Search

Wind turbines (WT) cause bird and bat mortalities which depend on the WT and landscape features. The effects of WT features and environmental variables at different spatial scales associated to bat deaths in a mountainous and forested area in Thrace, NE Greece were investigated. Initially, we sought to quantify the most lethal WT characteristic between tower height, rotor diameter and power. The scale of interaction distance between bat deaths and the land cover characteristics surrounding the WTs was quantified. A statistical model was trained and validated against bat deaths and WT, land cover and topography features. Variance partitioning between bat deaths and the explanatory covariates was conducted. The trained model was used to predict bat deaths attributed to existing and future wind farm development in the region. Results indicated that the optimal interaction distance between WT and surrounding land cover was 5 km, the larger distance than the ones examined. WT power, natural land cover type and distance from water explained 40 %, 15 % and 11 % respectively of the total variance in bat deaths by WTs. The model predicted that operating but not surveyed WTs comprise of 377.8% and licensed but not operating yet will contribute to 210.2% additional deaths than the ones recorded. Results indicate that among all WT features and land cover characteristics, wind turbine power is the most significant factor associated to bat deaths. Results indicated that WTs located within 5 km buffer comprised of natural land cover types have substantial higher deaths. More WT power will result in more deaths. Wind turbines should not be licensed in areas where natural land cover at a radius of 5km exceeds 50%. These results are discussed in the climate-land use-biodiversity-energy nexus.

Disease spread is a complex phenomenon requiring an interdisciplinary approach. Covid-19 exhibited a global spatial spread in a very short time frame resulting in a global pandemic. Data of web search effort in Greece on Covid-19 as a topic for one year on a weekly temporal scale were analyzed using governmental intervention measures such a s school closures, movement restrictions, national and international travelling restrictions, stay at home requirements, mask requirements, financial support measures, and epidemiological variables such as new cases and new deaths as potential explanatory covariates. The relationship between web search effort on Covid-19 and the 16 in total explanatory covariates was analyzed with machine learning. Web search in time was compared with the corresponding epidemiological situation, expressed by the Rt at the same week. Results indicated that the trained model exhibited a fit of R2 = 91% between the actual and predicted web search effort. The top five variables for predicting web search effort were new deaths, the opening of international borders to non-Greek nationals, new cases, testing policy, and restrictions in internal movements. Web search peaked during the same weeks that the Rt was peaking although new deaths or new cases were not peaking during those dates, and Rt rarely is reported in public media. As both web search effort and Rt peaked during 1-15 August 2020, the peak of the tourist season, the implications of this are discussed.

Fire has been an integral part of the Earth for millennia. Several recent wildfires have exhibited an unprecedented spatial and temporal extent and their control is beyond national firefighting capabilities. Prescribed or controlled burning treatments are debated as a potential measure for ameliorating the spread and intensity of wildfires. Machine learning analysis using random forests was performed in a spatio-temporal data set comprising a large number of savanna fires across 22 years. Results indicate that fire return interval was not an important predictor of fire spread rate or fire intensity, having a feature importance of 3.5%, among eight other predictor variables. Manipulating burn seasonality showed a feature importance of 6% or less regarding fire spread rate or fire intensity. While manipulated fire return interval and seasonality moderated both fire spread rate and intensity, their overall effects were low in comparison with meteorological (hydrological and climatic) variables. The variables with the highest feature importance regarding fire spread rate resulted in fuel moisture with 21%, relative humidity with 15%, wind speed with 14%, and last years rainfall with 14%. The variables with the highest feature importance regarding fire intensity included fuel load with 21.5%, fuel moisture with 16.5%, relative humidity with 12.5%, air temperature with 12.5%, and rainfall with 12.5%. Predicting fire spread rate and intensity has been a poor endeavour thus far and we show that more data of the variables already monitored would not result in higher predictive accuracy., Comment: Journal paper published in: Stochastic Environmental Research & Risk Assessment

The rapid increase of transportation infrastructure during the recent decades has caused a number of effects on bird species, including collision mortality, habitat loss, fragmentation and noise. This paper investigates the effects of traffic noise and road-induced fragmentation on breeding bird richness in Cyprus. Cyprus, situated along one of the main migratory routes for birds, has a rich and diverse avifauna threatened by an ever-expanding road network and a road density among the highest in Europe. In this first island-wide study we used data from 102 breeding birds recorded in 10 km x 10 km grid cells. Within every cell we calculated road traffic noise and eight road-related properties. Most of the grid cells are subject to intense fragmentation and traffic noise with combined impact hotspots located even within protected areas (such as Cape Greco, and the Troodos Massif). Results from variance partitioning indicated that road-related properties (total road extent and road length) accounted for a combined 59% of variation in species richness, followed by fragmentation-related properties and noise properties. The study posits the need for further in-depth research on the effects of road networks on birds, and road construction, particularly in protected areas within Mediterranean islands.

Spatio temporal data are more ubiquitous and richer than even before and the availability of such data poses great challenges in data analytics. Ecological facilitation, the positive effect of density of individuals on the individual's survival across a stress gradient, is a complex phenomenon. A large number of tree individuals coupled with soil moisture, temperature, and water stress data across a long temporal period were followed. Data driven analysis in the absence of hypothesis was performed. Information theoretic analysis of multiple statistical models was employed in order to quantify the best data-driven index of vegetation density and spatial scale of interactions. Sequentially, tree survival was quantified as a function of the size of the individual, vegetation density, and time at the optimal spatial interaction scale. Land surface temperature and soil moisture were also statistically explained by tree size, density, and time. Results indicated that in space both facilitation and competition coexist in the same ecosystem and the sign and magnitude of this depend on the spatial scale. Overall, within the optimal data driven spatial scale, tree survival was best explained by the interaction between density and year, sifting overall from facilitation to competition through time. However, small sized trees were always facilitated by increased densities, while large sized trees had either negative or no density effects. Tree size was more important predictor than density in survival and this has implications for nature based solutions: maintaining large tree individuals or planting species that can become large-sized can safeguard against tree less areas by promoting survival at long time periods through harsh environmental conditions. Large trees had also a significant effect in moderating land surface temperature.

Data of alien species presences are generally more readily available in protected than non-protected areas due to higher sampling efforts inside protected areas. Are the results and conclusions based on analyses of data collected in protected areas representative of wider non-protected regions? We address this question by analysing some recently published data of alien plants in Greece. Mixed effects models were used with alien species presences in 8.25 x 8.25 km cells as dependent variable and the percentage of protected area, as well as the agricultural and artificial land cover types richness (as indicators of human presence) as independent variables. In addition, the spatial cross-correlation between the percentage of protected area and alien species richness was examined across scales. Results indicated that the percentage of protected area per cell is a poor predictor of alien species richness. Spatial analysis indicated that cells with higher percentage of protected areas have slightly less alien species than cells with lower percentage of protected areas. This result is likely to be driven by the overall negative correlation between habitat protection and anthropogenic activities. Thus, the conclusions deduced by data deriving from protected areas are likely to hold true for patterns of alien species in non-protected areas when the human pressures are accounted for., Comment: accepted journal paper

Understanding the spread of any disease is a highly complex and interdisciplinary exercise as biological, social, geographic, economic, and medical factors may shape the way a disease moves through a population and options for its eventual control or eradication. Disease spread poses a serious threat in animal and plant health and has implications for ecosystem functioning and species extinctions as well as implications in society through food security and potential disease spread in humans. Space-time epidemiology is based on the concept that various characteristics of the pathogenic agents and the environment interact in order to alter the probability of disease occurrence and form temporal or spatial patterns. Epidemiology aims to identify these patterns and factors, to assess the relevant uncertainty sources, and to describe disease in the population. Thus disease spread at the population level differs from the approach traditionally taken by veterinary practitioners that are principally concerned with the health status of the individual. Patterns of disease occurrence provide insights into which factors may be affecting the health of the population, through investigating which individuals are affected, where are these individuals located and when did they become infected. With the rapid development of smart sensors, social networks, as well as digital maps and remotely-sensed imagery spatio-temporal data are more ubiquitous and richer than ever before. The availability of such large datasets (Big data) poses great challenges in data analysis. In addition, increased availability of computing power facilitates the use of computationally-intensive methods for the analysis of such data. Thus new methods as well as case studies are needed to understand veterinary and ecological epidemiology. A special issue aimed to address this topic.

Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed., Comment: to appear in the journal Stochastic Environmental Research & Risk Assessment

Protected areas are an important conservation measure. However, there are controversial findings regarding whether closed areas are beneficial for species and habitat conservation as well as landings. Species dispersal is acknowledged as a key factor for the design and impacts of closed areas. A series of agent based models using random diffusion to model fish dispersal were run before and after habitat protection. All results were normalised without the protected habitat in each scenario to detect the relative difference after closing an area, all else being equal. Results show that landings of species with short dispersal ranges will take longer to reach the levels of pre Marine Protected Areas (MPAs) establishment than landings of species with long dispersal ranges. Further the establishment of an MPA generates a higher relative population source within the MPA for species with low dispersal abilities than for species with high dispersal abilities. Results derived here show that there exists a win-win feasible scenario that maximises both fish biomass as well as fish catches., Comment: to appear in the journal: Web Ecology

Bovine tuberculosis (TB) is a chronic disease in cattle that causes a serious food security challenge to the agricultural industry in terms of dairy and meat production. In GB, Scotland has had a risk based surveillance testing policy under which high risk herds are tested frequently, and in Sept 2009 was officially declared as TB free. Wales have had an annual or more frequent testing policy for all cattle herds since Jan 2010, while in England several herds are still tested every 4 years except some high TB prevalence areas where annual testing is applied. Time series analysis using publicly available data for total tests on herds, total cattle slaughtered, new herd incidents, and herds not TB free, were analysed globally for GB and locally for the constituent regions of Wales, Scotland, West, North, and East England. After detecting trends over time, underlying regional differences were compared with the testing policies in the region. Total cattle slaughtered are decreasing in Wales, Scotland and West England, but increasing in the North and East English regions. New herd incidents, i.e., disease incidence, are decreasing in Wales, Scotland, West English region, but increasing in North and East English regions. Herds not TB free, are increasing in West, North, and East English regions, while they are decreasing in Wales and Scotland. Total cattle slaughtered were positively correlated with total tests in the West, North, and East English regions, with high slopes of regression. There was no correlation between total cattle slaughtered and total tests on herds in Wales indicating that herds are tested frequent enough in order to detect all likely cases and so control TB. The main conclusion of the analysis conducted here is that more frequent testing is leading to lower TB infections in cattle both in terms of TB prevalence as well as TB incidence., Comment: (in press) Stochastic Environmental Research and Risk Assessment (2015)

Two endemic plant species in the Mediterranean island of Cyprus, Crocus cyprius and Ophrys kotschyi, were used as a case study. We have coupled climate change scenarios, and land use change models with species distribution models. Future land use scenarios were modelled by initially calculating the rate of current land use changes between two time snapshots (2000 and 2006) on the island, and based on these transition probabilities markov-chain cellular automata were used to generate future land use changes for 2050. Climate change scenarios A1B, A2, B1 and B2A were derived from the IPCC reports. Species climatic preferences were derived from their current distributions using classification trees while habitats preferences were derived from the Red Data Book of the Flora of Cyprus. A bioclimatic model for Crocus cyprius was built using mean temperature of wettest quarter, max temperature of warmest month and precipitation seasonality, while for Ophrys kotchyi the bioclimatic model was built using precipitation of wettest month, mean temperature of warmest quarter, isothermality, precipitation of coldest quarter, and annual precipitation. Sequentially, simulation scenarios were performed regarding future species distributions by accounting climate alone and both climate and land use changes. The distribution of the two species resulting from the bioclimatic models was then filtered by future land use changes, providing the species projected potential distribution. The species projected potential distribution varies depending on the type and scenario used, but many of both species current sites/locations are projected to be outside their future potential distribution. Our results demonstrate the importance of including both land use and climatic changes in predictive species modeling., Comment: to appear (in press in Ecological Informatics (2015))

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle.

Allometry and growth rates of 8 forest species in the UK. The data were collected from two United Kingdom woodlands - Wytham Woods and Alice Holt. Here we present data from 582 individual trees of eight taxa in the form of summary variables. In addition the raw data files containing the variables from which the summary data were obtained. Large sample sizes with longitudinal data spanning 22 years make these datasets useful for future studies concerned with the way trees change in size and shape over their life-span. The allometric relationships include (1) trunk diameter, (2) height, (3) crown height, (4) crown radius and (5) trunk radial growth rate to (A) the light environment of each tree and (B) diameter at breast height., Comment: To appear (in press). Scientific Data 2015

Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose. We investigate the survival rates of ten tree species in a dataset designed to monitor growth rates. As some individuals were not included in the census at some time points we use capture-mark-recapture methods both to allow us to account for missing individuals, and to estimate relocation probabilities. Growth rates, size, and light availability were included as covariates in the model predicting survival rates. The study demonstrates that tree mortality is best described as constant between years and size-dependent at early life stages and size independent at later life stages for most species of UK hardwood. We have demonstrated that even with a twenty-year dataset it is possible to discern variability both between individuals and between species. Our work illustrates the potential utility of the method applied here for calculating plant population dynamics parameters in time replicated datasets with small sample sizes and missing individuals without any loss of sample size, and including explanatory covariates., Comment: To appear (pre-print, January 2015) in BMC Ecology (2015)

Fire is an indissoluble component of ecosystems, however quantifying the effects of fire on vegetation is challenging task as fire lies outside the typical experimental design attributes. A recent simulation study showed that under increased fire regimes positive tree-tree interactions were recorded (Bacelar et al., 2014). Data from experimental burning plots in an African savanna, the Kruger National Park, were collected across unburnt and annual burn plots. Indices of aggregation and spatial autocorrelation of the distribution of trees between different fire regimes were explored. Results show that the distribution of trees under fire were more clumped and exhibited higher spatial autocorrelation than in unburnt plots: In burnt plots spatial autocorrelation values were positive at finer scales and negative at coarser scales potentially indicating co-existence of facilitation and competition within the same ecosystem depending on the scale. The pattern derived here provides inference for (a) fire acting as an increasing aggregation & spatial autocorrelation force (b) tree survival under fire regimes is potentially facilitated by forming patches of trees (c) scale-dependent facilitation and competition coexisting within the same ecosystem with finer scale facilitation and coarser scale competition., Comment: To appear in Ecological Complexity (in press)