Differing taxonomic responses of mosquito vectors to anthropogenic land-use change in Latin America and the Caribbean

Anthropogenic land-use change, such as deforestation and urban development, can affect the emergence and re-emergence of mosquito-borne diseases, e.g., dengue and malaria, by creating more favourable vector habitats. There has been a limited assessment of how mosquito vectors respond to land-use changes, including differential species responses, and the dynamic nature of these responses. Improved understanding could help design effective disease control strategies. We compiled an extensive dataset of 10,244 Aedes and Anopheles mosquito abundance records across multiple land-use types at 632 sites in Latin America and the Caribbean. Using a Bayesian mixed effects modelling framework to account for between-study differences, we compared spatial differences in the abundance and species richness of mosquitoes across multiple land-use types, including agricultural and urban areas. Overall, we found that mosquito responses to anthropogenic land-use change were highly inconsistent, with pronounced responses observed at the genus- and species levels. There were strong declines in Aedes (-26%) and Anopheles (-35%) species richness in urban areas, however certain species such as Aedes aegypti, thrived in response to anthropogenic disturbance. When abundance records were coupled with remotely sensed forest loss data, we detected a strong positive response of dominant and secondary malaria vectors to recent deforestation. This highlights the importance of the temporal dynamics of land-use change in driving disease risk and the value of large synthetic datasets for understanding changing disease risk with environmental change.

Estimating motor unit numbers from a CMAP scan: Repeatability study on three muscles at 15 centres

OBJECTIVE

To assess the repeatability and suitability for multicentre studies of MScanFit motor unit number estimation (MUNE), which involves modelling compound muscle action potential (CMAP) scans.

METHODS

Fifteen groups in 9 countries recorded CMAP scans twice, 1-2 weeks apart in healthy subjects from abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) muscles. The original MScanFit program (MScanFit-1) was compared with a revised version (MScanFit-2), designed to accommodate different muscles and recording conditions by setting the minimal motor unit size as a function of maximum CMAP.

RESULTS

Complete sets of 6 recordings were obtained from 148 subjects. CMAP amplitudes differed significantly between centres for all muscles, and the same was true for MScanFit-1 MUNE. With MScanFit-2, MUNE differed less between centres but remained significantly different for APB. Coefficients of variation between repeats were 18.0% for ADM, 16.8% for APB, and 12.1% for TA.

CONCLUSIONS

It is recommended for multicentre studies to use MScanFit-2 for analysis. TA provided the least variable MUNE values between subjects and the most repeatable within subjects.

SIGNIFICANCE

MScanFit was primarily devised to model the discontinuities in CMAP scans in patients and is less suitable for healthy subjects with smooth scans.

Rodent trapping studies as an overlooked information source for understanding endemic and novel zoonotic spillover

Rodents, a diverse, globally distributed and ecologically important order of mammals are nevertheless important reservoirs of known and novel zoonotic pathogens. Ongoing anthropogenic land use change is altering these species' abundance and distribution, which among zoonotic host species may increase the risk of zoonoses spillover events. A better understanding of the current distribution of rodent species is required to guide attempts to mitigate against potentially increased zoonotic disease hazard and risk. However, available species distribution and host-pathogen association datasets (e.g. IUCN, GBIF, CLOVER) are often taxonomically and spatially biased. Here, we synthesise data from West Africa from 127 rodent trapping studies, published between 1964-2022, as an additional source of information to characterise the range and presence of rodent species and identify the subgroup of species that are potential or known pathogen hosts. We identify that these rodent trapping studies, although biased towards human dominated landscapes across West Africa, can usefully complement current rodent species distribution datasets and we calculate the discrepancies between these datasets. For five regionally important zoonotic pathogens (Arenaviridae spp., Borrelia spp., Lassa mammarenavirus, Leptospira spp. and Toxoplasma gondii), we identify host-pathogen associations that have not been previously reported in host-association datasets. Finally, for these five pathogen groups, we find that the proportion of a rodent hosts range that have been sampled remains small with geographic clustering. A priority should be to sample rodent hosts across a greater geographic range to better characterise current and future risk of zoonotic spillover events. In the interim, studies of spatial pathogen risk informed by rodent distributions must incorporate a measure of the current sampling biases. The current synthesis of contextually rich rodent trapping data enriches available information from IUCN, GBIF and CLOVER which can support a more complete understanding of the hazard of zoonotic spillover events.

Widespread exposure to Crimean-Congo haemorrhagic fever in Uganda might be driven by transmission from Rhipicephalus ticks: Evidence from cross-sectional and modelling studies

BACKGROUND

Crimean-Congo haemorrhagic fever (CCHF) is a widespread tick-borne viral infection, present across Africa and Eurasia, which might pose a cryptic public health problem in Uganda. We aimed to understand the magnitude and distribution of CCHF risk in humans, livestock and ticks across Uganda by synthesising epidemiological (cross-sectional) and ecological (modelling) studies.

METHODS

We conducted a cross-sectional study at three urban abattoirs receiving cattle from across Uganda. We sampled humans (n = 478), livestock (n = 419) and ticks (n = 1065) and used commercially-available kits to detect human and livestock CCHF virus (CCHFV) antibodies and antigen in tick pools. We developed boosted regression tree models to evaluate the correlates and geographical distribution of expected tick and wildlife hosts, and of human CCHF exposures, drawing on continent-wide data.

FINDINGS

The cross-sectional study found CCHFV IgG/IgM seroprevalence in humans of 10·3% (7·8-13·3), with antibody detection positively associated with reported history of tick bite (age-adjusted odds ratio = 2·09 (1·09-3·98)). Cattle had a seroprevalence of 69·7% (65·1-73·4). Only one Hyalomma tick (CCHFV-negative) was found. However, CCHFV antigen was detected in Rhipicephalus (5·9% of 304 pools) and Amblyomma (2·9% of 34 pools) species. Modelling predicted high human CCHF risk across much of Uganda, low environmental suitability for Hyalomma, and high suitability for Rhipicephalus and Amblyomma.

INTERPRETATION

Our epidemiological and ecological studies provide complementary evidence that CCHF exposure risk is widespread across Uganda. We challenge the idea that Hyalomma ticks are consistently the principal reservoir and vector for CCHFV, and postulate that Rhipicephalus might be important for CCHFV transmission in Uganda, due to high frequency of infected ticks and predicted environmental suitability.

FUNDING

UCL Global Challenges Research Fund (GCRF) and Pan-African Network on Emerging and Re-Emerging Infections (PANDORA-ID-NET) funded by the European and Developing Countries Clinical Trials Partnership (EDCTP) under the EU Horizon 2020 Framework Programme for Research and Innovation.

Synergies between environmental degradation and climate variation on malaria re-emergence in southern Venezuela: a spatiotemporal modelling study

BACKGROUND

Environmental degradation facilitates the emergence of vector-borne diseases, such as malaria, through changes in the ecological landscape that increase human-vector contacts and that expand vector habitats. However, the modifying effects of environmental degradation on climate-disease relationships have not been well explored. Here, we investigate the rapid re-emergence of malaria in a transmission hotspot in southern Venezuela and explore the synergistic effects of environmental degradation, specifically gold-mining activity, and climate variation.

METHODS

In this spatiotemporal modelling study of the 46 parishes of the state of Bolívar, southeast Venezuela, we used data from the Venezuelan Ministry of Health including population data and monthly cases of Plasmodium falciparum malaria and Plasmodium vivax malaria between 1996 and 2016. We estimated mean precipitation and temperature using the ERA5-Land dataset and used monthly anomalies in sea-surface temperature as an indicator of El Niño events between 1996 and 2016. The location of suspected mining sites in Bolívar in 2009, 2017, and 2018 were sourced from the Amazon Geo-Referenced Socio-Environmental Information Network. We estimated measures of cumulative forest loss and urban development by km2 using annual land cover maps from the European Space Agency Climate Change Initiative between 1996 and 2016. We modelled monthly cases of P falciparum and P vivax malaria using a Bayesian hierarchical mixed model framework. We quantified the variation explained by mining activity before exploring the modifying effects of environmental degradation on climate-malaria relationships.

FINDINGS

We observed a 27% reduction in the additional unexplained spatial variation in incidence of P falciparum malaria and a 23% reduction in P vivax malaria when mining was included in our models. The effect of temperature on malaria was greater in high mining areas than low mining areas, and the P falciparum malaria effect size at temperatures of 26·5°C (2·4 cases per 1000 people [95% CI 1·78-3·06]) was twice as high as the effect in low mining areas (1 case per 1000 people [0·68-1·49]).

INTERPRETATION

We show that mining activity in southern Venezuela is associated with hotspots of malaria transmission. Increased temperatures exacerbated malaria transmission in mining areas, highlighting the need to consider how environmental degradation modulates climate effect on disease risk, which is especially important in areas subjected to rapidly rising temperatures and land-use change globally. Our findings have implications for the progress towards malaria elimination in the Latin American region. Our findings are also important for effectively targeting timely treatment programmes and vector-control activities in mining areas with high rates of malaria transmission.

FUNDING

Biotechnology and Biological Sciences Research Council, Royal Society, US National Institutes of Health, and Global Challenges Research Fund.

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India

Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52–4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance–a key component of JE hazard–over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts.

Japanese encephalitis (JE) is the leading cause of viral encephalopathy in Asia with an estimated 100,000 annual cases and 25,000 deaths. However, insufficient data on the predominant mosquito vector Culex tritaeniorhynchus–a key component of JE hazard–precludes hazard estimation required to target public health interventions. Previous studies have provided limited estimates of JE hazard, often predicting geographic distributions of potential vector occurrence without accounting for vector abundance, seasonality, or uncertainty in predictions. This study details a novel approach to predict spatiotemporal patterns in JE vector abundance using a joint-likelihood modelling technique that leverages information from sparse vector surveillance data. We showed that patterns in JE vector abundance were driven by seasonality and environmental factors and so demonstrated the limitations of previously available static vector distribution maps in estimating the vector population component of JE hazard. One-month lagged vector abundance predictions showed a positive relationship with JE outbreaks, signalling the potential use of vector abundance as a proxy for JE hazard. While vector surveillance data are limited, joint-likelihood models offer a useful approach to inform vector abundance predictions. This study provides decision-makers with a more complete picture of the distribution of JE vector abundance and can be used to target vector surveillance and control efforts and enhance the allocation of resources.

MAMMALS IN PORTUGAL: A data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ca. 26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated to habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished geo-referenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of Azores and Madeira that includes 107,852 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (42%), sign surveys (38%), camera trapping (16%), bioacoustics surveys (4%) and radio-tracking and inquiries that represent less than 1% of the records. The data set includes 13 types of records: 1) burrows | soil mounds | tunnel, 2) capture, 3) colony, 4) dead animal | hair | skulls | jaws, 5) genetic confirmation, 6) inquiries, 7) observation of live animal, 8), observation in shelters, 9) photo trapping | video, 10), predators diet | pellets | pine cones/nuts, 11) scat | track | ditch, 12) telemetry and 13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n = 34,754) has the highest number of records followed by Chiroptera (n = 18,858), Carnivora (n = 18,594), Lagomorpha (n = 17,679), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 6400). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus (n = 12,407), Monachus monachus (n = 1512), and Lynx pardinus (n = 197)]. We believe that this data set may stimulate the publication of other European countries data sets which would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications.

AutoBend: An Automated Approach for Estimating Intervertebral Joint Function from Bone-Only Digital Models

Deciphering the biological function of rare or extinct species is key to understanding evolutionary patterns across the tree of life. While soft tissues are vital determinants of joint function, they are rarely available for study. Therefore, extracting functional signals from skeletons, which are more widely available via museum collections, has become a priority for the field of comparative biomechanics. While most work has focused on the limb skeleton, the axial skeleton plays a critical role in body support, respiration, and locomotion, and is therefore of central importance for understanding broad-scale functional evolution. Here, we describe and experimentally validate AutoBend, an automated approach to estimating intervertebral joint function from bony vertebral columns. AutoBend calculates osteological range of motion (oROM) by automatically manipulating digitally articulated vertebrae while incorporating multiple constraints on motion, including both bony intersection and the role of soft tissues by restricting excessive strain in both centrum and zygapophyseal articulations. Using AutoBend and biomechanical data from cadaveric experiments on cats and tegus, we validate important modeling parameters required for oROM estimation, including the degree of zygapophyseal disarticulation, and the location of the center of rotation. Based on our validation, we apply a model with the center of rotation located within the vertebral disk, no joint translation, around 50% strain permitted in both zygapophyses and disks, and a small amount of vertebral intersection permitted. Our approach successfully reconstructs magnitudes and craniocaudal patterns of motion obtained from ex vivo experiments, supporting its potential utility. It also performs better than more typical methods that rely solely on bony intersection, emphasizing the importance of accounting for soft tissues. We estimated the sensitivity of the analyses to vertebral model construction by varying joint spacing, degree of overlap, and the impact of landmark placement. The effect of these factors was small relative to biological variation craniocaudally and between bending directions. We also present a new approach for estimating joint stiffness directly from oROM and morphometric measurements that can successfully reconstruct the craniocaudal patterns, but not magnitudes, derived from experimental data. Together, this work represents a significant step forward for understanding vertebral function in difficult-to-study (e.g., rare or extinct) species, paving the way for a broader understanding of patterns of functional evolution in the axial skeleton.

A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil.

COVID-Clarity demands unification of health and environmental policy

Spillover of novel pathogens from wildlife to people, such as the virus responsible for the COVID-19 pandemic, is increasing and this trend is most strongly associated with tropical deforestation driven by agricultural expansion. This same process is eroding natural capital, reducing forest-associated health co-benefits, and accelerating climate change. Protecting and promoting tropical forests is one of the most immediate steps we can take to simultaneously mitigate climate change while reducing the risk of future pandemics; however, success in this undertaking will require greater connectivity of policy initiatives from local to global, as well as unification of health and environmental policy.

The species awareness index as a conservation culturomics metric for public biodiversity awareness

Although threats to global biodiversity are well known, slowing current rates of biodiversity loss remains a challenge. The Aichi targets set out 20 goals on which the international community should act to alleviate biodiversity decline, 1 of which (Target 1) aims to raise public awareness of the importance of biodiversity. Although conventional indicators for Target 1 are of low spatial and temporal coverage, conservation culturomics metrics show how biodiversity awareness can be quantified at the global scale. Following methods used for the Living Planet Index, we devised a species awareness index (SAI) to measure change in species awareness based on Wikipedia views. We calculated this index at the page level for 41,197 species listed by the International Union for Conservation of Nature (IUCN) across 10 Wikipedia languages and >2 billion views from 1 July 2015 to 30 March 2020. Bootstrapped indices for the page-level SAI showed that overall awareness of biodiversity increased marginally over time, although there were differences among taxonomic classes and languages. Among taxonomic classes, overall awareness increased fastest for reptiles and slowest for amphibians. Among languages, overall species awareness increased fastest for Japanese and slowest for Chinese and German users. Although awareness of species as a whole increased and was significantly higher for traded species, from January 2016 through January 2020, change in awareness appeared not to be strongly related to whether the species is traded or is a pollinator. As a data source for public biodiversity awareness, the SAI could be integrated into the Conservation International Biodiversity Engagement Indicator.

Human responses to nature- and culture-based non-clinical interventions: a systematised review

AIMS

A wide range of non-clinical nature- and culture-based interventions for the treatment of health issues have been evaluated in evidence and systematic reviews. However, common outcomes of these interventions have not been identified and neuro-bio-psychosocial mechanisms underlying how these interventions impact health are not well understood. We conducted a systematised review and compared the evidence for human responses to nature- and culture-based non-clinical interventions for a range of health issues and assessed the proposed mechanisms and conceptual frameworks underlying these interventions.

METHODS

Comprehensive searches were conducted up to May 2018 in six bibliographic databases: Campbell Collaboration, Cochrane Library, Embase, Medline, Scopus and Web of Science. Studies included were evidence reviews or systematic reviews on any nature- or culture-based non-clinical intervention to improve the health of individuals.

RESULTS

A total of 60 reviews were included (33 of nature, 26 of culture, 1 of both) covering 1480 individual studies and trials. The most common review types were systematic (32), literature (22) and meta-analyses (6). Positive effects on mental health were reported for the majority of interventions, while other health outcomes such as immunity were not well represented in the review literature. A range of secondary outcomes were common to both nature- and culture-based interventions including psychological and emotional impacts, social interaction and relationship development, skills development, physical health benefits, and positive impact of the intervention environment. Only two reviews proposed conceptual frameworks, and the neuro-bio-psychosocial mechanisms that underpin the health changes were not clarified.

CONCLUSION

Future research should focus on reviewing the evidence gaps for non-clinical nature- and culture-based interventions with an emphasis on implementing larger sample sizes, cohort and longitudinal studies, which deploy a wider range of mixed-methods, quasi-experimental and randomised control trials. There should also be agreement on terminology and developing conceptual frameworks to better understand the neuro-bio-psychosocial mechanisms underlying interventions.

Georgina Mace (1953–2020)

Pioneering conservation biologist and sustainability scientist

Spatial and taxonomic biases in bat records: Drivers and conservation implications in a megadiverse country

Biases in data availability have serious consequences on scientific inferences that can be derived. The potential consequences of these biases could be more detrimental in the less-studied megadiverse regions, often characterized by high biodiversity and serious risks of human threats, as conservation and management actions could be misdirected. Here, focusing on 134 bat species in Mexico, we analyze spatial and taxonomic biases and their drivers in occurrence data; and identify priority areas for further data collection which are currently under-sampled or at future environmental risk. We collated a comprehensive database of 26,192 presence-only bat records in Mexico to characterize taxonomic and spatial biases and relate them to species' characteristics (range size and foraging behavior). Next, we examined variables related to accessibility, species richness and security to explain the spatial patterns in occurrence records. Finally, we compared the spatial distributions of existing data and future threats to these species to highlight those regions that are likely to experience an increased level of threats but are currently under-surveyed. We found taxonomic biases, where species with wider geographical ranges and narrow-space foragers (species easily captured with traditional methods), had more occurrence data. There was a significant oversampling toward tropical regions, and the presence and number of records was positively associated with areas of high topographic heterogeneity, road density, urban, and protected areas, and negatively associated with areas which were predicted to have future increases in temperature and precipitation. Sampling efforts for Mexican bats appear to have focused disproportionately on easily captured species, tropical regions, areas of high species richness and security; leading to under-sampling in areas of high future threats. These biases could substantially influence the assessment of current status of, and future anthropogenic impacts on, this diverse species group in a tropical megadiverse country.

Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa

Recent outbreaks of animal-borne emerging infectious diseases have likely been precipitated by a complex interplay of changing ecological, epidemiological and socio-economic factors. Here, we develop modelling methods that capture elements of each of these factors, to predict the risk of Ebola virus disease (EVD) across time and space. Our modelling results match previously-observed outbreak patterns with high accuracy, and suggest further outbreaks could occur across most of West and Central Africa. Trends in the underlying drivers of EVD risk suggest a 1.75 to 3.2-fold increase in the endemic rate of animal-human viral spill-overs in Africa by 2070, given current modes of healthcare intervention. Future global change scenarios with higher human population growth and lower rates of socio-economic development yield a fourfold higher likelihood of epidemics occurring as a result of spill-over events. Our modelling framework can be used to target interventions designed to reduce epidemic risk for many zoonotic diseases.

The effect of global change on mosquito-borne disease

More than 80% of the global population is at risk of a vector-borne disease, with mosquito-borne diseases being the largest contributor to human vector-borne disease burden. Although many global processes, such as land-use and socioeconomic change, are thought to affect mosquito-borne disease dynamics, research to date has strongly focused on the role of climate change. Here, we show, through a review of contemporary modelling studies, that no consensus on how future changes in climatic conditions will impact mosquito-borne diseases exists, possibly due to interacting effects of other global change processes, which are often excluded from analyses. We conclude that research should not focus solely on the role of climate change but instead consider growing evidence for additional factors that modulate disease risk. Furthermore, future research should adopt new technologies, including developments in remote sensing and system dynamics modelling techniques, to enable a better understanding and mitigation of mosquito-borne diseases in a changing world.

Engaging research with policy and action: what are the challenges of responding to zoonotic disease in Africa?

Zoonotic diseases will maintain a high level of public policy attention in the coming decades. From the spectre of a global pandemic to anxieties over agricultural change, urbanization, social inequality and threats to natural ecosystems, effectively preparing and responding to endemic and emerging diseases will require technological, institutional and social innovation. Much current discussion emphasizes the need for a 'One Health' approach: bridging disciplines and sectors to tackle these complex dynamics. However, as attention has increased, so too has an appreciation of the practical challenges in linking multi-disciplinary, multi-sectoral research with policy, action and impact. In this commentary paper, we reflect on these issues with particular reference to the African sub-continent. We structure the themes of our analysis on the existing literature, expert opinion and 11 interviews with leading One Health scholars and practitioners, conducted at an international symposium in 2016. We highlight a variety of challenges in research and knowledge production, in the difficult terrain of implementation and outreach, and in the politicized nature of decision-making and priority setting. We then turn our attention to a number of strategies that might help reconfigure current pathways and accepted norms of practice. These include: (i) challenging scientific expertise; (ii) strengthening national multi-sectoral coordination; (iii) building on what works; and (iv) re-framing policy narratives. We argue that bridging the research-policy-action interface in Africa, and better connecting zoonoses, ecosystems and well-being in the twenty-first century, will ultimately require greater attention to the democratization of science and public policy.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.

Bat detective-Deep learning tools for bat acoustic signal detection

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.

Crush injury to motor nerves in the G93A transgenic mouse model of amyotrophic lateral sclerosis promotes muscle reinnervation and survival of functionally intact nerve-muscle contacts

Selective survival of small motor nerve fibers and their neuromuscular contacts in the SOD1^G93A transgenic mouse model of amyotrophic lateral sclerosis (ALS) suggests that smaller regenerated nerve fibers are more able to sustain reformed nerve-muscle connections as functionally intact motor units (MUs). The sciatic nerve was crushed unilaterally in SOD1^G93A transgenic mice at 40 days of age and contractile forces of reinnervated muscles and their MUs were recorded at 90 days in order to determine the capacities of the nerves to regenerate and to form and retain functional neuromuscular connections. Reduced MU numbers in fast-twitch tibialis anterior, extensor digitorum longus and medial gastrocnemius muscles and the lesser reductions in slow-twitch soleus muscle of SOD1^G93A transgenic mice were reversed in reinnervated muscles: there were more reinnervated MUs and their contractile forces and the muscle forces and weights increased. In line with the contrasting ability of only small not large nerve fibers to sprout to form enlarged MUs in the SOD1^G93A transgenic mouse, the smaller regenerating nerve fibers formed enlarged MUs that were better able to survive. Because nerve fibers with and without muscle contacts were severed by the sciatic nerve crush injury, the conditioning lesion is untenable as the explanation for improved maintenance of reinnervated neuromuscular junctions. Elevated neurotrophic factor expression in axotomized motoneurons and/or denervated Schwann cells and the synapse withdrawal from axotomized motoneurons are other factors that, in addition to reduced size of nerve fibers reinnervating muscles, may account for increased survival and size of reinnervated MUs in ALS.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

Understanding the cryptic nature of Lassa fever in West Africa

Lassa fever (LF) is increasingly recognized by global health institutions as an important rodent-borne disease with severe impacts on some of West Africa's poorest communities. However, our knowledge of LF ecology, epidemiology and distribution is limited, which presents barriers to both short-term disease forecasting and prediction of long-term impacts of environmental change on Lassa virus (LASV) zoonotic transmission dynamics. Here, we synthesize current knowledge to show that extrapolations from past research have produced an incomplete picture of the incidence and distribution of LF, with negative consequences for policy planning, medical treatment and management interventions. Although the recent increase in LF case reports is likely due to improved surveillance, recent studies suggest that future socio-ecological changes in West Africa may drive increases in LF burden. Future research should focus on the geographical distribution and disease burden of LF, in order to improve its integration into public policy and disease control strategies.

Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.

The Global Distribution and Drivers of Alien Bird Species Richness

Alien species are a major component of human-induced environmental change. Variation in the numbers of alien species found in different areas is likely to depend on a combination of anthropogenic and environmental factors, with anthropogenic factors affecting the number of species introduced to new locations, and when, and environmental factors influencing how many species are able to persist there. However, global spatial and temporal variation in the drivers of alien introduction and species richness remain poorly understood. Here, we analyse an extensive new database of alien birds to explore what determines the global distribution of alien species richness for an entire taxonomic class. We demonstrate that the locations of origin and introduction of alien birds, and their identities, were initially driven largely by European (mainly British) colonialism. However, recent introductions are a wider phenomenon, involving more species and countries, and driven in part by increasing economic activity. We find that, globally, alien bird species richness is currently highest at midlatitudes and is strongly determined by anthropogenic effects, most notably the number of species introduced (i.e., “colonisation pressure”). Nevertheless, environmental drivers are also important, with native and alien species richness being strongly and consistently positively associated. Our results demonstrate that colonisation pressure is key to understanding alien species richness, show that areas of high native species richness are not resistant to colonisation by alien species at the global scale, and emphasise the likely ongoing threats to global environments from introductions of species.

The introduction of alien species is one of the primary ways in which human actions are changing the environment. Alien species have been responsible for numerous global and local extinctions and are eroding the uniqueness of many natural environments. There is thus a basic need to understand which areas end up with more alien species. Here, we use a major new global database on the distribution of alien birds to show, first, how patterns in the number of species introduced to a location (colonisation pressure) have changed over time. We show that historical introductions were driven largely by European, and especially British, colonialism. However, the rate of bird introductions is increasing, with shifts in the locations of origin and introduction of species probably driven by the cage bird trade. We then combine information on where bird species have been introduced with a global map of alien bird species richness to identify the main drivers of richness. We show that colonisation pressure is the strongest predictor of alien bird species richness, but that there are other anthropogenic and environmental drivers. Most notably, once colonisation pressure has been accounted for, alien bird species richness is higher in areas where native bird species richness is higher.

Why do different oceanic archipelagos harbour contrasting levels of species diversity? The macaronesian endemic genus Pericallis (Asteraceae) provides insight into explaining the 'Azores diversity Enigma'

Background

Oceanic archipelagos typically harbour extensive radiations of flowering plants and a high proportion of endemics, many of which are restricted to a single island (Single Island Endemics; SIEs). The Azores represents an anomaly as overall levels of endemism are low; there are few SIEs and few documented cases of intra-archipelago radiations. The distinctiveness of the flora was first recognized by Darwin and has been referred to as the ‘Azores Diversity Enigma’ (ADE). Diversity patterns in the Macaronesian endemic genus Pericallis (Asteraceae) exemplify the ADE. In this study we used morphometric, Amplified Length Polymorphisms, and bioclimatic data for herbaceous Pericallis lineages endemic to the Azores and the Canaries, to test two key hypotheses proposed to explain the ADE: i) that it is a taxonomic artefact or Linnean shortfall, ie. the under description of taxa in the Azores or the over-splitting of taxa in the Canaries and (ii) that it reflects the greater ecological homogeneity of the Azores, which results in limited opportunity for ecological diversification compared to the Canaries.

Results

In both the Azores and the Canaries, morphological patterns were generally consistent with current taxonomic classifications. However, the AFLP data showed no genetic differentiation between the two currently recognized Azorean subspecies that are ecologically differentiated. Instead, genetic diversity in the Azores was structured geographically across the archipelago. In contrast, in the Canaries genetic differentiation was mostly consistent with morphology and current taxonomic treatments. Both Azorean and Canarian lineages exhibited ecological differentiation between currently recognized taxa.

Conclusions

Neither a Linnean shortfall nor the perceived ecological homogeneity of the Azores fully explained the ADE-like pattern observed in Pericallis. Whilst variation in genetic data and morphological data in the Canaries were largely congruent, this was not the case in the Azores, where genetic patterns reflected inter-island geographical isolation, and morphology reflected intra-island bioclimatic variation. The combined effects of differences in (i) the extent of geographical isolation, (ii) population sizes and (iii) geographical occupancy of bioclimatic niche space, coupled with the morphological plasticity of Pericallis, may all have contributed to generating the contrasting patterns observed in the archipelagos.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0766-1) contains supplementary material, which is available to authorized users.

Axial allometry in a neutrally buoyant environment: effects of the terrestrial-aquatic transition on vertebral scaling

Ecological diversification into new environments presents new mechanical challenges for locomotion. An extreme example of this is the transition from a terrestrial to an aquatic lifestyle. Here, we examine the implications of life in a neutrally buoyant environment on adaptations of the axial skeleton to evolutionary increases in body size. On land, mammals must use their thoracolumbar vertebral column for body support against gravity and thus exhibit increasing stabilization of the trunk as body size increases. Conversely, in water, the role of the axial skeleton in body support is reduced, and, in aquatic mammals, the vertebral column functions primarily in locomotion. Therefore, we hypothesize that the allometric stabilization associated with increasing body size in terrestrial mammals will be minimized in secondarily aquatic mammals. We test this by comparing the scaling exponent (slope) of vertebral measures from 57 terrestrial species (23 felids, 34 bovids) to 23 semi-aquatic species (pinnipeds), using phylogenetically corrected regressions. Terrestrial taxa meet predictions of allometric stabilization, with posterior vertebral column (lumbar region) shortening, increased vertebral height compared to width, and shorter, more disc-shaped centra. In contrast, pinniped vertebral proportions (e.g. length, width, height) scale with isometry, and in some cases, centra even become more spool-shaped with increasing size, suggesting increased flexibility. Our results demonstrate that evolution of a secondarily aquatic lifestyle has modified the mechanical constraints associated with evolutionary increases in body size, relative to terrestrial taxa.

Quantifying Global Drivers of Zoonotic Bat Viruses: A Process-Based Perspective

Emerging infectious diseases (EIDs), particularly zoonoses, represent a significant threat to global health. Emergence is often driven by anthropogenic activity (e.g., travel, land use change). Although disease emergence frameworks suggest multiple steps from initial zoonotic transmission to human-to-human spread, there have been few attempts to empirically model specific steps. We create a process-based framework to separate out components of individual emergence steps. We focus on early emergence and expand the first step, zoonotic transmission, into processes of generation of pathogen richness, transmission opportunity, and establishment, each with its own hypothesized drivers. Using this structure, we build a spatial empirical model of these drivers, taking bat viruses shared with humans as a case study. We show that drivers of both viral richness (host diversity and climatic variability) and transmission opportunity (human population density, bushmeat hunting, and livestock production) are associated with virus sharing between humans and bats. We also show spatial heterogeneity between the global patterns of these two processes, suggesting that high-priority locations for pathogen discovery and surveillance in wildlife may not necessarily coincide with those for public health intervention. Finally, we offer direction for future studies of zoonotic EIDs by highlighting the importance of the processes underlying their emergence.

Preliminary data on the effect of osseous anatomy on ex vivo joint mobility in the equine thoracolumbar region

REASONS FOR PERFORMING STUDY

The thoracolumbar region is clinically important in horses; however, the link between joint mobility and bony joint morphology has not been tested quantitatively.

OBJECTIVES

To establish which aspects of vertebral morphology correlate with ex vivo range of motion in the thoracolumbar region of Equus caballus, and demonstrate methodologies for linking vertebral form and function.

STUDY DESIGN

Morphometric study of osteological specimens.

METHODS

A digital model was created of a disarticulated thoracolumbar region to examine bone-to-bone interactions during in silico bending. Linear measurements and geometric morphometric landmarks were taken from 6 vertebrae per specimen (specimens n = 5, vertebrae n = 30), and compared with experimental range of motion in dorsiflexion, ventroflexion, lateroflexion and axial rotation data using Spearman's rank correlation, to test a priori hypotheses regarding thoracolumbar functional anatomy.

RESULTS

Decreased sagittal mobility correlates with a tall, heart-shaped vertebral body, although bony interactions restrict dorsiflexion more than ventroflexion. Lateroflexion correlates with a narrow vertebral body, a short transverse process lever arm, and narrowly placed horizontally oriented zygapophyses. Lateral joints also restrict lateroflexion in the posterior lumbar region. Axial rotation is related to the shape of the zygapophyseal joint.

CONCLUSIONS

These preliminary data suggest that vertebral joint morphology does determine experimentally measured range of motion, but patterns depend upon the type of motion. These methods are useful for identifying functionally relevant morphological variation and suggest osteological features are important in determining motion.

A generalised random encounter model for estimating animal density with remote sensor data

Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals.

We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement.

We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM.

We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.

Colony size predicts division of labour in attine ants

Division of labour is central to the ecological success of eusocial insects, yet the evolutionary factors driving increases in complexity in division of labour are little known. The size-complexity hypothesis proposes that, as larger colonies evolve, both non-reproductive and reproductive division of labour become more complex as workers and queens act to maximize inclusive fitness. Using a statistically robust phylogenetic comparative analysis of social and environmental traits of species within the ant tribe Attini, we show that colony size is positively related to both non-reproductive (worker size variation) and reproductive (queen-worker dimorphism) division of labour. The results also suggested that colony size acts on non-reproductive and reproductive division of labour in different ways. Environmental factors, including measures of variation in temperature and precipitation, had no significant effects on any division of labour measure or colony size. Overall, these results support the size-complexity hypothesis for the evolution of social complexity and division of labour in eusocial insects. Determining the evolutionary drivers of colony size may help contribute to our understanding of the evolution of social complexity.

Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease

The proper allocation of public health resources for research and control requires quantification of both a disease's current burden and the trend in its impact. Infectious diseases that have been labeled as "emerging infectious diseases" (EIDs) have received heightened scientific and public attention and resources. However, the label 'emerging' is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled "emerging," and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health.

A framework for the study of zoonotic disease emergence and its drivers: spillover of bat pathogens as a case study

Many serious emerging zoonotic infections have recently arisen from bats, including Ebola, Marburg, SARS-coronavirus, Hendra, Nipah, and a number of rabies and rabies-related viruses, consistent with the overall observation that wildlife are an important source of emerging zoonoses for the human population. Mechanisms underlying the recognized association between ecosystem health and human health remain poorly understood and responding appropriately to the ecological, social and economic conditions that facilitate disease emergence and transmission represents a substantial societal challenge. In the context of disease emergence from wildlife, wildlife and habitat should be conserved, which in turn will preserve vital ecosystem structure and function, which has broader implications for human wellbeing and environmental sustainability, while simultaneously minimizing the spillover of pathogens from wild animals into human beings. In this review, we propose a novel framework for the holistic and interdisciplinary investigation of zoonotic disease emergence and its drivers, using the spillover of bat pathogens as a case study. This study has been developed to gain a detailed interdisciplinary understanding, and it combines cutting-edge perspectives from both natural and social sciences, linked to policy impacts on public health, land use and conservation.

What is macroecology?

The symposium 'What is Macroecology?' was held in London on 20 June 2012. The event was the inaugural meeting of the Macroecology Special Interest Group of the British Ecological Society and was attended by nearly 100 scientists from 11 countries. The meeting reviewed the recent development of the macroecological agenda. The key themes that emerged were a shift towards more explicit modelling of ecological processes, a growing synthesis across systems and scales, and new opportunities to apply macroecological concepts in other research fields.

Ecology and evolution of mammalian biodiversity

Mammals have incredible biological diversity, showing extreme flexibility in eco-morphology, physiology, life history and behaviour across their evolutionary history. Undoubtedly, mammals play an important role in ecosystems by providing essential services such as regulating insect populations, seed dispersal and pollination and act as indicators of general ecosystem health. However, the macroecological and macroevolutionary processes underpinning past and present biodiversity patterns are only beginning to be explored on a global scale. It is also particularly important, in the face of the global extinction crisis, to understand these processes in order to be able to use this knowledge to prevent future biodiversity loss and loss of ecosystem services. Unfortunately, efforts to understand mammalian biodiversity have been hampered by a lack of data. New data compilations on current species' distributions, ecologies and evolutionary histories now allow an integrated approach to understand this biodiversity. We review and synthesize these new studies, exploring the past and present ecology and evolution of mammalian biodiversity, and use these findings to speculate about the mammals of our future.

Can unified theories of biodiversity explain mammalian macroecological patterns?

The idea of a unifying theory of biodiversity linking the diverse array of macroecological patterns into a common theoretical framework is very appealing. We explore this idea to examine currently proposed unified theories of biodiversity (UTBs) and their predictions. Synthesizing the literature on the macroecological patterns of mammals, we critically evaluate the evidence to support these theories. We find general qualitative support for the UTBs' predictions within mammals, but rigorous testing is hampered by the types of data typically collected in studies of mammals. In particular, abundance is rarely estimated for entire mammalian communities or of individual species in multiple locations, reflecting the logistical challenges of studying wild mammal populations. By contrast, there are numerous macroecological patterns (especially allometric scaling relationships) that are extremely well characterized for mammals, but which fall outside the scope of current UTBs. We consider how these theories might be extended to explain mammalian biodiversity patterns more generally. Specifically, we suggest that UTBs need to incorporate the dimensions of geographical space, species' traits and time to reconcile theory with pattern.