Reassessing the alternative ecosystem states proposition in the African savanna-forest domain.

Ecologists are being challenged to predict how ecosystems will respond to climate changes. According to the Multi-Colored World (MCW) hypothesis, climate impacts may not manifest because consumers such as fire and herbivory can override the influence of climate on ecosystem state. One MCW interpretation is that climate determinism fails because alternative ecosystem states (AES) are possible at some locations in climate space. We evaluated theoretical and empirical evidence for the proposition that forest and savanna are AES in Africa. We found that maps which infer where AES zones are located were contradictory. Moreover, data from longitudinal and experimental studies provide inconclusive evidence for AES. That is, although the forest-savanna AES proposition is theoretically sound, the existing evidence is not yet convincing. We conclude by making the case that the AES proposition has such fundamental consequences for designing management actions to mitigate and adapt to climate change in the savanna-forest domain that it needs a more robust evidence base before it is used to prescribe management actions.

The Phylogenetic Structure of Reptile, Avian and Uropathogenic Escherichia coli with Particular Reference to Extraintestinal Pathotypes.

The impact of the Gram-negative bacterium Escherichia coli (E. coli) on the microbiomic and pathogenic phenomena occurring in humans and other warm-blooded animals is relatively well-recognized. At the same time, there are scant data concerning the role of E. coli strains in the health and disease of cold-blooded animals. It is presently known that reptiles are common asymptomatic carriers of another human pathogen, Salmonella, which, when transferred to humans, may cause a disease referred to as reptile-associated salmonellosis (RAS). We therefore hypothesized that reptiles may also be carriers of specific E. coli strains (reptilian Escherichia coli, RepEC) which may differ in their genetic composition from the human uropathogenic strain (UPEC) and avian pathogenic E. coli (APEC). Therefore, we isolated RepECs (n = 24) from reptile feces and compared isolated strains' pathogenic potentials and phylogenic relations with the aforementioned UPEC (n = 24) and APEC (n = 24) strains. To this end, we conducted an array of molecular analyses, including determination of the phylogenetic groups of E. coli, virulence genotyping, Pulsed-Field Gel Electrophoresis-Restriction Analysis (RA-PFGE) and genetic population structure analysis using Multi-Locus Sequence Typing (MLST). The majority of the tested RepEC strains belonged to nonpathogenic phylogroups, with an important exception of one strain, which belonged to the pathogenic group B2, typical of extraintestinal pathogenic E. coli. This strain was part of the globally disseminated ST131 lineage. Unlike RepEC strains and in line with previous studies, a high percentage of UPEC strains belonged to the phylogroup B2, and the percentage distribution of phylogroups among the tested APEC strains was relatively homogenous, with most coming from the following nonpathogenic groups: C, A and B1. The RA-PFGE displayed a high genetic diversity among all the tested E. coli groups. In the case of RepEC strains, the frequency of occurrence of virulence genes (VGs) was lower than in the UPEC and APEC strains. The presented study is one of the first attempting to compare the phylogenetic structures of E. coli populations isolated from three groups of vertebrates: reptiles, birds and mammals (humans).

Comment on "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate", author Coro.

In this letter we present comments on the article "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus" by Coro published in 2020.

Q(ST)-F(ST) comparisons: evolutionary and ecological insights from genomic heterogeneity.

Comparative studies of the divergence of quantitative traits and neutral molecular markers, known as Q(ST)-F(ST) comparisons, provide a means for researchers to distinguish between natural selection and genetic drift as causes of population differentiation in complex polygenic traits. The use of Q(ST)-F(ST) comparisons has increased rapidly in the last few years, highlighting the utility of this approach for addressing a wide range of questions that are relevant to evolutionary and ecological genetics. These studies have also provided lessons for the design of future Q(ST)-F(ST) comparisons. Methods based on the Q(ST)-F(ST) approach could also be used to analyse various types of 'omics' data in new and revealing ways.

Uncovering the drivers of host-associated microbiota with joint species distribution modelling.

In addition to the processes structuring free-living communities, host-associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host-specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model-based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host-specific factors are in structuring the microbiota; and (c) co-occurrence networks to visualize microbe-to-microbe associations.

Disentangling the effects of multiple environmental drivers on population changes within communities.

The effects of different environmental drivers on the changes in species' population abundances can be difficult to disentangle as they often act simultaneously. Researchers have built statistical models that include environmental variables (such as annual temperature) or species attributes (such as a species' temperature preference), which are assumed to detect the impacts of specific drivers (such as climate change). However, these approaches are often applied separately or, if combined, not explicitly compared. We show the complementary insights gained by applying both these approaches to a community dataset on Danish terrestrial birds. We use our analysis to compare the relative importance of climate change and agricultural land-use change for the abundance changes within the community between 1983 and 2013. Population models were fitted to the community data of species' annual abundances with predictors comprising: species attributes (species' temperature and habitat preferences), environmental variables (climatic and agricultural land-use change variables) or both. Relationships between species' abundances and environmental variables were used to identify the drivers associated with average abundance changes of species in the community. Relationships between species' abundances and their attributes were used to understand the drivers causing interspecific variation in abundance changes. Warmer winters were positively associated with community-level abundances, and warm-adapted species had more positive abundance changes than cold-adapted ones. Agricultural land-use area was negatively associated with community-level abundances, and birds using a high proportion of meadow and habitat specialists had more negative abundance changes than birds using other habitats and habitat generalists. Effect sizes of environmental variables were larger for agricultural land-use change while those of species attributes were larger for climate change. The environmental data approach suggested that agricultural land-use change has decreased the average abundances of species in the community, affecting total community size while the species attribute-based approach suggested that climate change has caused variation in abundance among species, affecting community composition. Environmental variables and species attributes that are hypothesized to link to specific drivers can be used together to provide complementary information on the impacts of different drivers on communities.

Disentangling synergistic disease dynamics: Implications for the viral biocontrol of rabbits.

European rabbits (Oryctolagus cuniculus) have been exposed to rabbit haemorrhagic disease virus (RHDV) and myxoma virus (MYXV) in their native and invasive ranges for decades. Yet, the long-term effects of these viruses on rabbit population dynamics remain poorly understood. In this context, we analysed 17 years of detailed capture-mark-recapture data (2000-2016) from Turretfield, South Australia, using a probabilistic state-space hierarchical modelling framework to estimate rabbit survival and epidemiological dynamics. While RHDV infection and disease-induced death were most prominent during annual epidemics in winter and spring, we found evidence for continuous infection of susceptible individuals with RHDV throughout the year. RHDV-susceptible rabbits had, on average, 25% lower monthly survival rates compared to immune individuals, while the average monthly force of infection in winter and spring was ~38%. These combined to result in an average infection-induced mortality rate of 69% in winter and spring. Individuals susceptible to MYXV and immune to RHDV had similar survival probabilities to those having survived infections from both viruses, whereas individuals susceptible to both RHDV and MYXV had higher survival probabilities than those susceptible to RHDV and immune to MYXV. This suggests that MYXV may reduce the future survival rates of individuals that endure initial MYXV infection. There was no evidence for long-term changes in disease-induced mortality and infection rates for either RHDV or MYXV. We conclude that continuous, year-round virus perpetuation (and perhaps heterogeneity in modes of transmission and infectious doses during and after epidemics) acts to reduce the efficiency of RHDV and MYXV as biocontrol agents of rabbits in their invasive range. However, if virulence can be maintained as relatively constant through time, RHDV and MYXV will likely continue realizing strong benefits as biocontrol agents.

Cross-taxa generalities in the relationship between population abundance and ambient temperatures.

Identifying patterns in the effects of temperature on species' population abundances could help develop a general framework for predicting the consequences of climate change across different communities and realms. We used long-term population time series data from terrestrial, freshwater, and marine species communities within central Europe to compare the effects of temperature on abundance across a broad range of taxonomic groups. We asked whether there was an average relationship between temperatures in different seasons and annual abundances of species in a community, and whether species attributes (temperature range of distribution, range size, habitat breadth, dispersal ability, body size, and lifespan) explained interspecific variation in the relationship between temperature and abundance. We found that, on average, warmer winter temperatures were associated with greater abundances in terrestrial communities (ground beetles, spiders, and birds) but not always in aquatic communities (freshwater and marine invertebrates and fish). The abundances of species with large geographical ranges, larger body sizes, and longer lifespans tended to be less related to temperature. Our results suggest that climate change may have, in general, positive effects on species' abundances within many terrestrial communities in central Europe while the effects are less predictable in aquatic communities.

Environmental effects and individual body condition drive seasonal fecundity of rabbits: identifying acute and lagged processes.

The reproduction of many species is determined by seasonally-driven resource supply. But it is difficult to quantify whether the fecundity is sensitive to short- or long-term exposure to environmental conditions such as rainfall that drive resource supply. Using 25 years of data on individual fecundity of European female rabbits, Oryctolagus cuniculus, from semiarid Australia, we investigate the role of individual body condition, rainfall and temperature as drivers of seasonal and long-term and population-level changes in fecundity (breeding probability, ovulation rate, embryo survival). We built distributed lag models in a hierarchical Bayesian framework to account for both immediate and time-lagged effects of climate and other environmental drivers, and possible shifts in reproduction over consecutive seasons. We show that rainfall during summer, when rabbits typically breed only rarely, increased breeding probability immediately and with time lags of up to 10 weeks. However, an earlier onset of the yearly breeding period did not result in more overall reproductive output. Better body condition was associated with an earlier onset of breeding and higher embryo survival. Breeding probability in the main breeding season declined with increased breeding activity in the preceding season and only individuals in good body condition were able to breed late in the season. Higher temperatures reduce breeding success across seasons. We conclude that a better understanding of seasonal dynamics and plasticity (and their interplay) in reproduction will provide crucial insights into how lagomorphs are likely to respond and potentially adapt to the influence of future climate and other environmental change.

Relocation, high-latitude warming and host genetic identity shape the foliar fungal microbiome of poplars.

Micro-organisms associated with plants and animals affect host fitness, shape community structure and influence ecosystem properties. Climate change is expected to influence microbial communities, but their reactions are not well understood. Host-associated micro-organisms are influenced by the climate reactions of their hosts, which may undergo range shifts due to climatic niche tracking, or may be actively relocated to mitigate the effects of climate change. We used a common-garden experiment and rDNA metabarcoding to examine the effect of host relocation and high-latitude warming on the complex fungal endophytic microbiome associated with leaves of an ecologically dominant boreal forest tree (Populus balsamifera L.). We also considered the potential effects of poplar genetic identity in defining the reactions of the microbiome to the treatments. The relocation of hosts to the north increased the diversity of the microbiome and influenced its structure, with results indicating enemy release from plausible pathogens. High-latitude warming decreased microbiome diversity in comparison with natural northern conditions. The warming also caused structural changes, which made the fungal communities distinct in comparison with both low-latitude and high-latitude natural communities, and increased the abundance of plausible pathogens. The reactions of the microbiome to relocation and warming were strongly dependent on host genetic identity. This suggests that climate change effects on host-microbiome systems may be mediated by the interaction of environmental factors and the population genetic processes of the hosts.

Spatio-temporal distribution of malaria and its association with climatic factors and vector-control interventions in two high-risk districts of Nepal.

BACKGROUND: Over the last decade, the incidence of confirmed malaria has declined significantly in Nepal. The aim of this paper is to assess the spatio-temporal distribution of malaria and its association with climatic factors and vector control interventions in two high-risk districts of Nepal. METHODS: Hotspot analysis was used to visualize the spatio-temporal variation of malaria incidence over the years at village level and generalized additive mixed models were fitted to assess the association of malaria incidence with climatic variables and vector control interventions. RESULTS: Opposing trends of malaria incidence were observed in two high-risk malaria districts of eastern and far-western Nepal after the introduction of long-lasting insecticidal nets (LLINs). The confirmed malaria incidence was reduced from 2.24 per 10,000 in 2007 to 0.31 per 10,000 population in 2011 in Morang district but increased from 3.38 to 8.29 per 10,000 population in Kailali district. Malaria hotspots persisted mostly in the same villages of Kailali district, whereas in Morang district malaria hotspots shifted to new villages after the introduction of LLINs. A 1° C increase in minimum and mean temperatures increased malaria incidence by 27% (RR =1.27, 95% CI =1.12-1.45) and 25% (RR =1.25, 95% CI =1.11-1.43), respectively. The reduction in malaria incidence was 25% per one unit increase of LLINs (RR =0.75, 95% CI =0.62-0.92). The incidence of malaria was 82% lower in Morang than in Kailali district (RR =0.18, 95% CI =0.11-0.33). CONCLUSIONS: The study findings suggest that LLIN coverage should be scaled up to entire districts rather than high-incidence foci only. Climatic factors should be considered for malaria micro-stratification, mosquito repellents should be prescribed for those living in forests, forest fringe and foothills and have regular visits to forests, and imported cases should be controlled by establishing fever check posts at border crossings.

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Facial morphology predicts male fitness and rank but not survival in Second World War Finnish soldiers.

We investigated fitness, military rank and survival of facial phenotypes in large-scale warfare using 795 Finnish soldiers who fought in the Winter War (1939-1940). We measured facial width-to-height ratio-a trait known to predict aggressive behaviour in males-and assessed whether facial morphology could predict survival, lifetime reproductive success (LRS) and social status. We found no difference in survival along the phenotypic gradient, however, wider-faced individuals had greater LRS, but achieved a lower military rank.

Inferring host specificity and network formation through agent-based models: tick-mammal interactions in Borneo.

Patterns of host-parasite association are poorly understood in tropical forests. While we typically observe only snapshots of the diverse assemblages and interactions under variable conditions, there is a desire to make inferences about prevalence and host-specificity patterns. We studied the interaction of ticks with non-volant small mammals in forests of Borneo. We inferred the probability of species interactions from individual-level data in a multi-level Bayesian model that incorporated environmental covariates and advanced estimates for rarely observed species through model averaging. We estimated the likelihood of observing particular interaction frequencies under field conditions and a scenario of exhaustive sampling and examined the consequences for inferring host specificity. We recorded a total of 13 different tick species belonging to the five genera Amblyomma, Dermacentor, Haemaphysalis, Ixodes, and Rhipicephalus from a total of 37 different host species (Rodentia, Scandentia, Carnivora, Soricidae) on 237 out of 1,444 host individuals. Infestation probabilities revealed most variation across host species but less variation across tick species with three common rat and two tree shrew species being most heavily infested. Host species identity explained ca. 75 % of the variation in infestation probability and another 8-10 % was explained by local host abundance. Host traits and site-specific attributes had little explanatory power. Host specificity was estimated to be similarly low for all tick species, which were all likely to infest 34-37 host species if exhaustively sampled. By taking into consideration the hierarchical organization of individual interactions that may take place under variable conditions and that shape host-parasite networks, we can discern uncertainty and sampling bias from true interaction frequencies, whereas network attributes derived from observed values may lead to highly misleading results. Multi-level approaches may help to move this field towards inferential approaches for understanding mechanisms that shape the strength and dynamics in ecological networks.

Recognizability bias in citizen science photographs.

Citizen science and automated collection methods increasingly depend on image recognition to provide the amounts of observational data research and management needs. Recognition models, meanwhile, also require large amounts of data from these sources, creating a feedback loop between the methods and tools. Species that are harder to recognize, both for humans and machine learning algorithms, are likely to be under-reported, and thus be less prevalent in the training data. As a result, the feedback loop may hamper training mostly for species that already pose the greatest challenge. In this study, we trained recognition models for various taxa, and found evidence for a 'recognizability bias', where species that are more readily identified by humans and recognition models alike are more prevalent in the available image data. This pattern is present across multiple taxa, and does not appear to relate to differences in picture quality, biological traits or data collection metrics other than recognizability. This has implications for the expected performance of future models trained with more data, including such challenging species.

Limited climatic space for alternative ecosystem states in Africa.

One of the foundational premises of ecology is that climate determines ecosystems. This has been challenged by alternative ecosystem state models, which illustrate that internal ecosystem dynamics acting on the initial ecosystem state can overwhelm the influence of climate, and by observations suggesting that climate cannot reliably discriminate forest and savanna ecosystem types. Using a novel phytoclimatic transform, which estimates the ability of climate to support different types of plants, we show that climatic suitability for evergreen trees and C4 grasses are sufficient to discriminate between forest and savanna in Africa. Our findings reassert the dominant influence of climate on ecosystems and suggest that the role of feedbacks causing alternative ecosystem states is less prevalent than has been suggested.

Ectoparasite infestation patterns of domestic dogs in suburban and rural areas in Borneo.

Domestic dogs, Canis lupus, have been one of the longest companions of humans and have introduced their own menagerie of parasites and pathogens into this relationship. Here, we investigate the parasitic load of 212 domestic dogs with fleas (Siphonaptera) chewing lice (Phthiraptera), and ticks (Acarina) along a gradient from rural areas with near-natural forest cover to suburban areas in Northern Borneo (Sabah, Malaysia). We used a spatially-explicit hierarchical Bayesian model that allowed us to impute missing data and to consider spatial structure in modelling dog infestation probability and parasite density. We collected a total of 1,968 fleas of two species, Ctenocephalides orientis and Ctenocephalides felis felis, from 195 dogs (prevalence, 92 %). Flea density was higher on dogs residing in houses made of bamboo or corrugated metal (increase of 40 % from the average) compared to timber or stone/compound houses. Host-dependent and landscape-level environmental variables and spatial structure only had a weak explanatory power. We found adults of the invasive chewing louse Heterodoxus spiniger on 42 dogs (20 %). The effect of housing conditions was opposite to those for fleas; lice were only found on dogs residing in stone or timber houses. We found ticks of the species Rhipicephalus sanguineus as well as Haemaphysalis bispinosa gp., Haemaphysalis cornigera, Haemaphysalis koenigsbergi, and Haemaphysalis semermis on 36 dogs (17 %). The most common tick species was R. sanguineus, recorded from 23 dogs. Tick infestations were highest on dogs using both plantation and forest areas (282 % increase in overall tick density of dogs using all habitat types). The infestation probability of dogs with lice and ticks decreased with elevation, most infestations occurred below 800 m above sea level. However, the density of lice and ticks revealed no spatial structure; infestation probability of dogs with these two groups revealed considerable autocorrelation. Our study shows that environmental conditions on the house level appeared to be more influential on flea and lice density whereas tick density was also influenced by habitat use. Infestation of dogs with Haemaphysalis ticks identified an important link between dogs and forest wildlife for potential pathogen transmission.

Cognitive biases among those with frequent or chronic headaches or migraines: a systematic review and meta-analysis.

ABSTRACT: The aim of this systematic review and meta-analysis was to determine the pattern of cognitive processing biases (ie, attentional, interpretation, and memory bias) towards headache and pain information observed in individuals with frequent or chronic headaches or migraines, compared with individuals without. We identified 11 studies (total N = 841). Most studies (10 of 11) assessed attentional bias. For reaction time-based attentional bias (k = 8), significant group differences were observed, with small (500 ms; g = 0.200) to medium (≥ 1000 ms; g = 0.544) effects. Those with chronic headaches showed an absolute attentional bias towards headache and pain information. In 2 eye-tracking studies, both found significant medium-to-large group differences on initial orientation. Interpretation and memory bias research was also reviewed. A medium-to-large effect was found for interpretation bias (k = 2; g = 0.708), whereas the effect for recall memory bias (k = 3) was not significant. Overall, the findings confirm an attentional bias for headache-related stimuli among people with headache, with some evidence for interpretation bias but equivocal evidence for a memory bias. For attentional biases, eye-tracking studies found evidence for biases in initial orienting. We provide suggestions for how to extend the current research to better understand cognitive biases in chronic headache.

Rewriting results sections in the language of evidence.

Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the 'good practices' that exist in other fields.

Evaluation of a novel direct RT-LAMP assay for the detection of SARS-CoV-2 from saliva samples in asymptomatic individuals.

Large scale screening of health care workers and the general population for asymptomatic COVID-19 infection requires modalities that are amenable to testing at scale while retaining acceptable levels of sensitivity and specificity. This study evaluated a novel COVID-19 Direct-RT LAMP assay using saliva samples in asymptomatic individuals by comparison to RT-PCR. Additional studies were performed using VTM collected from routine diagnostic testing. Analytical sensitivity was determined for Direct RT-LAMP assay using the WHO International Standard. Finally, quantified results from RT-PCR testing of 9177 nose and throat swabs obtained from routine diagnostic testing were used to estimate the sensitivity of Direct RT-LAMP using the limit of detection curve obtained from the analytical sensitivity data. Results from saliva testing demonstrated a sensitivity of 40.91% and a specificity of 100% for Direct RT-LAMP. The sensitivity and specificity for nose and throat swabs were 44.85% and 100% respectively. The 95% limit of detection (LOD) for Direct RT-LAMP was log 7.13 IU/ml (95% 6.9-7.5). The estimated sensitivity for Direct-RT LAMP based on the results of 9117 nose and throat swabs was 34% and 45% for saliva and VTM respectively. The overall diagnostic sensitivity of Direct RT-LAMP was low compared to RT-PCR. Testing of nose and throat swabs and estimating the sensitivity based on a large cohort of clinical samples demonstrated similar results. This study highlights the importance of utilising the prospective collection of samples from the intended target population in the assessment of diagnostic sensitivity.