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To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1523-1739.2010.01479.x Byline: MARC KERY (*[double dagger]), J. ANDREW ROYLE ([dagger]), HANS SCHMID (*), MICHAEL SCHAUB (*), BERNARD VOLET (*), GUIDO HAFLIGER (*), NIKLAUS ZBINDEN (*) Keywords: biodiversity monitoring; checklist; citizen science; distribution; monitoring; occupancy; site-occupancy model; species-distribution model; population trend; WinBUGS Abstract: Abstract: Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was Abstract (Spanish): Modelado de la Distribucion de la Ocupacion de Sitios para Corregir Estimaciones de Tendencias Poblacionales Derivadas de Observaciones Oportunistas Resumen: Las evaluaciones de especies frecuentemente deben ser derivadas de observaciones oportunistas de voluntarios (i.e., cientificos ciudadanos). La interpretacion de los datos resultantes para estimar las tendencias poblacionales esta plagada de problemas, incluyendo la distincion entre las tendencias poblacionales genuinas y las variaciones en el esfuerzo de observacion. Disenamos un metodo para corregir la variacion anual en el esfuerzo en la estimacion de las tendencias en la ocupacion (distribucion de especies) a partir de bases de datos de observaciones oportunistas de fauna o flora. Primero, se generan historias de deteccion para todos los sitios muestreo (i.e., cadenas de registros de deteccion-no deteccion). Muestreos intraestacionales replicados proporcionan informacion sobre la detectabilidad de un sitio ocupado. La detectabilidad representa el esfuerzo de observacion directamente; por lo tanto, la estimacion de la detectabilidad significa la correccion del esfuerzo de observacion. Segundo, los modelos de ocupacion de sitios son aplicados directamente al conjunto de datos de la historia de deteccion (i.e., sin agregacion por sitio y ano) para estimar la detectabilidad y distribucion de especies (ocupacion, i.e., la proporcion real de sitios donde ocurre una especie). Los modelos de ocupacion de sitios tambien proporcionan una estimacion no sesgada de los componentes de un cambio en la distribucion (i.e., tasas de colonizacion y extincion). Ilustramos nuestro metodo con datos de un proyecto de ciencia ciudadana en Suiza en el cual ornitologos de campo registran observaciones oportunistas. Analizamos datos recolectados para cuatro especies: Alcedo atthis y Accipiter nisus ampliamente distribuidas y Monticola saxatilis y Tichodroma muraria que son escasas. Nuestro metodo requiere que todas las especies observadas sean registradas. La detectabilidad fue Author Affiliation: (*)Swiss Ornithological Institute, 6204 Sempach, Switzerland ([dagger])U.S. Geological Survey, Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, MD 20708, U.S.A. Article History: Paper submitted May 20, 2009; revised manuscript accepted November 9, 2009. Article note: ([double dagger]) email marc.kery@vogelwarte.ch

The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist 'Adam,' which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Soccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1523-1739.2006.00353.x Keywords: biodiversity management; conservation; economics; interdisciplinary research Abstract: Abstract: Ecologists and economists both use models to help develop strategies for biodiversity management. The practical use of disciplinary models, however, can be limited because ecological models tend not to address the socioeconomic dimension of biodiversity management, whereas economic models tend to neglect the ecological dimension. Given these shortcomings of disciplinary models, there is a necessity to combine ecological and economic knowledge into ecological-economic models. It is insufficient if scientists work separately in their own disciplines and combine their knowledge only when it comes to formulating management recommendations. Such an approach does not capture feedback loops between the ecological and the socioeconomic systems. Furthermore, each discipline poses the management problem in its own way and comes up with its own most appropriate solution. These disciplinary solutions, however, are likely to be so different that a combined solution considering aspects of both disciplines cannot be found. Preconditions for a successful model-based integration of ecology and economics include (1) an in-depth knowledge of the two disciplines, (2) the adequate identification and framing of the problem to be investigated, and (3) a common understanding between economists and ecologists of modeling and scale. To further advance ecological-economic modeling the development of common benchmarks, quality controls, and refereeing standards for ecological-economic models is desirable. Abstract (Spanish): Modelado Ecologico-Economico para la Gestion de Biodiversidad: Potencial, Escollos y Prospectos Resumen: Tanto ecologos como economistas utilizan modelos para desarrollar estrategias para la gestion de biodiversidad. Sin embargo, el uso practico de modelos disciplinares puede ser limitado porque los modelos ecologicos tienden a no considerar la dimension socioeconomica de la gestion de biodiversidad, mientras que los modelos economicos tienden a descuidar la dimension ecologica. Considerando estas limitaciones de los modelos disciplinares, existe la necesidad de combinar conocimiento ecologico y economico en modelos ecologico-economicos. No es suficiente que los cientificos trabajen separadamente en sus propias disciplinas y que combinen su conocimiento solo cuando se trata de formular recomendaciones de gestion. Tal aproximacion no captura la realimentacion entre los sistemas ecologico y socioeconomico. Mas aun, cada disciplina plantea el problema de gestion en su propia forma y propone su propia solucion adecuada. Sin embargo, es probable que estas soluciones disciplinares sean tan diferentes que no es posible encontrar una solucion que combine aspectos de ambas disciplinas. Las condiciones previas para una exitosa integracion, basada en modelos, de la ecologia y la economia incluyen (1) un conocimiento profundo de las dos disciplinas, (2) la adecuada identificacion y encuadre del problema a investigar, y (3) un entendimiento comun de modelado y escala entre economistas y ecologos. Para avanzar en el modelado ecologico-economico es deseable el desarrollo de puntos de referencia comunes, controles de calidad y estandares de referencia para modelos ecologico-economicos. Author Affiliation: (*)Department of Economics, UFZ-Centre for Environmental Research, Permoser Strasse 15, 04318 Leipzig, Germany ([dagger])Department of Economics, Norwegian College of Fishery Science, University of Tromso, 9037 Tromso, Norway ([double dagger])Interdisciplinary Institute for Environmental Economics, University of Heidelberg, Bergheimer Strasse 20, 69115 Heidelberg, Germany (s.)Environment Department, University of York at Heslington, York YO 10 5DD, United Kingdom (**)The Ecology Centre, University of Queensland, Brisbane, QLD 4072, Australia ([dagger][dagger])Department of Economics and Finance, University of Wyoming, 1000 E. University Avenue, Laramie WY 82071, U.S.A. ([double dagger][double dagger])Department of Economics, Norwegian University of Science and Technology, Dragvoll University Campus, 7491 Dragvoll-Trondheim, Norway (s.s.)Department of Landscape Ecology, ALTERRA Green World Research, Droevendaalsesteg 3, 6708 Wageningen, The Netherlands (***)Department of Ecological Modelling, UFZ-Centre for Environmental Research, Permoser Strasse 15, 04318 Leipzig, Germany Article History: Paper submitted April 12, 2005; revised manuscript accepted August 29, 2005. Article note: ([dagger][dagger][dagger]) email frank.waetzold@ufz.de ([double dagger][double dagger][double dagger]) Current address: Global Institute of Sustainability, Arizona State University, Box 873211, Tempe, AZ 85287-3211, U.S.A.