The effects of extreme heat on human health in tropical Africa

This review examines high-quality research evidence that synthesises the effects of extreme heat on human health in tropical Africa. Web of Science (WoS) was used to identify research articles on the effects extreme heat, humidity, Wet-bulb Globe Temperature (WBGT), apparent temperature, wind, Heat Index, Humidex, Universal Thermal Climate Index (UTCI), heatwave, high temperature and hot climate on human health, human comfort, heat stress, heat rashes, and heat-related morbidity and mortality. A total of 5, 735 articles were initially identified, which were reduced to 100 based on a set of inclusion and exclusion criteria. The review discovered that temperatures up to 60°C have been recorded in the region and that extreme heat has many adverse effects on human health, such as worsening mental health in low-income adults, increasing the likelihood of miscarriage, and adverse effects on well-being and safety, psychological behaviour, efficiency, and social comfort of outdoor workers who spend long hours performing manual labour. Extreme heat raises the risk of death from heat-related disease, necessitating preventative measures such as adaptation methods to mitigate the adverse effects on vulnerable populations during hot weather. This study highlights the social inequalities in heat exposure and adverse health outcomes.

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient

The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and associated mis-interpretations are often due to variations in prevalence and the use of an imperfect reference standard. The fundamental issues underlying the problems associated with variations in prevalence and reference standard quality are revisited here for binary classifications with particular attention focused on the use of the Matthews correlation coefficient (MCC). A key attribute claimed of the MCC is that a high value can only be attained when the classification performed well on both classes in a binary classification. However, it is shown here that the apparent magnitude of a set of popular accuracy metrics used in fields such as computer science medicine and environmental science (Recall, Precision, Specificity, Negative Predictive Value, J, F1, likelihood ratios and MCC) and one key attribute (prevalence) were all influenced greatly by variations in prevalence and use of an imperfect reference standard. Simulations using realistic values for data quality in applications such as remote sensing showed each metric varied over the range of possible prevalence and at differing levels of reference standard quality. The direction and magnitude of accuracy metric mis-estimation were a function of prevalence and the size and nature of the imperfections in the reference standard. It was evident that the apparent MCC could be substantially under- or over-estimated. Additionally, a high apparent MCC arose from an unquestionably poor classification. As with some other metrics of accuracy, the utility of the MCC may be overstated and apparent values need to be interpreted with caution. Apparent accuracy and prevalence values can be mis-leading and calls for the issues to be recognised and addressed should be heeded.

The Spectral Species Concept in Living Color

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Active restoration accelerates the carbon recovery of human-modified tropical forests

More than half of all tropical forests are degraded by human impacts, leaving them threatened with conversion to agricultural plantations and risking substantial biodiversity and carbon losses. Restoration could accelerate recovery of aboveground carbon density (ACD), but adoption of restoration is constrained by cost and uncertainties over effectiveness. We report a long-term comparison of ACD recovery rates between naturally regenerating and actively restored logged tropical forests. Restoration enhanced decadal ACD recovery by more than 50%, from 2.9 to 4.4 megagrams per hectare per year. This magnitude of response, coupled with modal values of restoration costs globally, would require higher carbon prices to justify investment in restoration. However, carbon prices required to fulfill the 2016 Paris climate agreement [$40 to $80 (USD) per tonne carbon dioxide equivalent] would provide an economic justification for tropical forest restoration.

Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

A virtual species set for robust and reproducible species distribution modelling tests

Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation. In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700,000 random observations at the extent of Europe. The dataset includes spatial attributes such as: distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide-spread species.

Linking remote sensing, land cover and disease

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Unmixing aggregate data: estimating the social composition of enumeration districts

"In this paper the authors address the problem of interpreting and classifying aggregate data sources and draw parallels between tasks commonly encountered in image processing and census analysis. Both of these fields already have a range of standard classification tools which are applied in such situations, but these are hindered by the aggregate nature of the input data. An approach to ¿unmixing' aggregate data, and thus revealing the nature of the subunit variation masked by aggregation, is introduced. This approach has already shown considerable success in Earth Observation applications, and in this paper the authors present the adaptation and application of the approach to Census small area statistics data for Southampton, Hants, [in England] revealing something of the social composition of Southampton's enumeration districts. The unmixing technique utilises an artificial neural network."