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Abstract:Despite the growing use of population viability analysis (PVA) Probando la Precisión del Análisis de Viabilidad Poblacional Resumen: A pesar del incremento en el uso del Análisis de Viabilidad Poblacional (AVP), las predicciones de estos modelos raramente han sido probadas con datos de campo que no fueron utilizados para el desarrollo inicial del modelo. Revisamos y discutimos una serie de métodos que pueden ser usados para probar la habilidad predictiva de los modelos utilizados en el APV. Además de probar las predicciones promedio, los méto-dos apropiados deben analizar la distribución de probabilidades de las predicciones de los modelos. Los métodos que discutimos proporcionan pruebas de las predicciones promedio, de la predicción de la frecuencia de eventos como extinciones y colonizaciones y de la distribución de probabilidades de variables de estado prevista. Discutimos aproximaciones visuales basadas en parcelas de las observaciones versus las predicciones y aproximaciones estadísticas basadas en la determinación de diferencias significativas entre las observaciones y las predicciones. Se identifican las ventajas y desventajas

We tested the effects of four data characteristics on the results of reserve selection algorithms. The data characteristics were nestedness of features (land types in this case), rarity of features, size variation of sites (potential reserves) and size of data sets (numbers of sites and features). We manipulated data sets to produce three levels, with replication, of each of these data characteristics while holding the other three characteristics constant. We then used an optimizing algorithm and three heuristic algorithms to select sites to solve several reservation problems. We measured efficiency as the number or total area of selected sites, indicating the relative cost of a reserve system. Higher nestedness increased the efficiency of all algorithms (reduced the total cost of new reserves). Higher rarity reduced the efficiency of all algorithms (increased the total cost of new reserves). More variation in site size increased the efficiency of all algorithms expressed in terms of total area of selected sites. We measured the suboptimality of heuristic algorithms as the percentage increase of their results over optimal (minimum possible) results. Suboptimality is a measure of the reliability of heuristics as indicative costing analyses. Higher rarity reduced the suboptimality of heuristics (increased their reliability) and there is some evidence that more size variation did the same for the total area of selected sites. We discuss the implications of these results for the use of reserve selection algorithms as indicative and real-world planning tools.

Team pursuit track cycling is a bicycle racing sport held on velodromes and it is part of the Summer Olympics. It involves the use of strategies to minimize the overall time that a team of cyclists needs to complete a race. We present an optimisation framework for team pursuit track cycling and show how to evolve strategies using metaheuristics for this interesting real-world problem. Our experimental results show that these heuristics lead to significantly better strategies than state-of-art strategies that are currently used by teams of cyclists.