca una extensión de aproximadamente 119.000 ha, con un rango de elevación entre los 2.800 y los 4.280 m s.n.m., y cuenta con unas 85 lagunas y humedales. Por su posición geográfica, incluye varias provincias climáticas a lo largo del complejo, con un régimen de lluvias bimodal y fluctuaciones entre 700 y más de 3000 mm/año, con una zona muy húmeda en el sector sur y oeste del complejo, y una mucho más seca en el sector noreste, bajo la influencia del Cañón del Chicamocha. Por su localización hacia el norte de la Cordillera Oriental, rodeado por otros seis complejos de páramos (Iguaque-Merchán, Yariguíes, Almorzadero, Tota-Bijagual-Mamapacha, Pisba, y Cocuy), su extensión y su variedad climática y ecológica, es una de las zonas en el mundo con mayor riqueza de especies de frailejones. En efecto, hasta el momento se ha confirmado la presencia de 20 especies, ilustradas aquí. Se destacan 11 especies amenazadas, en alguna de las categorías de riesgo de extinción: 3 vulnerables (VU), 5 amenazadas (EN) y 3 críticamente amenazadas. Este trabajo hace parte del proyecto “¿Cómo almacenan el agua los Páramos? El papel de las plantas y las personas” (PARAGUAS), que reúne científicos británicos y colombianos para entender cómo la diversidad de plantas y actividades de las personas contribuyen con la regulación y provisión de agua del ecosistema de páramo. Para conocer más sobre el proyecto, visita https://paraguas.ceh.ac.uk/es.
The páramos biome of the northern Andes is a collection of high-mountain tropical grassland wetland ecosystems that provides important ecosystem services including hydrological buffering and water supply. Human activities in these ecosystems transform vegetation cover and soil hydro-physical properties, affecting their hydrological performance and water quality and quantity. Here, we conducted a systematic review on the influence of land use (agriculture, livestock grazing, and afforestation) on the hydro-physical properties of páramo soils and analyzed its implications for streamflow buffering. Our review protocol identified 32 relevant papers, from which key hydro-physical properties linked to streamflow variability were available: soil organic matter (SOM), soil organic carbon (SOC), porosity, bulk density, saturated hydraulic conductivity, and water retention capacity (WRC). The analysis shows that soils with native cover are characterized by a porous structure that allows a high WRC and SOM content. Agriculture increases macroporosity but it leads to bare fallow plots that promote loss of nutrients and SOM. Burning generates hydrophobic aggregates that affect WRC. Livestock grazing produces soil compaction and increases bulk density, reducing infiltration and WRC. Lastly, afforestation with exotic species (e.g. pines, eucalyptus) decreases SOM and WRC by changing soil structure. In general, the analyzed land-use activities generate hydrophobic aggregates, increase bulk density, promote erosion and runoff, and impair hydrological buffering capacity. This integrated evidence from multiple empirical studies can be used to effectively communicate the effects of different land use practices on páramo soils, provide information for modelling in data-scarce situations, and contribute to decision making processes for land use planning and conservation.
The identification and counting of plant individuals is essential for environmental monitoring. UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and so provide a great opportunity to enhance current plant and in-situ field surveying. However, accurate mapping of individual plants from UAV imagery remains challenging, given the great variation in the sizes and geometries of individual plants and in their distribution. This is true even for deep learning based semantic segmentation and classification methods. In this research, a novel Scale Sequence Residual U-Net (SS Res U-Net) deep learning method was proposed, which integrates a set of Residual U-Nets with a sequence of input scales that can be derived automatically. The SS Res U-Net classifies individual plants by continuously increasing the patch scale, with features learned at small scales passing gradually to larger scales, thus, achieving multi-scale information fusion while retaining fine spatial details of interest. The SS Res U-Net was tested to identify and map frailejones (all plant species of the subtribe Espeletiinae), the dominant plants in one of the world's most biodiverse high-elevation ecosystems (i.e. the páramos) from UAV imagery. Results demonstrate that the SS Res U-Net has the ability to self-adapt to variation in objects, and consistently achieved the highest classification accuracy (91.67% on average) compared with four state-of-the-art benchmark approaches. In addition, SS Res U-Net produced the best performances in terms of both robustness to training sample size reduction and computational efficiency compared with the benchmarks. Thus, SS Res U-Net shows great promise for solving remotely sensed semantic segmentation and classification tasks, and more general machine intelligence. The prospective implementation of this method to identify and map frailejones in the páramos will benefit immensely the monitoring of their populations for conservation assessments and management, among many other applications.
Páramos, a neotropical alpine grassland‐peatland biome of the northern Andes and Central America, play an essential role in regional and global cycles of water, carbon, and nutrients. They act as water towers, delivering water and ecosystem services from the high mountains down to the Pacific, Caribbean, and Amazon regions. Páramos are also widely recognized as a biodiversity and climate change hot spots, yet they are threatened by anthropogenic activities and environmental changes. Despite their importance for water security and carbon storage, and their vulnerability to human activities, only three decades ago, páramos were severely understudied. Increasing awareness of the need for hydrological evidence to guide sustainable management of páramos prompted action for generating data and for filling long‐standing knowledge gaps. This has led to a remarkably successful increase in scientific knowledge, induced by a strong interaction between the scientific, policy, and (local) management communities. A combination of well‐established and innovative approaches has been applied to data collection, processing, and analysis. In this review, we provide a short overview of the historical development of research and state of knowledge of the hydrometeorology, flux dynamics, anthropogenic impacts, and the influence of extreme events in páramos. We then present emerging technologies for hydrology and water resources research and management applied to páramos. We discuss how converging science and policy efforts have leveraged traditional and new observational techniques to generate an evidence base that can support the sustainable management of páramos. We conclude that this co‐evolution of science and policy was able to successfully cover different spatial and temporal scales. Lastly, we outline future research directions to showcase how sustainable long‐term data collection can foster the responsible conservation of páramos water towers.