Tropical mountains are hot spots of biodiversity and endemism, but the evolutionary origins of their unique biotas are poorly understood. In varying degrees, local and regional extinction, long-distance colonization, and local recruitment may all contribute to the exceptional character of these communities. Also, it is debated whether mountain endemics mostly originate from local lowland taxa, or from lineages that reach the mountain by long-range dispersal from cool localities elsewhere. Here we investigate the evolutionary routes to endemism by sampling an entire tropical mountain biota on the 4,095-metre-high Mount Kinabalu in Sabah, East Malaysia. We discover that most of its unique biodiversity is younger than the mountain itself (6 million years), and comprises a mix of immigrant pre-adapted lineages and descendants from local lowland ancestors, although substantial shifts from lower to higher vegetation zones in this latter group were rare. These insights could improve forecasts of the likelihood of extinction and 'evolutionary rescue' in montane biodiversity hot spots under climate change scenarios.

No abstract

No abstract

No abstract

Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter's exponential smoothing model, have been investigated for their applicability for forecasting weekly reference crop ET. A seasonal ARIMA model with one autoregressive and one moving average process and with a seasonality of 52 weeks was found to be an appropriate stochastic model. The ARIMA and Winter's models were compared with a simple ET model to assess their performance in forecasting. The forecast errors produced by these models were very small and the models would be promisingly of great use in real-time irrigation management.Prévision de séries d'évapotranspiration hebdomadaire de référence Résumé L'évapotranspiration (ET) est un processus important du cycle hydrologique et doit être estimé précisément pour élaborer un calendrier d'irrigation convenable et pour une gestion optimale des systèmes de ressources en eau. La variabilité au cours du temps des caractéristiques de l'ET entraîne la nécessité de prévoir l'ET. Dans cette étude, deux techniques, à savoir un modèle ARIMA saisonnier et le modèle de lissage exponentiel de Winter, ont été examinées en vue de leur application à la prévision d'une ET hebdomadaire de référence. Un modèle ARIMA saisonnier comprenant une composante autorégressive et un composante de moyenne mobile et dont la saisonnalité est hebdomadaire s'est révélé approprié. Le modèle ARIMA et celui de Winter ont été comparés à un modèle simple d'ET afin apprécier leurs performances en matière de prévision. Les erreurs de prévision de ces modèles étaient très petites et ils apparaissent comme très prometteurs pour la gestion de l'irrigation en temps réel.