We perform multidisciplinary research aiming at providing the scientific knowledge needed for the conservation of biodiversity in all its forms. Our research is oriented towards threatened ecosystems, communities, species and populations. We have no a priori taxonomic or regional bias. Nevertheless due to historical and practical reasons most of our work deals with vertebrates. We use long-term data series to evaluate changes in composition, processes and dynamics in ecosystems, communities, populations and individuals as well as the impact of specific human activities at local and regional levels and the role of global change drivers. We make use techniques from many disciplines (physiology, epidemiology, complex systems modeling, etc) in order to determine causes, evaluate effects and make projections. We work in cooperation with other researchers both from CSIC and national and international Universities and research institutions.
Our research answers problem-specific questions from the perspective of several disciplines (evolutionary ecology, behavioral ecology, spatial ecology, population ecology and demography, conservation genetics, etc.). We aim at linking all our research efforts with problem-solving social demand. An important result of the increased use of multidisciplinary approaches is our contribution to the development of new paradigms, such as the conceptual framework for organismal movement.
Research on the conservation of vertebrates is an important item of our research, obtaining new insights on population dynamics by using ecophysiological approaches. We focus on population dynamics, aiming at increasing our predictive capacity through probabilistic forecasting and increasing our emphasis on community and ecosystem levels. In doing so, we aim to provide the basis for informed management of ecological systems, from the unaltered to the heavily disrupted by humans.
Our ongoing projects focus on population dynamics aiming at increasing our predictive capacity, first with the statistical description of large comparative datasets in order to identify general patterns and then, together with the available theory (or developing it when needed), modeling processes aiming at probabilistic forecasting. In the future, we aim at increasing our research at community and ecosystem levels, where we have ample room for improvement.