Evidence that recent climatic changes have expanded the potential geographical range of the Mediterranean fruit fly.

The species distributions migration poleward and into higher altitudes in a warming climate is especially concerning for economically important insect pest species, as their introduction can potentially occur in places previously considered unsuitable for year-round survival. We explore the expansion of the climatically suitable areas for a horticultural pest, the Mediterranean fruit fly (medfly) Ceratitis capitata (Diptera, Tephritidae), with an emphasis on Europe and California. We reviewed and refined a published CLIMEX model for C.

The kinetic Ising model encapsulates essential dynamics of land pattern change.

A land pattern change represents a globally significant trend with implications for the environment, climate and societal well-being. While various methods have been developed to predict land change, our understanding of the underlying change processes remains inadequate. To address this issue, we investigate the suitability of the two-dimensional kinetic Ising model (IM), an idealized model from statistical mechanics, for simulating land change dynamics.

A quixotic view of spatial bias in modelling the distribution of species and their diversity.

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them.

Abundance of Ganoderma sp. in Europe and SW Asia: modelling the pathogen infection levels in local trees using the proxy of airborne fungal spore concentrations.

Ganoderma comprises a common bracket fungal genus that causes basal stem rot in deciduous and coniferous trees and palms, thus having a large economic impact on forestry production. We estimated pathogen abundance using long-term, daily spore concentration data collected in five biogeographic regions in Europe and SW Asia. We hypothesized that pathogen abundance in the air depends on the density of potential hosts (trees) in the surrounding area, and that its spores originate locally.

belg: A Tool for Calculating Boltzmann Entropy of Landscape Gradients.

Entropy is a fundamental concept in thermodynamics that is important in many fields, including image processing, neurobiology, urban planning, and sustainability. As of recently, the application of Boltzmann entropy for landscape patterns was mostly limited to the conceptual discussion. However, in the last several years, a number of methods for calculating Boltzmann entropy for landscape mosaics and gradients were proposed.

Airborne Alternaria and Cladosporium fungal spores in Europe: Forecasting possibilities and relationships with meteorological parameters.

Airborne fungal spores are prevalent components of bioaerosols with a large impact on ecology, economy and health. Their major socioeconomic effects could be reduced by accurate and timely prediction of airborne spore concentrations. The main aim of this study was to create and evaluate models of Alternaria and Cladosporium spore concentrations based on data on a continental scale.

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations.

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km) in Southwest Ohio and Taylorsville Lake (11.

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset.

Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources.

Forecasting model of , , and pollen concentration levels using spatiotemporal correlation properties of pollen count.

The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of , , and using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split.

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula.

Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data.