A Freshwater Classification Approach for Biodiversity Conservation Planning

Abstract

Freshwater biodiversity is highly endangered and faces increasing threats worldwide. To be complete, regional plans that identify critical areas for conservation must capture representative components of freshwater biodiversity as well as rare and endangered species. We present a spatially hierarchical approach to classify freshwater systems to create a coarse filter to capture representative freshwater biodiversity in regional conservation plans. The classification framework has four levels that we described using abiotic factors within a zoogeographic context and mapped in a geographic information system. Methods to classify and map units are flexible and can be automated where high-quality spatial data exist, or can be manually developed where such data are not available. Products include a spatially comprehensive inventory of mapped and classified units that can be used remotely to characterize regional patterns of aquatic ecosystems. We provide examples of classification procedures in data-rich and data-poor regions from the Columbia River Basin in the Pacific Northwest of North America and the upper Paraguay River in central South America. The approach, which has been applied in North, Central, and South America, provides a relatively rapid and pragmatic way to account for representative freshwater biodiversity at scales appropriate to regional assessments. Resumen: La biodiversidad de agua dulce está en peligro y enfrenta amenazas crecientes en todo el mundo.Para ser completos, los planes regionales que identificanáreas críticas para la conservación deben incluir componentes representativos de la biodiversidad de agua dulce así como especies raras y en peligro. Presentamos un método espacialmente jerárquico para clasificar sistemas de agua dulce para crear un filtro grueso que capte a la biodiversidad de agua dulce en los planes regionales de conservación. La estructura de la clasificación tiene cuatro niveles que describimos utilizando factores abióticos en un contexto zoogeográfico y localizamos en un sistema de información geográfico. Los métodos para clasificar y trazar mapas son flexibles y pueden ser automatizados, donde existen datos espaciales de alta calidad, o desarrollados manualmente cuando tales datos no están disponibles. Los productos incluyen un inventario completo de unidades mapeadas y clasificadas que pueden ser usadas remotamente para caracterizar patrones regionales de ecosistemas acuáticos. Proporcionamos ejemplos de procedimientos de clasificación en regiones ricas y pobres en datos en la cuenca del Río Columbia en el noroeste de Norte América y del Río Paraguay en Sudamérica central. El método, que ha sido aplicado en Norte, Centro y Sudamérica, proporciona una forma relativamente rápida y pragmática de contabilizar biodiversidad de agua dulce representativa en escalas adecuadas para evaluaciones regionales.Palabras Clave: biodiversidad de agua

Multiscale landscape and wetland drivers of lake total phosphorus and water color

“…EDUs are part of a hierarchical ecoregion framework that was developed to classify freshwater ecosystems for aquatic biodiversity conservation using landscape features, such as climate and landform. The Nature Conservancy delineated EDU boundaries by grouping eight-digit USGS Hydrologic Unit watersheds that share common physiographic and climatic characteristics (Higgins et al 2005). We chose this framework because it has been shown to capture more variation in lake-water chemistry variables, on average, than other ecoregion frameworks (Cheruvelil et al 2008).…”

“…Regional landscape variables in this paper were quantified within Ecological Drainage Units (EDU; Higgins et al 2005). EDUs are part of a hierarchical ecoregion framework that was developed to classify freshwater ecosystems for aquatic biodiversity conservation using landscape features, such as climate and landform.…”

GIS Framework for Large River Geomorphic Classification to Aid in the Evaluation of Flow-Ecology Relationships

“…Many studies have taken advantage of the capability of a GIS to spatially organize and find similarity in data for use in river classification (Dauble et al 2003;Hersh and Maidment 2007;Higgins et al 2005;Jacobson, Elliot, and Huhmann 2010;Snelder, Biggs, and Weatherhead 2004;Wang et al 2011;Whited, Stanford, and Kimball 2002). The integration of readily available, current GIS data is vital to the success of any newer GIS river classification approach that is to be spatially transferable.…”

Uncertainty and the design of in-situ biodiversity-monitoring programs

“…For instance, vascular plants are often used to identify "habitats", "ecoregions" or "ecosystems" that purportedly represent boundaries to the distribution of other organisms, such as insects, mammals or fish (e.g. Olson et al 2001, Higgins et al 2005). The technology is continually advancing, and it is not possible to predict what remotesensing products will be available in the future, but the greatest sources of uncertainty at the moment relate to the relationships between surrogates and the target organisms in which we are interested (Magnusson 2004, Franklin 2009, Caro 2010.…”