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Climate change increases cross-species viral transmission risk
At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals
1
,
2
. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife
3
,
4
. In some cases, this will facilitate zoonotic spillover—a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal–virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.
Changes in climate and land use will lead to species aggregating in new combinations at high elevations, in biodiversity hotspots and in areas of high human population density in Asia and Africa, driving the cross-species transmission of animal-associated viruses.
Journal Article
Large carnivore expansion in Europe is associated with human population density and land cover changes
Cimatti, M., Ranc, N., Benítez-López, A., Maiorano, L., Boitani, L., Cagnacci, F., Čengić, M., Ciucci, P., Huijbregts, M.A.J., Krofel, M., López-Bao, J.V., Selva, N., Andren, H., Bautista, C., Ćirović, D., Hemmingmoore, H., Reinhardt, I., Marenče, M., Mertzanis, Y., Pedrotti, L., Trbojević, I., Zetterberg, A., Zwijacz-Kozica, T., Santini, L.
Journal Article
Human activities have opposing effects on distributions of narrow-ranged and widespread plant species in China
Human activities have shaped large-scale distributions of many species, driving both range contractions and expansions. Species differ naturally in range size, with small-range species concentrated in particular geographic areas and potentially deviating ecologically from widespread species. Hence, species’ responses to human activities may be influenced by their geographic range sizes, but if and how this happens are poorly understood. Here, we use a comprehensive distribution database and species distribution modeling to examine if and how human activities have affected the extent to which 9,701 vascular plants fill their climatic potential ranges in China. We find that narrow-ranged species have lower range filling and widespread species have higher range filling in the human-dominated southeastern part of China, compared with their counterparts distributed in the less human-influenced northwestern part. Variations in range filling across species and space are strongly associated with indicators of human activities (human population density, human footprint, and proportion of cropland) even after controlling for alternative drivers. Importantly, narrow-ranged and widespread species show negative and positive range-filling relationships to these human indicators, respectively. Our results illustrate that floras risk biotic homogenization as a consequence of anthropogenic activities, with narrow-ranged species becoming replaced by widespread species. Because narrow-ranged species are more numerous than widespread species in nature, negative impacts of human activities will be prevalent. Our findings highlight the importance of establishing more protected areas and zones of reduced human activities to safeguard the rich flora of China.
Journal Article
Target-group backgrounds prove effective at correcting sampling bias in Maxent models
Aim Accounting for sampling bias is the greatest challenge facing presence‐only and presence‐background species distribution models; no matter what type of model is chosen, using biased data will mask the true relationship between occurrences and environmental predictors. To address this issue, we review four established bias correction techniques, using empirical occurrences with known sampling effort, and virtual species with known distributions. Innovation Occurrence data come from a national recording scheme of hoverflies (Syrphidae) in Great Britain, spanning 1983–2002. Target‐group backgrounds, distance‐restricted backgrounds, travel time to cities and human population density were used to account for sampling bias in 58 species of hoverfly. Distributions generated by bias correction techniques were compared in geographical space to the distribution produced accounting for known sampling effort, using Schoener's distance, centroid shifts and range size changes. To validate our results, we performed the same comparisons using 50 randomly generated virtual species. We used sampling effort from the hoverfly recording scheme to structure our biased sampling regime, emulating complex real‐life sampling bias. Main conclusions Models made without any correction typically produced distributions that mapped sampling effort rather than the underlying habitat suitability. Target‐group backgrounds performed the best at emulating sampling effort and unbiased virtual occurrences, but also showed signs of overcompensation in places. Other methods performed better than no‐correction, but often differences were difficult to visually detect. In line with previous studies, when sampling effort is unknown, target‐group backgrounds provide a useful tool for reducing the effect of sampling bias. Models should be visually inspected for biological realism to identify any areas of potential overcompensation. Given the disparity between corrected and un‐corrected models, sampling bias constitutes a major source of error in species distribution modelling, and more research is needed to confidently address the issue.
Journal Article
Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans
Iñaki Comas and colleagues report whole-genome sequencing and analysis of 259
Mycobacterium tuberculosis
complex strains, providing a survey of global diversity and facilitating evolutionary analyses. Their phylogeographic analysis suggests the emergence of
M. tuberculosis
complex strains about 70,000 years ago in Africa, with expansion correlated with increased human population density during the Neolithic Demographic Transition.
Tuberculosis caused 20% of all human deaths in the Western world between the seventeenth and nineteenth centuries and remains a cause of high mortality in developing countries. In analogy to other crowd diseases, the origin of human tuberculosis has been associated with the Neolithic Demographic Transition, but recent studies point to a much earlier origin. We analyzed the whole genomes of 259
M. tuberculosis
complex (MTBC) strains and used this data set to characterize global diversity and to reconstruct the evolutionary history of this pathogen. Coalescent analyses indicate that MTBC emerged about 70,000 years ago, accompanied migrations of anatomically modern humans out of Africa and expanded as a consequence of increases in human population density during the Neolithic period. This long coevolutionary history is consistent with MTBC displaying characteristics indicative of adaptation to both low and high host densities.
Journal Article
Biological invasions facilitate zoonotic disease emergences
Outbreaks of zoonotic diseases are accelerating at an unprecedented rate in the current era of globalization, with substantial impacts on the global economy, public health, and sustainability. Alien species invasions have been hypothesized to be important to zoonotic diseases by introducing both existing and novel pathogens to invaded ranges. However, few studies have evaluated the generality of alien species facilitating zoonoses across multiple host and parasite taxa worldwide. Here, we simultaneously quantify the role of 795 established alien hosts on the 10,473 zoonosis events across the globe since the 14
th
century. We observe an average of ~5.9 zoonoses per alien zoonotic host. After accounting for species-, disease-, and geographic-level sampling biases, spatial autocorrelation, and the lack of independence of zoonosis events, we find that the number of zoonosis events increase with the richness of alien zoonotic hosts, both across space and through time. We also detect positive associations between the number of zoonosis events per unit space and climate change, land-use change, biodiversity loss, human population density, and PubMed citations. These findings suggest that alien host introductions have likely contributed to zoonosis emergences throughout recent history and that minimizing future zoonotic host species introductions could have global health benefits.
Alien species invasions are thought to be important to zoonotic diseases through the introduction of both existing and novel pathogens to invaded ranges. Using data from 795 established alien animals and 10,473 zoonosis events worldwide, this study examines the role of alien zoonotic hosts on zoonosis emergences after accounting for climate, propagule pressure, global change and sampling bias.
Journal Article
Regime shifts, trends, and variability of lake productivity at a global scale
Lakes are often described as sentinels of global change. Phenomena like lake eutrophication, algal blooms, or reorganization in community composition belong to the most studied ecosystem regime shifts. However, although regime shifts have been well documented in several lakes, a global assessment of the prevalence of regime shifts is still missing, and, more in general, of the factors altering stability in lake status, is missing. Here, we provide a first global assessment of regime shifts and stability in the productivity of 1,015 lakes worldwide using trophic state index (TSI) time series derived from satellite imagery. We find that 12.8% of the lakes studied show regime shifts whose signatures are compatible with tipping points, while the number of detected regime shifts from low to high TSI has increased over time. Although our results suggest an overall stable picture for global lake dynamics, the limited instability signatures do not mean that lakes are insensitive to global change. Modeling the interaction between lake climatic, geophysical, and socioeconomic features and their stability properties, we find that the probability of a lake experiencing a tipping point increases with human population density in its catchment, while it decreases as the gross domestic product of that population increases. Our results show how quantifying lake productivity dynamics at a global scale highlights socioeconomic inequalities in conserving natural environments.
Journal Article
The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit—A systematic review
This systematic review aims to assess how different urbanisation patterns related to rapid urban growth, unplanned expansion, and human population density affect the establishment and distribution of Aedes aegypti and Aedes albopictus and create favourable conditions for the spread of dengue, chikungunya, and Zika viruses.
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted using the PubMed, Virtual Health Library, Cochrane, WHO Library Database (WHOLIS), Google Scholar, and and the Institutional Repository for Information Sharing (IRIS) databases. From a total of 523 identified studies, 86 were selected for further analysis, and 29 were finally analysed after applying all inclusion and exclusion criteria. The main explanatory variables used to associate urbanisation with epidemiological/entomological outcomes were the following: human population density, urban growth, artificial geographical space, urban construction, and urban density. Associated with the lack of a global definition of urbanisation, several studies provided their own definitions, which represents one of the study's limitations. Results were based on 8 ecological studies/models, 8 entomological surveillance studies, 7 epidemiological surveillance studies, and 6 studies consisting of spatial and predictive models. According to their focus, studies were categorised into 2 main subgroups, namely \"Aedes ecology\" and \"transmission dynamics.\" There was a consistent association between urbanisation and the distribution and density of Aedes mosquitoes in 14 of the studies and a strong relationship between vector abundance and disease transmission in 18 studies. Human population density of more than 1,000 inhabitants per square kilometer was associated with increased levels of arboviral diseases in 15 of the studies.
The use of different methods in the included studies highlights the interplay of multiple factors linking urbanisation with ecological, entomological, and epidemiological parameters and the need to consider a variety of these factors for designing effective public health approaches.
Journal Article
A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records
Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN). In this experiment, the highest spatial resolution of every grid cell is 125125 square metre, while the temporal resolution can vary from minutes to hours with varying accuracy. To demonstrate our method, we present an application of how to map the estimated population density distribution dynamically for CDR big data from Beijing, choosing a half hour as the temporal resolution. Finally, in order to cross-check previous studies that claim the population distribution at nighttime (from 8 p.m. to 8 a.m. on the next day) mapped by Beijing census data are similar to the ground truth data, we estimated the baseline distribution, first, based upon records in CDRs. Second, we estimate a baseline distribution based upon Global Navigation Satellite System (GNSS) data. The results also show the Root Mean Square Error (RMSE) is about 5000 while the two baseline distributions mentioned above have an RMSE of over 13,500. Our estimation method provides a fast and simple process to map people’s actual density distributions at a more finely grained, i.e., hourly, temporal resolution.
Journal Article