I. Alados

Bio: I. Alados is an academic researcher from University of Málaga. The author has contributed to research in topics: Photosynthetically active radiation & Irradiance. The author has an hindex of 17, co-authored 26 publications receiving 939 citations. Previous affiliations of I. Alados include University of Granada.

Terrestrial insects along elevation gradients: species and community responses to altitude

Abstract: The literature on the response of insect species to the changing environments experienced along altitudinal gradients is diverse and widely dispersed. There is a growing awareness that such responses may serve as analogues for climate warming effects occurring at a particular fixed altitude or latitude over time. This review seeks, therefore, to synthesise information on the responses of insects and allied groups to increasing altitude and provide a platform for future research. It focuses on those functional aspects of insect biology that show positive or negative reaction to altitudinal changes but avoids emphasising adaptation to high altitude per se. Reactions can be direct, with insect characteristics or performance responding to changing environmental parameters, or they can be indirect and mediated through the insect's interaction with other organisms. These organisms include the host plant in the case of herbivorous insects, and also competitor species, specific parasitoids, predators and pathogens. The manner in which these various factors individually and collectively influence the morphology, behaviour, ecophysiology, growth and development, survival, reproduction, and spatial distribution of insect species is considered in detail. Resultant patterns in the abundance of individual species populations and of community species richness are examined. Attempts are made throughout to provide mechanistic explanations of trends and to place each topic, where appropriate, into the broader theoretical context by appropriate reference to key literature. The paper concludes by considering how montane insect species will respond to climate warming.

Advantages of diffuse radiation for terrestrial ecosystem productivity

Abstract: [1] Clouds and aerosols alter the proportion of diffuse radiation in global solar radiation reaching the Earth's surface. It is known that diffuse and direct beam radiation differ in the way they transfer through plant canopies and affect the summation of nonlinear processes like photosynthesis differently than what would occur at the leaf scale. We compared the relative efficiencies of canopy photosynthesis to diffuse and direct photosynthetically active radiation (PAR) for a Scots pine forest, an aspen forest, a mixed deciduous forest, a tallgrass prairie and a winter wheat crop. The comparison was based on the seasonal patterns of the parameters that define the canopy photosynthetic responses to diffuse PAR and those that define the responses to direct PAR. These parameters were inferred from half-hourly tower CO2 flux measurements. We found that: (1) diffuse radiation results in higher light use efficiencies by plant canopies; (2) diffuse radiation has much less tendency to cause canopy photosynthetic saturation; (3) the advantages of diffuse radiation over direct radiation increase with radiation level; (4) temperature as well as vapor pressure deficit can cause different responses in diffuse and direct canopy photosynthesis, indicating that their impacts on terrestrial ecosystem carbon assimilation may depend on radiation regimes and thus sky conditions. These findings call for different treatments of diffuse and direct radiation in models of global primary production, and studies of the roles of clouds and aerosols in global carbon cycle.

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Abstract: Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and pho...

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Abstract: The objective of this study is to compare the predictive ability of Bayesian regularization with Levenberg–Marquardt Artificial Neural Networks. To examine the best architecture of neural networks, the model was tested with one-, two-, three-, four-, and five-neuron architectures, respectively. MATLAB (2011a) was used for analyzing the Bayesian regularization and Levenberg–Marquardt learning algorithms. It is concluded that the Bayesian regularization training algorithm shows better performance than the Levenberg–Marquardt algorithm. The advantage of a Bayesian regularization artificial neural network is its ability to reveal potentially complex relationships, meaning it can be used in quantitative studies to provide a robust model.