Evidence-based advice on timing and location of tsetse control measures in Shimba Hills National reserve, Kenya.

Controlling tsetse flies is critical for effective management of African trypanosomiasis in Sub-Saharan Africa. To enhance timely and targeted deployment of tsetse control strategies a better understanding of their temporal dynamics is paramount. A few empirical studies have explained and predicted tsetse numbers across space and time, but the resulting models may not easily scale to other areas.

Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling.

Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF).

Climate and land use changes will degrade the distribution of Rhododendrons in China.

Biodiversity loss and variation in species responses to climate and land use change have been found across broad taxonomic groups. However, whether species from the same taxonomic group with distinct geographical ranges will respond differently is poorly understood. The aim of this study is to predict the potential impacts of future climate and land use change on the distribution of narrow- and wide-ranging Rhododendron species, and estimate their relative contribution in China.

Ecology of West Nile virus across four European countries: empirical modelling of the Culex pipiens abundance dynamics as a function of weather.

Background: Culex pipiens is the major vector of West Nile virus in Europe, and is causing frequent outbreaks throughout the southern part of the continent. Proper empirical modelling of the population dynamics of this species can help in understanding West Nile virus epidemiology, optimizing vector surveillance and mosquito control efforts. But modelling results may differ from place to place.

Surgical Care Required for Populations Affected by Climate-related Natural Disasters: A Global Estimation.

Background: Climate extremes will increase the frequency and severity of natural disasters worldwide.  Climate-related natural disasters were anticipated to affect 375 million people in 2015, more than 50% greater than the yearly average in the previous decade. To inform surgical assistance preparedness, we estimated the number of surgical procedures needed.

Ecology of West Nile virus across four European countries: review of weather profiles, vector population dynamics and vector control response.

West Nile virus (WNV) represents a serious burden to human and animal health because of its capacity to cause unforeseen and large epidemics. Until 2004, only lineage 1 and 3 WNV strains had been found in Europe. Lineage 2 strains were initially isolated in 2004 (Hungary) and in 2008 (Austria) and for the first time caused a major WNV epidemic in 2010 in Greece with 262 clinical human cases and 35 fatalities.

The spatial distribution of injuries in need of surgical intervention in Nepal.

Geographic information system modelling can accurately represent the geospatial distribution of disease burdens to inform health service delivery. Given the dramatic topography of Nepal and a high prevalence of unmet surgical needs, we explored the consequences of topography on the prevalence of surgical conditions. The Nepalese Surgeons OverSeas Assessment of Surgical Need (SOSAS) is a validated, countrywide, cluster randomised survey that assesses surgical need in lowand middle-income countries; it was performed in Nepal in 2014.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities.

Optimal foraging for multiple resources in several food species.

The concentrations of resources in forage are not perfectly balanced to the needs of an animal, and food species differ in these concentrations. Under many circumstances, animals should thus forage on multiple food species to attain the maximum and most balanced intake of several resources. In this article we present a model to extend optimal foraging theory to incorporate concurrent foraging for multiple resources from several food species.