Habitat loss and fragmentation are affecting populations of forest dwelling mammalian carnivores worldwide. In southern Chile, a biodiversity hotspot, anthropogenic activities have resulted in high loss of native forest cover. The guiña, or kodkod cat, Leopardus guigna is a small forest-dwelling felid with a narrow range in the temperate forest of southern Chile. The few existing studies of the species have suggested that it is almost exclusively restricted to large tracts of native forest. This paper reports a study in the temperate forest within a fragmented Andean piedmont landscape which demonstrates that smaller forest fragments in the farmland matrix are playing a key role in the persistence of the guiña. We estimated occupancy in both continuous native forest and remnant forest fragments and, with single-species/single-season models, evaluated the extent to which forest cover, habitat type and proximity to protected areas have a modulating effect on occupancy. A continuous survey during 2008-2009, in three seasons of 90-100 days each, accumulated 6,200 camera trap days and returned 47 photographs of guiña. Total detection in fragments was higher than in continuous forests, with detection confirmed in almost 70% of studied fragments. We found that probability of a site being occupied significantly increased with forest cover (adult/secondary forest, scrubland) and probability was low (, 0.2) in sites with , 50% of surrounding forest cover. Our study highlights the importance of remnant forest fragments in the mosaic of extensive agriculture for the spatial dynamics of a guiña population and hence for the future conservation of the species.

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SUMMARYForest growth models are key tools for both managing and understanding forest dynamics. These models have evolved from yield tables to models that simulate ecological and physiological processes. Because several approaches exist for modelling forest growth, understanding their strengths and weaknesses is complex. Here, we present a review of forest growth modelling and focus on the most common types of models: growth and yield, succession, process-based, and hybrid. These models might or might not include stochastic components. Worldwide there is a trend towards building hybrid models, because they are best suited to represent the effect of climate change on tree growth. However, empirical evidence has not shown major differences in predictions between hybrid and simpler growth models. Finally, we emphasize that none type of growth model is demonstrably better than others and that each is used to answer a great variety of research and management questions.Key words: modelling, simulators, forest ecology, tree physiology, climate change. RESUMENLos modelos de crecimiento de bosques son herramientas claves para el manejo y la comprensión de la dinámica de los bosques. Estos modelos han evolucionado en complejidad desde las tablas de rendimiento a modelos que simulan procesos fisiológicos y ecológicos. Dada la actual multiplicidad de aproximaciones para modelar el crecimiento de los bosques, es difícil entender sus diferencias, fortalezas y debilidades. En este artículo se presenta una revisión del estado del conocimiento sobre modelos de crecimiento de bosques considerando los siguientes tipos de modelos: crecimiento y rendimiento, sucesión, basados en procesos, e híbridos. Además, se hace énfasis en que los modelos anteriores pueden, o no, incluir componentes estocásticos. A nivel mundial existe una tendencia hacia construir modelos híbridos por sus bondades al poder representar más naturalmente el efecto del cambio climático en el crecimiento de los árboles, sin embargo, la evidencia empírica no ha demostrado mayores diferencias predictivas con modelos más simples. Finalmente, se enfatiza que no existen tipos de modelos mejores que otros, sino que cada cual se emplea para responder diversas preguntas de investigación o de manejo.Palabras clave: modelación, simuladores, ecología forestal, fisiología vegetal, cambio climático. INTRODUCCIÓNLos factores ambientales (e.g. clima, suelo y topografía), características genéticas y la competencia influyen en el crecimiento de los árboles. Este crecimiento puede ser medido a diferentes niveles dentro de la estructura del árbol o bosque y mediante diversas variables tales como diámetro, área basal, altura, volumen y biomasa. Así, el crecimiento es producto de diversos factores bióticos y abióticos, que interactúan sobre un árbol y sobre el bosque. Conocer cómo estos factores afectan el crecimiento de los árboles y bosques es fundamental para entender cómo varía su estructura y composición en el tiempo. Los bosques son sistemas biológicos dinámicos que están continuam...

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Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values.