BackgroundAlthough Plasmodium falciparum transmission frequently exhibits seasonal patterns, the drivers of malaria seasonality are often unclear. Given the massive variation in the landscape upon which transmission acts, intra-annual fluctuations are likely influenced by different factors in different settings. Further, the presence of potentially substantial inter-annual variation can mask seasonal patterns; it may be that a location has “strongly seasonal” transmission and yet no single season ever matches the mean, or synoptic, curve. Accurate accounting of seasonality can inform efficient malaria control and treatment strategies. In spite of the demonstrable importance of accurately capturing the seasonality of malaria, data required to describe these patterns is not universally accessible and as such localized and regional efforts at quantifying malaria seasonality are disjointed and not easily generalized.Methods The purpose of this review was to audit the literature on seasonality of P. falciparum and quantitatively summarize the collective findings. Six search terms were selected to systematically compile a list of papers relevant to the seasonality of P. falciparum transmission, and a questionnaire was developed to catalogue the manuscripts.Results and discussion152 manuscripts were identified as relating to the seasonality of malaria transmission, deaths due to malaria or the population dynamics of mosquito vectors of malaria. Among these, there were 126 statistical analyses and 31 mechanistic analyses (some manuscripts did both).DiscussionIdentified relationships between temporal patterns in malaria and climatological drivers of malaria varied greatly across the globe, with different drivers appearing important in different locations. Although commonly studied drivers of malaria such as temperature and rainfall were often found to significantly influence transmission, the lags between a weather event and a resulting change in malaria transmission also varied greatly by location.ConclusionsThe contradicting results of studies using similar data and modelling approaches from similar locations as well as the confounding nature of climatological covariates underlines the importance of a multi-faceted modelling approach that attempts to capture seasonal patterns at both small and large spatial scales.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-015-0849-2) contains supplementary material, which is available to authorized users.

Wind farms can have two broad potential adverse effects on birds via antagonistic processes: displacement from the vicinity of turbines (avoidance), or death through collision with rotating turbine blades. These effects may not be mutually exclusive. Using detailed data from 99 turbines at two wind farms in central Scotland and thousands of GPS-telemetry data from dispersing golden eagles, we tested three hypotheses. Before-and-after-operation analyses supported the hypothesis of avoidance: displacement was reduced at turbine locations in more preferred habitat and with more preferred habitat nearby. After-operation analyses (i.e. from the period when turbines were operational) showed that at higher wind speeds and in highly preferred habitat eagles were less wary of turbines with motionless blades: rejecting our second hypothesis. Our third hypothesis was supported, since at higher wind speeds eagles flew closer to operational turbines; especially–once more–turbines in more preferred habitat. After operation, eagles effectively abandoned inner turbine locations, and flight line records close to rotor blades were rare. While our study indicated that whole-wind farm functional habitat loss through avoidance was the substantial adverse impact, we make recommendations on future wind farm design to minimise collision risk further. These largely entail developers avoiding outer turbine locations which are in and surrounded by swathes of preferred habitat. Our study illustrates the insights which detailed case studies of large raptors at wind farms can bring and emphasises that the balance between avoidance and collision can have several influences.

Wind farms may have two broad potential adverse effects on birds via antagonistic processes: displacement from the vicinity of turbines (avoidance), or death through collision with rotating turbine blades. Large raptors are often shown or presumed to be vulnerable to collision and are demographically sensitive to additional mortality, as exemplified by several studies of the Golden Eagle Aquila chrysaetos. Previous findings from Scottish Eagles, however, have suggested avoidance as the primary response. Our study used data from 59 GPS-tagged Golden Eagles with 28 284 records during natal dispersal before and after turbine operation < 1 km of 569 turbines at 80 wind farms across Scotland. We tested three hypotheses using measurements of tag records' distance from the hub of turbine locations: (1) avoidance should be evident; (2) older birds should show less avoidance (i.e. habituate to turbines); and (3) rotor diameter should have no influence (smaller diameters are correlated with a turbine's age, in examining possible habituation). Four generalized linear mixed models (GLMMs) were constructed with intrinsic habitat preference of a turbine location using Golden Eagle Topography (GET) model, turbine operation status (before/after), bird age and rotor diameter as fixed factors. The best GLMM was subsequently verified by k-fold cross-validation and involved only GET habitat preference and presence of an operational turbine. Eagles were eight times less likely to be within a rotor diameter's distance of a hub location after turbine operation, and modelled displacement distance was 70 m. Our first hypothesis expecting avoidance was supported. Eagles were closer to turbine locations in preferred habitat but at greater distances after turbine operation. Results on bird age (no influence to 5+ years) rejected hypothesis 2, implying no habituation. Support for hypothesis 3 (no influence of rotor diameter) also tentatively inferred no habituation, but data indicated birds went slightly closer to longer rotor blades although not to the turbine tower. We proffer that understanding why avoidance or collision in large raptors may occur can be conceptually envisaged via variation in fear of humans as the 'super predator' with turbines as cues to this life-threatening agent.

Bioacoustic recording is used to generate occupancy and detectability estimates • Rare heathland breeding birds varied in their occupancy between 0.68 and 0.13 • Detectability varied from 0.74 to 0.20, and was affected by habitat • Bioacoustics can be used to provide improved data over traditional survey methods ABSTRACT Effective monitoring of rare and declining species is critical to enable their conservation, but can often be difficult due to detectability or survey constraints. However, developments in acoustic recorders are enabling an important new approach for improved monitoring that is especially applicable for long-term studies, and for use in difficult environments or with cryptic species. Bioacoustic data may be effectively analysed within an occupancy modelling framework, as presence/absence can be determined, and repeated survey events can be accommodated. Hence, both occupancy and detectability estimates can be produced from large, coherent datasets. However, the most effective methods for the practical detection and identification of call data are still far from established. We assessed a novel combination of automated clustering and manual verification to detect and identify heathland bird vocalizations, covering a period of six days at 44 sampling locations Occupancy (Ψ) and detectability (p) were modelled for each species, and the best fit models provided values of: nightjar Ψ=0.684, p=0.740, Dartford warbler Ψ=0.449 p=0.196 and woodlark Ψ=0.13 p=0.996. Including environmental covariates within the occupancy models indicated that tree, wetland and heather cover were important variables, particularly influencing detectability. The protocol used here allowed robust and consistent survey data to be gathered, with limited fieldwork resourcing, allowing population estimates to be generated for the target bird species. The combination of

With increasing pressures on land for human use, it is important to identify the habitat requirements of key species, not just in terms of a correlation with a given habitat feature, but also the relationship between species presence and its coverage, proximity to other habitat types, and importance at different spatial scales. We used maximum entropy to estimate the optimal proportions of 18 habitat types, plus elevation and habitat richness associated with the presence of leks of Black Grouse Tetrao tetrix within an 800-km 2 study area in Perthshire, Scotland. We repeated the analysis at several radii (0.2-3 km) to assess how the importance of different habitats changed with proximity to lek and scale. We then examined habitat features or combinations of features that were associated with large leks or positive lek growth. Models at all radii had satisfactory predictive power. Using response curves from MAXENT, we constructed ideal habitat mixes for leks at each radius. At the 2-km radius, suitability was highest with around 20% each of three moorland types and open/mixed forestry, whereas close to leks (0.2 km), higher proportions of grouse moor and lower proportions of closed-canopy woodland were optimal. The relationship between habitat and lek size or direction of lek growth was complex, indicating that a landscape containing large or productive leks can be the result of more than one combination of habitats. This demonstrates a degree of flexibility in designing landscapes for Black Grouse conservation, so landowners can prioritize combinations of habitats that are the most practical and/ or economical, while still serving the requirements of the target species.