A spatially explicit impact assessment of road characteristics, road-induced fragmentation and noise on birds species in Cyprus

Abstract

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 × 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 fragmentationrelated 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.

Planning for fauna-sensitive road design: A review

“…The metric is highly versatile and has been modified by several authors to address a range of scenarios, such as: measuring road and noise impacts on birds populations (Cuervo and Moller, 2020;Konstantopoulos et al, 2020), landscape connectivity (Deslauriers et al, 2018;Spanowicz and Jaeger, 2019), the impact of conservation measures (De Montis et al, 2018), urban sprawl (Torres et al, 2016;Canedoli et al, 2018), and regional planning (Girvetz et al, 2008;Jaeger et al, 2008). Importantly, effective mesh-size accounts for within-patch connectivity and is, therefore, a more reliable indicator of connectivity compared to the previous IND2 CBI (Deslauriers et al, 2018).…”

Ranking the explanatory power of factors associated with worldwide new Covid-19 cases

“…Hierarchical Variance Partitioning (HVP) statistical modelling was implemented to account for the contribution of each data driven epidemiological, economic, public health, and governmental intervention explanatory variable to the total variance of new Covid-19 per million cases [29,30]. HVP is a statistical framework that is capable of handling correlated independent variables, whilst providing a reliable ranking of predictor importance of each variable [29].…”

“…Having selected the optimal index of testing, population density and age structures the analysis proceeded with the following variables: (1) population density, (2) new tests per thousand, (3) governmental stringency index, (4) percentage of the population aged > 65, (5) percentage of the population under extreme poverty, (6) cvd death rate, (7) diabetes prevalence, (8) percentage of smokers, (9) percentage of the population with access to hand washing facilities, and (10) hospital beds per 100k inhabitants within each country as independent variables. Hierarchical Variance Partitioning (HVP) statistical modelling was implemented to account for the contribution of each data driven epidemiological, economic, public health, and governmental intervention explanatory variable to the total variance of new Covid-19 per million cases [29,30]. HVP is a statistical framework that is capable of handling correlated independent variables, whilst providing a reliable ranking of predictor importance of each variable [29].…”