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Climate warming is a ubiquitous stressor in freshwater ecosystems, yet its interactive effects with other stressors are poorly understood. We address this knowledge gap by testing the ability of three contrasting null models to predict the joint impacts of warming and a range of other aquatic stressors using a new database of 296 experimental combinations. Despite concerns that stressors will interact to cause synergisms, we found that net impacts were usually best explained by the effect of the stronger stressor alone (the dominance null model), especially if this stressor was a local disturbance associated with human land use. Prediction accuracy depended on stressor identity and how asymmetric stressors were in the magnitude of their effects. These findings suggest we can effectively predict the impacts of multiple stressors by focusing on the stronger stressor, as habitat alteration, nutrients and contamination often override the biological consequences of higher temperatures in freshwater ecosystems.

There is a general agreement within the wildfire community that exclusively top down approaches to policy making and management are limited and that we need to build governance capacity to cooperatively manage across jurisdictional boundaries. Accordingly, the concept of co-management has grown in popularity as a theoretical lens through which to understand cross-boundary response to wildland fires. However, definitional ambiguity has led to on-going debates about what co-management is. Further, there is limited understanding about the nature of co-management during crisis events. This had led to scholars posing the question: What is co-management in the context of jurisdictionally complex wildfire? In this paper, we seek to address this question based on interviews with leaders engaged in the management of jurisdictionally complex wildfire incidents. We propose a multi-level framework that defines co-management as strategic efforts of individual actors to cooperatively manage perceived interdependencies with others through one or more formal or informal institutional arrangements. Our findings reveal different strategic orientations to co-management that have management implications. We then demonstrate the value of the proposed framework in its ability to organize a series of diagnostic questions for diagnosing co-management situations within the context of jurisdictionally complex wildfires. Accepted version

Apex predators structure ecosystems by hunting mesopredators and herbivores. Their ecological influence is determined not only by the number of animals they kill, but also by how prey alter their behaviours to reduce risk. Predation risk is variable in space and time creating a landscape of fear. In Australia, dingoes hunt red foxes and suppress their populations. As both predators are commonly subjected to eradication programs, the question arises whether humans alter the risk dingoes pose to foxes and in turn alter the foxes' avoidance behaviours. We studied the spatio-temporal activity patterns and wariness behaviours of foxes and dingoes at sites where they were protected (predator friendly), where they were persecuted (predator persecuted), and at sites where foxes were persecuted, and dingoes had been eradicated (dingo eradicated). The landscape of fear hypothesis predicts that foxes will be the most spatiotemporally restricted and most fearful at predator friendly sites, and least restricted and fearful at dingo eradicated sites. We found that fox occupancy was highest at dingo eradicated sites; and that they avoided times of heightened dingo activity at predator friendly sites more than at predator persecuted sites. Contrary to predictions, foxes were the least fearful (lowest frequency of cautious and vigilant behaviour) and most social (highest frequency of social interactions) at predator friendly sites. Our findings suggest that in the absence of persecution, mesopredators living with socially-stable apex predators can anticipate and avoid risk, reducing the need for constant vigilance (i.e. fear). Where predators are protected, predator avoidance may be driven by knowledge rather than fear alone.

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

1. Understanding the role of different species in the transmission of multi-host pathogens, such as rabies virus, is vital for effective control strategies. Across most of sub-Saharan Africa domestic dogs Canis familiaris are considered the reservoir for rabies, but the role of wildlife has been long debated. Here we explore the multi-host transmission dynamics of rabies across south-east Tanzania. 2. Between January 2011 and July 2019, data on probable rabies cases were collected in the regions of Lindi and Mtwara. Hospital records of animal-bite patients presenting to healthcare facilities were used as sentinels for animal contact tracing. The timing, location and species of probable rabid animals were used to reconstruct transmission trees to infer who infected whom and the relative frequencies of within- and between-species transmission. 3. During the study, 688 probable human rabies exposures were identified, resulting in 47 deaths. Of these exposures, 389 were from domestic dogs (56.5%) and 262 from jackals (38.1%). Over the same period, 549 probable animal rabies cases were traced: 303 in domestic dogs (55.2%) and 221 in jackals (40.3%), with the remainder in domestic cats and other wildlife species. 4. Although dog-to-dog transmission was most commonly inferred (40.5% of transmission events), a third of inferred events involved wildlife-to-wildlife transmission (32.6%), and evidence suggested some sustained transmission chains within jackal populations. 5. A steady decline in probable rabies cases in both humans and animals coincided with the implementation of widespread domestic dog vaccination during the first 6 years of the study. Following the lapse of this program, dog rabies cases began to increase in one of the northernmost districts. 6. Synthesis and applications. In south-east Tanzania, despite a relatively high incidence of rabies in wildlife and evidence of wildlife-to-wildlife transmission, domestic dogs remain essential to the reservoir of infection. Continued dog vaccination alongside improved surveillance would allow a fuller understanding of the role of wildlife in maintaining transmission in this area. Nonetheless, dog vaccination clearly suppressed rabies in both domestic dog and wildlife populations, reducing both public health and conservation risks and, if sustained, has potential to eliminate rabies from this region.

Fire has long been a part of the Australian environment and remains a potent force in shaping the adaptations of species, the dynamics of populations, and the structure of ecological communities. However, the fire regime is changing. Fire seasons are longer, wet vegetation types that do not usually burn are now at risk, and fire intensity and severity are greater than in the recent past. A particularly widespread event occurred in the forest and woodland regions of Australia over the summer of 2019-2020. Termed the "Black Summer" bushfires, remotely sensed data indicate that over 30 million hectares of vegetation were burned, including an unprecedented large area of forest. The extraordinary geographical scale of the Black Summer fires, including the intensity and speed of fire spread, has led to widespread concern about the ecological damage that occurred. Recent estimates suggest that almost three billion vertebrates and up to 240 trillion invertebrates were affected by the fires. Fires were experienced in part of the geographical ranges of 832 vertebrate species and 37 threatened ecological communities, with some of these entities feared to be at risk of extinction. Field assessments of ecological recovery have been slowed by COVID-19 restrictions and by a diminution in ecological monitoring capacity that occurred before the last fire season. This paper assesses the dire ecological consequences of the Black Summer bushfires and proposes a series of steps to help achieve recovery of biodiversity and mitigate the effects of future mega-fires. Integr Environ Assess Manag 2021;17:1162-1167. © 2021 SETAC.

Water is the main limiting factor for groundwater-dependent ecosystems (GDEs) in drylands. Predicted climate change (precipitation reductions and temperature increases) and anthropogenic activities such as groundwater drawdown jeopardise the functioning of these ecosystems, presenting new challenges for their management. We developed a trait-based analysis to examine the spatiotemporal variability in the ecophysiology of Ziziphus lotus, a long-lived phreatophyte that dominates one of the few terrestrial GDEs of semiarid regions in Europe. We assessed morpho-functional traits and stem water potential along a naturally occurring gradient of depth-to-groundwater (DTGW, 2–25 m) in a coastal aquifer, and throughout the species-growing season. Increasing DTGW and salinity negatively affected photosynthetic and transpiration rates, increasing plant water stress (lower predawn and midday water potential), and positively affected Huber value (sapwood cross-sectional area per leaf area), reducing leaf area and likely, plant hydraulic demand. However, the species showed greater salt-tolerance at shallow depths. Despite groundwater characteristics, higher atmospheric evaporative demand in the study area, which occurred in summer, fostered higher transpiration rates and water stress, and promoted carbon assimilation and water loss more intensively at shallow water tables. This multiple-trait analysis allowed us to identify plant ecophysiological thresholds related to the increase in salinity, but mostly in DTGW (13 m), and in the evaporative demand during the growing season. These findings highlight the existence of tipping points in the functioning of a long-lived phreatophyte in drylands and can contribute to the sustainable management of GDEs in southern Europe, paving the way for further studies on phreatophytic species. Supplementary Information The online version contains supplementary material available at 10.1007/s00442-021-04993-w.

It has often been suggested that the productivity of an ecosystem affects the number of species that it can support. Despite decades of study, the nature, extent, and underlying mechanisms of this relationship are unclear. One suggested mechanism is the “more individuals” hypothesis (MIH). This proposes that productivity controls the number of individuals in the ecosystem, and that more individuals can be divided into a greater number of species before their population size is sufficiently small for each to be at substantial risk of extinction. Here, we test this hypothesis using REvoSim: an individual‐based eco‐evolutionary system that simulates the evolution and speciation of populations over geological time, allowing phenomena occurring over timescales that cannot be easily observed in the real world to be evaluated. The individual‐based nature of this system allows us to remove assumptions about the nature of speciation and extinction that previous models have had to make. Many of the predictions of the MIH are supported in our simulations: Rare species are more likely to undergo extinction than common species, and species richness scales with productivity. However, we also find support for relationships that contradict the predictions of the strict MIH: species population size scales with productivity, and species extinction risk is better predicted by relative than absolute species population size, apparently due to increased competition when total community abundance is higher. Furthermore, we show that the scaling of species richness with productivity depends upon the ability of species to partition niche space. Consequently, we suggest that the MIH is applicable only to ecosystems in which niche partitioning has not been halted by species saturation. Some hypotheses regarding patterns of biodiversity implicitly or explicitly overlook niche theory in favor of neutral explanations, as has historically been the case with the MIH. Our simulations demonstrate that niche theory exerts a control on the applicability of the MIH and thus needs to be accounted for in macroecology., We use simulation experiments to test the predictions of the more‐individuals hypothesis of biodiversity. We find that many of these predictions are supported, but that interspecific competition and limits to niche diversity can provide additional complexity not predicted by the more‐individuals hypothesis.

Varied strategies to alleviate the loss of farmland biodiversity have been tested, yet there is still insufficient evidence supporting their effectiveness, especially when considering phylogenetic and functional diversity alongside traditional taxonomic diversity metrics. This conservation challenge is accentuated in the Afrotropics by the rapid agricultural expansion and intensification for the production of cash crops and by a comparative lack of research. In this study, we assessed how farming practices influence avian phylogenetic and functional diversity. We conducted point-count surveys to assess avian diversity in monocultures of tea and mixed crop farming systems surrounding the Nyungwe rainforest in south-west Rwanda, allowing us to investigate the drivers of avian diversity at farm level. Species composition was found to be moderately different between farm types, with mixed crop farms supporting higher phylogenetic diversity than tea plantations. There were no significant seasonal differences in species composition, functional or phylogenetic diversity. Overall, functional diversity did not differ between farm types, but the dispersion of trophic-related traits was significantly higher in mixed crop farms. Both functional and phylogenetic diversity were influenced by floristic diversity, vegetation height, tree number, and elevation to varying degrees. Our results also (i) highlight the role of farmland heterogeneity (e.g., crop species composition, height, and tree cover extent) in encouraging avian functional and phylogenetic diversity in the Afrotropics and (ii) indicate that the generally negative biodiversity impacts of monoculture agriculture can be partially alleviated by extensive agroforestry with an emphasis on indigenous tree species.

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network architecture, the Transformer has the potential to greatly improve the speed and robustness of impact detection. This paper investigates the suitability of this new network, confronting the advantages and disadvantages offered by the Transformer and a well-known and established neural network for impact detection, the Convolutional Neural Network (CNN). The comparison is undertaken on performance, scalability, and computational time. The inputs to the networks were created using a data transformation technique, which transforms the raw time series data collected from the network of piezoelectric sensors, installed on a composite panel, through the use of Fourier Transform. It is demonstrated that the Transformer method reduces the computational complexity of the impact detection significantly, while achieving excellent prediction results.

Decades of environmental DNA (eDNA) method application, spanning a wide variety of taxa and habitats, has advanced our understanding of eDNA and underlined its value as a tool for conservation practitioners. The general consensus is that eDNA methods are more accurate and cost‐effective than traditional survey methods. However, they are formally approved for just a few species globally (e.g., Bighead Carp, Silver Carp, Great Crested Newt). We conducted a meta‐analysis of studies that directly compare eDNA with traditional surveys to evaluate the assertion that eDNA methods are consistently “better.”Environmental DNA publications for multiple species or single macro‐organism detection were identified using the Web of Science, by searching “eDNA” and “environmental DNA” across papers published between 1970 and 2020. The methods used, focal taxa, habitats surveyed, and quantitative and categorical results were collated and analyzed to determine whether and under what circumstances eDNA outperforms traditional surveys.Results show that eDNA methods are cheaper, more sensitive, and detect more species than traditional methods. This is, however, taxa‐dependent, with amphibians having the highest potential for detection by eDNA survey. Perhaps most strikingly, of the 535 papers reviewed just 49 quantified the probability of detection for both eDNA and traditional survey methods and studies were three times more likely to give qualitative statements of performance. Synthesis and applications: The results of this meta‐analysis demonstrate that where there is a direct comparison, eDNA surveys of macro‐organisms are more accurate and efficient than traditional surveys. This conclusion, however, is based on just a fraction of available eDNA papers as most do not offer this granularity. We recommend that conclusions are substantiated with comparable and quantitative data. Where a direct comparison has not been made, we caution against the use of qualitative statements about relative performance. This consistency and rigor will simplify how the eDNA research community tracks methods‐based advances and will also provide greater clarity for conservation practitioners. To this end suggest reporting standards for eDNA studies., A meta‐analysis shows that environmental DNA outperforms traditional methods for biodiversity monitoring, both in terms of cost and efficiency.

The application of water, or water mixed with suppressants, to combat wildfires is one of the most common firefighting methods but is rarely studied for smouldering peat wildfire, which is the largest type of fire worldwide in term of fuel consumption. We performed experiments by spraying suppressant to the top of a burning peat sample inside a reactor. A plant-based wetting agent suppressant was mixed with water at three concentrations: 0% (pure water), 1% (low concentration), and 5% (high concentration), and delivered with varying flowrates. The results showed that suppression time decreased non-linearly with flow rate. The average suppression time for the low-concentration solution was 39% lower than with just water, while the high-concentration solution reduced suppression time by 26%. The volume of fluid that contributes to the suppression of peat in our experiments is fairly constant at 5.7 ± 2.1 L kg−1 peat despite changes in flow rate and suppressant concentration. This constant volume suggests that suppression time is the duration needed to flood the peat layer and that the suppressant acts thermally and not chemically. The results provide a better understanding of the suppression mechanism of peat fires and can improve firefighting and mitigation strategies.

Aim: Urbanization exposes species to novel ecological conditions. Some species thrive in urban areas, whereas many others are excluded from these human-made environments. Previous analyses suggest that the ability to cope with rapid environmental change is associated with long-term patterns of diversification, but whether the suite of traits associated with the ability to colonize urban environments is linked to this process remains poorly understood. Location: World. Time period: Current. Major taxa studied: Passerine birds. Methods: We applied macroevolutionary models to a large dataset of passerine birds to compare the evolutionary history of urban-tolerant species with that of urban-avoidant species. Specifically, we examined models of state-dependent speciation and extinction to assess the macroevolution of urban tolerance as a binary trait, in addition to models of quantitative trait-dependent diversification based on relative urban abundance. We also ran simulation-based model assessments to explore potential sources of bias. Results: We provide evidence that historically, species with traits promoting urban colonization have undergone faster diversification than urban-avoidant species, indicating that urbanization favours clades with a historical tendency towards rapid speciation or reduced extinction. In addition, we find that past transitions towards states that currently impede urban colonization by passerines have been more frequent than in the opposite direction. Furthermore, we find a portion of urban-avoidant passerines to be recent and to undergo fast diversification. All highly supported models give this result consistently. Main conclusions: Urbanization is mainly associated with the loss of lineages that are inherently more vulnerable to extinction over deep time, whereas cities tend to be colonized by less vulnerable lineages, for which urbanization might be neutral or positive in terms of longer-term diversification. Urban avoidance is associated with high rates of recent diversification for some clades occurring in regions with relatively intact natural ecosystems and low current levels of urbanization.

Highly competitive ephemeral resources like carrion tend to support much greater diversity relative to longer-lived resources. The coexistence of diverse communities on short-lived carrion is a delicate balance, maintained by several processes including competition. Despite this balance, few studies have investigated the effect of competition on carrion, limiting our understanding of how competition drives coexistence. We investigated how priority effects and larval density influence coexistence between two blowfly species, the facultative predator Chrysomya rufifacies and its competitor Calliphora stygia, which occupy broadly similar niches but differ in their ecological strategies for exploiting carrion. We examined how adult oviposition, larval survival, developmental duration, and adult fitness were affected by the presence of differently aged heterospecific larval masses, and how these measures varied under three larval densities. We found C. rufifacies larval survival was lowest in conspecific masses with low larval densities. In heterospecific masses, survival increased, particularly at high larval density, with priority effects having minimal effect, suggesting a dependency on collective exodigestion. For C. stygia, we found survival to be constant across larval densities in a conspecific mass. In heterospecific masses, survival decreased drastically when C. rufifacies arrived first, regardless of larval density, suggesting C. stygia is temporally constrained to avoid competition with C. rufifacies. Neither species appeared to completely outcompete the other, as they were either constrained by density requirements (C. rufifacies) or priority effects (C. stygia). Our results provide new mechanistic insights into the ecological processes allowing for coexistence on a competitively intense, ephemeral resource such as carrion.

Most insect communities are composed of evolutionary diverse lineages, but detailed phylogenetic analyses of whole communities are lacking, in particular in species-rich tropical faunas. Likewise, our knowledge of the Tree-of-Life to document life’s evolutionary diversity remains highly incomplete and especially requires the inclusion of unstudied lineages from species-rich ecosystems. Here we present the SITE-100 program, which is an attempt at building the Tree-of-Life from whole-community sampling of high-biodiversity sites around the globe. Combining the local data sets into a global tree produces an increasingly comprehensive tree, while also re-tracing evolutionary history of lineages constituting the local community. Local sets are collected in bulk in standardised passive traps and imaged with a large-scale high-resolution camera, which is followed by a parataxonomy step for the preliminary separation of morphospecies and selection of specimens for phylogenetic analysis. Selected specimens are used for individual DNA extraction and sequencing, usually to sequence mitochondrial genomes. All remaining specimens are bulk extracted and subjected to metabarcoding. Phylogenetic analysis on the mitogenomes produces a reference tree to which short barcode sequences are added in a secondary analysis using phylogenetic placement or backbone constrained tree searches. However, the approach may be hampered by the fact that (1) mitogenomes are limited in phylogeneticinformativeness, and (2) site-based sampling may produce poor taxon coverage and cause a suite of challenges for phylogenetic inference. To mitigate problems of phylogenetic reconstruction at deep levels, we follow a hierarchical way of gathering molecular information, where nuclear genome and mitogenome data from taxonomically chosen specimens consolidate the base and middle portion of the tree, respectively, and adding species-resolution contributed by DNA barcode data. We posit that site-based sampling, though not meeting the criterion of “taxon-completeness”, has great merits given preliminary studies showing representativeness and evenness of taxa sampled. We therefore argue in favour of site-based sampling as an unorthodox but logistically efficient way to construct large phylogenetic trees.

Spatial turnover of biological communities is determined by both dispersal and environmental constraints. However, we lack quantitative predictions about how these factors interact and influence turnover across genealogical scales. In this study, we have implemented a predictive framework based on approximate Bayesian computation (ABC) to quantify the signature of dispersal and environmental constraints in community turnover. First, we simulated the distribution of haplotypes, intra-specific lineages and species in biological communities under different strengths of dispersal and environmental constraints. Our simulations show that spatial turnover rate is invariant across genealogical scales when dispersal limitation determines the species ranges. However, when environmental constraint limits species ranges, spatial turnover rates vary across genealogical scales. These simulations were used in an ABC framework to quantify the role of dispersal and environmental constraints in 16 empirical biological communities sampled from local to continental scales, including several groups of insects (both aquatic and terrestrial), molluscs and bats. In seven datasets, the observed genealogical invariance of spatial turnover, assessed with distance–decay curves, suggests a dispersal-limited scenario. In the remaining datasets, the variance in distance–decay curves across genealogical scales was best explained by various combinations of dispersal and environmental constraints. Our study illustrates how modelling spatial turnover at multiple genealogical scales (species and intraspecific lineages) provides relevant insights into the relative role of dispersal and environmental constraints in community turnover., his research was supported by the Spanish Ministerio de Ciencia e Innovación and the European Regional Development Fund (ERDF) through grants CGL2016-76637-P and PID2020-112935GB-I000. DP and MA receive support from the Xunta de Galicia. MBA and MA acknowledge support from Ministerio de Ciencia, Innovación y Universidades through the grants PGC2018-099363-B-I00 and RYC-2015-18241, respectively.

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an un-supervised deep learning registration methodology which can accurately model affine and non-rigid trans-formations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse-consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent rep-resentations, while involves two factorised transformation networks and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named "FIRE") shows improved performances against the reference standard baseline method on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments., 38 pages, 7 figures, will be published in Sensors journal by MDPI

Tobias, Joseph A., Sheard, Catherine, Pigot, Alex L., Devenish, Adam J. M., Yang, Jingyi, Sayol, Ferran, Neate‐Clegg, Montague H. C., Alioravainen, Nico, Weeks, Thomas L., Barber, Robert A., Walkden, Patrick A., MacGregor, Hannah E. A., Jones, Samuel E. I., Vincent, Claire, Phillips, Anna G., Marples, Nicola M., Montaño‐Centellas, Flavia A., Leandro‐Silva, Victor, Claramunt, Santiago, Darski, Bianca, Freeman, Benjamin G., Bregman, Tom P., Cooney, Christopher R., Hughes, Emma C., Capp, Elliot J. R., Varley, Zoë K., Friedman, Nicholas R., Korntheuer, Heiko, Corrales‐Vargas, Andrea, Trisos, Christopher H., Weeks, Brian C., Hanz, Dagmar M., Töpfer, Till, Bravo, Gustavo A., Remeš, Vladimír, Nowak, Larissa, Carneiro, Lincoln S., Moncada R., Amilkar J., Matysioková, Beata, Baldassarre, Daniel T., Martínez‐Salinas, Alejandra, Wolfe, Jared D., Chapman, Philip M., Daly, Benjamin G., Sorensen, Marjorie C., Neu, Alexander, Ford, Michael A., Mayhew, Rebekah J., Fabio Silveira, Luis, Kelly, David J., Annorbah, Nathaniel N. D., Pollock, Henry S., Grabowska‐Zhang, Ada M., McEntee, Jay P., Carlos T. Gonzalez, Juan, Meneses, Camila G., Muñoz, Marcia C., Powell, Luke L., Jamie, Gabriel A., Matthews, Thomas J., Johnson, Oscar, Brito, Guilherme R. R., Zyskowski, Kristof, Crates, Ross, Harvey, Michael G., Jurado Zevallos, Maura, Hosner, Peter A., Bradfer‐Lawrence, Tom, Maley, James M., Stiles, F. Gary, Lima, Hevana S., Provost, Kaiya L., Chibesa, Moses, Mashao, Mmatjie, Howard, Jeffrey T., Mlamba, Edson, Chua, Marcus A. H., Li, Bicheng, Gómez, M. Isabel, García, Natalia C., Päckert, Martin, Fuchs, Jérôme, Ali, Jarome R., Derryberry, Elizabeth P., Carlson, Monica L., Urriza, Rolly C., Brzeski, Kristin E., Prawiradilaga, Dewi M., Rayner, Matt J., Miller, Eliot T., Bowie, Rauri C. K., Lafontaine, René‐Marie, Scofield, R. Paul, Lou, Yingqiang, Somarathna, Lankani, Lepage, Denis, Illif, Marshall, Neuschulz, Eike Lena, Templin, Mathias, Dehling, D. Matthias, Cooper, Jacob C., Pauwels, Olivier S. G., Analuddin, Kangkuso, Fjeldså, Jon, Seddon, Nathalie, Sweet, Paul R., DeClerck, Fabrice A. J., Naka, Luciano N., Brawn, Jeffrey D., Aleixo, Alexandre, Böhning‐Gaese, Katrin, Rahbek, Carsten, Fritz, Susanne A., Thomas, Gavin H., Schleuning, Matthias (2022): AVONET: morphological, ecological and geographical data for all birds. Ecology Letters 25 (3): 581-597, DOI: 10.1111/ele.13898, URL: http://dx.doi.org/10.1111/ele.13898

Reliable and accurate biodiversity census methods are essential for monitoring ecosystem health and assessing potential ecological impacts of future development projects. Although metabarcoding is increasingly used to study biodiversity across ecological research, morphology‐based identification remains the preferred approach for marine ecological impact assessments. Comparing metabarcoding to morphology‐based protocols currently used by ecological surveyors is essential to determine whether this DNA‐based approach is suitable for the long‐term monitoring of marine ecosystems. We compared metabarcoding and morphology‐based approaches for the analysis of invertebrates in low diversity intertidal marine sediment samples. We used a recently developed bioinformatics pipeline and two taxonomic assignment methods to resolve and assign amplicon sequence variants (ASVs) from Illumina amplicon data. We analysed the community composition recovered by both methods and tested the effects, on the levels of diversity detected by the metabarcoding method, of sieving samples prior to DNA extraction. Metabarcoding of the mitochondrial marker cytochrome c oxidase I (COI) gene recovers the presence of more taxonomic groups than the morphological approach. We found that sieving samples results in lower alpha diversity detected and suggests a community composition that differs significantly from that suggested by un‐sieved samples in our metabarcoding analysis. We found that whilst metabarcoding and morphological approaches detected similar numbers of species, they are unable to identify the same set of species across samples. Synthesis and Applications We show that metabarcoding using the cytochrome c oxidase I (COI) marker provides a more holistic, community‐based, analysis of benthic invertebrate diversity than a traditional morphological approach. We also highlight current gaps in reference databases and bioinformatic pipelines for the identification of intertidal benthic invertebrates that need to be addressed before metabarcoding can replace traditional methods. Ultimately, with these limitations taken into consideration, resolving community‐wide diversity patterns with metabarcoding could improve the management of non‐protected marine habitats in the U.K.

Neutral theory proposes that some macroscopic biodiversity patterns can be explained in terms of drift, speciation and immigration, without invoking niches. There are many different varieties of neutral model, all assuming that the fitness of an individual is unrelated to its species identity. Variants that are spatially explicit provide a means for making quantitative predictions about spatial biodiversity patterns. We present software packages that make spatially explicit neutral simulations straightforward and efficient. The packages allow the user to customize both dispersal and landscape structure in a wide variety of ways. We provide a Python package pycoalescence and a functionally equivalent R package rcoalescence. In both packages, the core routines are written in C++ and make use of coalescence methods to optimize performance. We explain the technical details of the packages and give examples for their application, with a particular focus on two scenarios of ecological and evolutionary interest: a landscape with habitat fragmentation, and an archipelago of islands. Spatially explicit neutral models represent an important tool in ecology for understanding the processes of biodiversity generation and predicting outcomes at large scales. The effort required to implement these complex spatially explicit simulations efficiently has thus far been a barrier to entry. Our packages increase the accessibility of these models and encourage further investigation of the primary mechanisms underpinning biodiversity.

Background Bacteroides thetaiotaomicron (Bt) is a prominent member of the human intestinal microbiota that, like all gram-negative bacteria, naturally generates nanosized outer membrane vesicles (OMVs) which bud off from the cell surface. Importantly, OMVs can cross the intestinal epithelial barrier to mediate microbe-host cell crosstalk involving both epithelial and immune cells to help maintain intestinal homeostasis. Here, we have examined the interaction between Bt OMVs and blood or colonic mucosa-derived dendritic cells (DC) from healthy individuals and patients with Crohn’s disease (CD) or ulcerative colitis (UC). Results In healthy individuals, Bt OMVs stimulated significant (p < 0.05) IL-10 expression by colonic DC, whereas in peripheral blood-derived DC they also stimulated significant (p < 0.001 and p < 0.01, respectively) expression of IL-6 and the activation marker CD80. Conversely, in UC Bt OMVs were unable to elicit IL-10 expression by colonic DC. There were also reduced numbers of CD103+ DC in the colon of both UC and CD patients compared to controls, supporting a loss of regulatory DC in both diseases. Furthermore, in CD and UC, Bt OMVs elicited a significantly lower proportion of DC which expressed IL-10 (p < 0.01 and p < 0.001, respectively) in blood compared to controls. These alterations in DC responses to Bt OMVs were seen in patients with inactive disease, and thus are indicative of intrinsic defects in immune responses to this commensal in inflammatory bowel disease (IBD). Conclusions Overall, our findings suggest a key role for OMVs generated by the commensal gut bacterium Bt in directing a balanced immune response to constituents of the microbiota locally and systemically during health which is altered in IBD patients.

To better predict how populations and communities respond to climatic temperature variation, it is necessary to understand how the shape of the response of fitness-related traits to temperature evolves (the thermal performance curve). Currently, there is disagreement about the extent to which the evolution of thermal performance curves is constrained. One school of thought has argued for the prevalence of thermodynamic constraints through enzyme kinetics, whereas another argues that adaptation can—at least partly—overcome such constraints. To shed further light on this debate, we perform a phylogenetic meta-analysis of the thermal performance curves of growth rate of phytoplankton—a globally important functional group—, controlling for environmental effects (habitat type and thermal regime). We find that thermodynamic constraints have a minor influence on the shape of the curve. In particular, we detect a very weak increase of maximum performance with the temperature at which the curve peaks, suggesting a weak “hotter-is-better” constraint. Also, instead of a constant thermal sensitivity of growth across species, as might be expected from strong constraints, we find that all aspects of the thermal performance curve evolve along the phylogeny. Our results suggest that phytoplankton thermal performance curves adapt to thermal environments largely in the absence of hard thermodynamic constraints.

Synthetic gene drive constructs are being developed to control disease vectors, invasive species, and other pest species. In a well-mixed random mating population a sufficiently strong gene drive is expected to eliminate a target population, but it is not clear whether the same is true when spatial processes play a role. In species with an appropriate biology it is possible that drive-induced reductions in density might lead to increased inbreeding, reducing the efficacy of drive, eventually leading to suppression rather than elimination, regardless of how strong the drive is. To investigate this question we analyse a series of explicitly solvable stochastic models considering a range of scenarios for the relative timing of mating, reproduction, and dispersal and analyse the impact of two different types of gene drive, a Driving Y chromosome and a homing construct targeting an essential gene. We find in all cases a sufficiently strong Driving Y will go to fixation and the population will be eliminated, except in the one life history scenario (reproduction and mating in patches followed by dispersal) where low density leads to increased inbreeding, in which case the population persists indefinitely, tending to either a stable equilibrium or a limit cycle. These dynamics arise because Driving Y males have reduced mating success, particularly at low densities, due to having fewer sisters to mate with. Increased inbreeding at low densities can also prevent a homing construct from eliminating a population. For both types of drive, if there is strong inbreeding depression, then the population cannot be rescued by inbreeding and it is eliminated. These results highlight the potentially critical role that low-density-induced inbreeding and inbreeding depression (and, by extension, other sources of Allee effects) can have on the eventual impact of a gene drive on a target population.

AIM: The aim was to identify the primary drivers of compositional change in breeding bird assemblages over a 40-year period. LOCATION: Britain. TIME PERIOD: From 1970 to 2010. MAJOR TAXA STUDIED: Birds. METHODS: Using morphological trait measurements and a dataset of presence-absence data for British breeding birds surveyed in 10 km × 10 km hectads during two time periods, we calculated temporal taxonomic and functional beta diversity for each hectad alongside the change in species richness, mean nearest taxon distance (MNTD) and mean pairwise distance (MPD). We also estimated potential drivers of beta diversity, including climatic and land-use and land-cover (LULC) change variables, elevation and assemblage species richness in 1970 (1970rich). We used random forest regressions to test which variables best explained compositional change in the assemblages. We also assessed spatial taxonomic and functional change by analysing multiple-site beta diversity and pairwise dissimilarities between time periods. RESULTS: Initial (1970) species richness was the most important predictor (highest importance score) across all models, with areas characterized by higher initial richness experiencing less assemblage change overall. The coordinates included to capture spatial autocorrelation in the data were also important predictors of change. Most cli-mate and LULC variables had relatively low explanatory power; elevation and average temperature were the most influential. All metrics increased slightly with increasing elevation, except for species richness change and MPD, which decreased. MAIN CONCLUSIONS: The composition of British breeding bird assemblages changed substantially between 1970 and 2010. Spatial heterogeneity increased, both taxonomically and functionally. We show evidence that hectads with larger assemblages have been buffered from temporal diversity change and that those at higher elevations changed more in composition than those at lower elevations. Overall, coarse-resolution climate and LULC explained only small to moderate amounts of variation, suggesting that stochastic assembly change or finer-scale drivers might be drivers of temporal changes in assemblage composition. British Trust for Ornithology (BTO). info:eu-repo/semantics/publishedVersion

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

AimUnderstanding connections between environment and biodiversity is crucial for conservation, identifying causes of ecosystem stress, and predicting population responses to changing environments. Explaining biodiversity requires an understanding of how species richness and environment co-vary across scales. Here, we identify scales and locations at which biodiversity is generated and correlates with environment.LocationFull latitudinal range per continent.Time periodPresent-day.Major taxa studiedTerrestrial vertebrates: all mammals, carnivorans, bats, songbirds, humming-birds, amphibians.MethodsWe describe the use of wavelet power spectra, cross-power and coherence for identifying scale-dependent trends across Earth’s surface. Spectra reveal scale- and location-dependent coherence between species richness and topography (E), mean annual precipitation (Pn), temperature (Tm) and annual temperature range (∆T).Results>97% of species richness of taxa studied is generated at large scales, i.e. wavelengths 103km, with 30–69% generated at scales 104km. At these scales, richness tends to be highly coherent and anti-correlated withEand ∆T, and positively correlated withPnandTm. Coherence between carnivoran richness and ∆Tis low across scales, implying insensitivity to seasonal temperature variations. Conversely, amphibian richness is strongly anti-correlated with ∆Tat large scales. At scales 103km, examined taxa, except carnivorans, show highest richness within the tropics. Terrestrial plateaux exhibit high coherence between carnivorans andEat scales∼103km, consistent with contribution of large-scale tectonic processes to biodiversity. Results are similar across different continents and for global latitudinal averages. Spectral admittance permits derivation of rules-of-thumb relating long-wavelength environmental and species richness trends.Main conclusionsSensitivities of mammal, bird and amphibian populations to environment are highly scale-dependent. At large scales, carnivoran richness is largely independent of temperature and precipitation, whereas amphibian richness correlates strongly with precipitation and temperature, and anti-correlates with temperature range. These results pave the way for spectral-based calibration of models that predict biodiversity response to climate change scenarios.

Diptera is often considered to be the richest insect group due to its great species diversity and broad ecological versatility. However, data on dipteran diversity from subtropical ecosystems have hitherto been scarce, due to the lack of studies conducted at an appropriate large scale. We investigated the diversity and composition of Diptera communities on Tianmu Mountain, Zhejiang, China, using DNA metabarcoding technology, and evaluated their dynamic responses to the effects of slope aspect, season, and altitudinal zone. A total of 5,092 operational taxonomic units (OTUs) were discovered and tentatively assigned to 72 dipteran families, including 2 family records new for China and 30 family records new for the locality. Cecidomyiidae, Sciaridae, and Phoridae were the predominant families, representing 53.6% of total OTUs, while 52 families include >95% unidentified and presumed undescribed species. We found that the community structure of Diptera was significantly affected by aspect, seasonality (month) and elevation, with richer diversity harbored in north‐facing than south‐facing slopes, and seasonality a more profound driver of community structure and diversity than elevation. Overall, massive species diversity of Diptera communities was discovered in this subtropical ecosystem of east China. The huge diversity of potentially undescribed species only revealed by metabarcoding now requires more detailed taxonomic study, as a step toward an evolutionary integration that accumulates information on species’ geographic ranges, ecological traits, functional roles, and species interactions, and thus places the local communities in the context of the growing knowledge base of global biodiversity and its response to environmental change., Using DNA metabarcoding technology, we investigated the diversity of Diptera communities of a subtropical ecosystem of Tianmu Mountain in eastern China. The result demonstrates the extraordinary species (5092 OTUs) and phylogenetic (72 families) diversity of Diptera, particularly in Cecidomyiidae, Sciaridae, and Phoridae that combined represented 53.6% of total OTUs, while 52 families each contained more than 95% unknown or unidentifiable species, and many more species than recognized at these sites in morphology‐based studies (“excess taxa”). The community structure of Diptera was significantly affected by aspect, seasonality, and, to a lesser extent, elevation.

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Large archaeological wooden structures are potentially at risk of structural failure through deformation and cracking over time if they are left untreated and their structural health is not maintained. This could be in part due to, for example, the shrinkage of waterlogged wood as it dries, or time-dependent creep processes. These dimensional changes are accompanied by associated stresses. However, there are few studies analysing the movement of large wooden structures in real time as they dry, particularly after their conservation treatment. This paper follows the structural monitoring of the Mary Rose from after the conservation treatment, where it was sprayed with polyethylene glycol, through to the ship’s air-drying process and beyond to assess the effects that drying has had on the displacement of the timbers. A laser-based target system was used to collect displacement data between 2013 and 2020 and the data showed a significant slowing of displacement as the drying reached an equilibrium.

With the ever-increasing advancements in the technology of driver assistant systems, there is a need for a comprehensive way to identify traffic conflicts to avoid collisions. Although significant research efforts have been devoted to traffic conflict techniques applied for junctions, there is dearth of research on these methods for motorways. This paper presents the validation of a traffic conflict prediction algorithm applied to a motorway scenario in a simulated environment. An automatic video analysis system was developed to identify lane change and rear-end conflicts as ground truth. Using these conflicts, the prediction ability of the traffic conflict technique was validated in an integrated simulation framework. This framework consisted of a sub-microscopic simulator, which provided an appropriate testbed to accurately simulate the components of an intelligent vehicle, and a microscopic traffic simulator able to generate the surrounding traffic. Results from this framework show that for a 10% false alarm rate, approximately 80% and 73% of rear-end and lane change conflicts were accurately predicted, respectively. Despite the fact that the algorithm was not trained using the virtual data, the sensitivity was high. This highlights the transferability of the algorithm to similar road networks, providing a benchmark for the identification of traffic conflict and a relevant step for developing safety management strategies for autonomous vehicles.

Large-seeded, animal-dispersed (LSAD) trees include some of the most valuable and threatened species in the tropics, but they are chronically underrepresented in regenerating forests. Toucans disperse many LSAD species, so attracting toucans to regenerating forests should help re-establish more diverse tree communities. We ask: (1) What constitutes suitable toucan habitat in premontane southern Costa Rica? (2) How much do small-scale restoration strategies influence toucan visitation compared to landscape-scale habitat suitability outside of restoration sites? (3) How well does toucan visitation predict the richness of LSAD tree species recruiting into regenerating forests? We combined habitat suitability models with long-term toucan observations and comprehensive tree recruitment surveys to assess these questions in a multi-site forest restoration experiment. Restoration treatments included tree plantations, natural regeneration, and applied nucleation. Habitat suitability obtained by modeling for three sympatric toucan species was predicted by elevation and the extent and age of landscape forest cover. Within suitable landscapes, toucans visited areas restored via tree planting ≥5 yr sooner and ≥2× more often than plots restored via natural regeneration. Tree plantations in suitable toucan habitat at the landscape scale had LSAD tree recruitment communities that were 2–3× richer in species than plantations in poor toucan habitat, and 71% (15/21) of all recruiting LSAD tree species were found only in plantations where landscape habitat was suitable for the largest toucan,Ramphastos ambiguus. Results support a multi-spatial-scale model for predicting toucan-mediated dispersal of LSAD trees. Tree planting increases toucan visitation and LSAD tree recruitment, but only within landscapes that represent suitable toucan habitat. More broadly, habitat suitability modeling for key seed dispersers can help prioritize restoration actions within heterogenous landscapes.

BackgroundObesity and vaginal microbiome (VMB) dysbiosis are each risk factors for adverse reproductive and oncological health outcomes in women. Here, we investigated the relationship between obesity, vaginal bacterial composition, local inflammation and bariatric surgery.MethodsVaginal bacterial composition assessed by high-throughput sequencing of bacterial 16S rRNA genes and local cytokine levels measured using a multiplexed Magnetic Luminex Screening Assay were compared between 67 obese and 42 non-obese women. We further assessed temporal changes in the microbiota and cytokines in a subset of 27 women who underwent bariatric surgery.ResultsThe bacterial component of the vaginal microbiota in obese women was characterised by a lower prevalence of aLactobacillus-dominant VMB and higher prevalence of a high diversity (Lactobacillusspp., andGardnerella- spp. depleted) VMB, compared with non-obese subjects (pDialisterspecies (pAnaerococcus vaginalis(p=0.021), andPrevotella timonensis(p=0.020) and decreased relative abundance ofLactobacillus crispatus(p=0.014). Local vaginal IL-1β, IL-4, IL-6, IL-8, IFNγ, MIP-1α and TNFα levels were all higher among obese women, however, only IL-1β and IL-8 correlated with VMB species diversity. In a subset of obese women undergoing bariatric surgery, there were no significant overall differences in VMB following surgery; however, 75% of these women remained obese at 6 months. Prior to surgery, there was no relationship between body mass index (BMI) and VMB structure; however, post-surgery women with aLactobacillus-dominant VMB had a significantly lower BMI than those with a high diversity VMB.ConclusionsObese women have a significantly different vaginal microbiota composition with increased levels of local inflammation compared to non-obese women. Bariatric surgery does not change the VMB; however, those with the greatest weight loss 6-month post-surgery are most likely to have aLactobacillus-dominant VMB.

Background Communication between the gut microbiota and the brain is primarily mediated via soluble microbe-derived metabolites, but the details of this pathway remain poorly defined. Methylamines produced by microbial metabolism of dietary choline and l-carnitine have received attention due to their proposed association with vascular disease, but their effects upon the cerebrovascular circulation have hitherto not been studied. Results Here, we use an integrated in vitro/in vivo approach to show that physiologically relevant concentrations of the dietary methylamine trimethylamine N-oxide (TMAO) enhanced blood-brain barrier (BBB) integrity and protected it from inflammatory insult, acting through the tight junction regulator annexin A1. In contrast, the TMAO precursor trimethylamine (TMA) impaired BBB function and disrupted tight junction integrity. Moreover, we show that long-term exposure to TMAO protects murine cognitive function from inflammatory challenge, acting to limit astrocyte and microglial reactivity in a brain region-specific manner. Conclusion Our findings demonstrate the mechanisms through which microbiome-associated methylamines directly interact with the mammalian BBB, with consequences for cerebrovascular and cognitive function.

Background There is increasing evidence that the airway microbiome plays a key role in the establishment of respiratory health by interacting with the developing immune system early in life. While it has become clear that bacteria are involved in this process, there is a knowledge gap concerning the role of fungi. Moreover, the inter-kingdom interactions that influence immune development remain unknown. In this prospective exploratory human study, we aimed to determine early post-natal microbial and immunological features of the upper airways in 121 healthy newborns. Results We found that the oropharynx and nasal cavity represent distinct ecological niches for bacteria and fungi. Breastfeeding correlated with changes in microbiota composition of oropharyngeal samples with the greatest impact upon the relative abundance of Streptococcus species and Candida. Host transcriptome profiling revealed that genes with the highest expression variation were immunological in nature. Multi-omics factor analysis of host and microbial data revealed unique co-variation patterns. Conclusion These data provide evidence of a diverse multi-kingdom microbiota linked with local immunological characteristics in the first week of life that could represent distinct trajectories for future respiratory health. Trial registration NHS Health Research Authority, IRAS ID 199053. Registered 5 Oct 2016. https://www.hra.nhs.uk/planning-and-improving-research/application-summaries/research-summaries/breathing-together/

Background: Trophic shifts from one dietary niche to another have played major roles in reshaping the evolutionary trajectories of a wide range of vertebrate groups, yet their consequences for morphological disparity and species diversity differ among groups. Methods: Here, we use phylogenetic comparative methods to examine whether the evolution of nectarivory and other trophic shifts have driven predictable evolutionary pathways in Australasian psittaculid parrots in terms of ecological traits such as body size, beak shape, and dispersal capacity. Results: We found no evidence for an ‘early-burst’ scenario of lineage or morphological diversification. The bestfitting models indicate that trait evolution in this group is characterized by abrupt phenotypic shifts (evolutionary jumps), with no sign of multiple phenotypic optima correlating with different trophic strategies. Thus, our results point to the existence of weak directional selection and suggest that lineages may be evolving randomly or slowly toward adaptive peaks they have not yet reached. Conclusions: This study adds to a growing body of evidence indicating that the relationship between avian morphology and feeding ecology may be more complex than usually assumed and highlights the importance of adding more flexible models to the macroevolutionary toolbox

Research into the relationship between ecosystem services and human well-being, including poverty alleviation, has blossomed. However, little is known about who has produced this knowledge, what collaborative patterns and institutional and funding conditions have underpinned it, or what implications these matters may have. To investigate the potential implications of such production for conservation science and practice, we address this by developing a social network analysis of the most prolific writers in the production of knowledge about ecosystem services and poverty alleviation. We show that 70% of these authors are men, most are trained in either the biological sciences or economics and almost none in the humanities. Eighty per cent of authors obtained their PhD from universities in the EU or the USA, and they are currently employed in these regions. The co-authorship network is strongly collaborative, without dominant authors, and with the top 30 most cited scholars being based in the USA and co-authoring frequently. These findings suggest, firstly, that the production of knowledge on ecosystem services and poverty alleviation research has the same geographical and gender biases that characterize knowledge production in other scientific areas and, secondly, that there is an expertise bias that also characterizes other environmental matters. This is despite the fact that the research field of ecosystem services and poverty alleviation, by its nature, requires a multidisciplinary lens. This could be overcome through promoting more extensive collaboration and knowledge co-production.

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, ‘Corner’, has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation.

Habitat fragmentation has become one of the largest areas of research in conservation biology. Empirical studies into habitat fragmentation impacts typically measure ecological responses to metrics describing fragmentation processes, for example ‘distance to the nearest forest edge’, ‘forest fragment area’ and ‘landscape habitat amount’. However, these studies often fail to sample across representative ranges of fragmentation metrics characterising the study region. They therefore lack the data to account for correlation among multiple fragmentation metrics and for spatial autocorrelation among sample sites, which reduces the strength of derived predictive models. Here, we draw on approaches used in the mining and soil science industry to develop standardised and repeatable protocols for designing optimised sampling schemes of biodiversity in fragmented landscapes that meet three criteria: the distance between sample sites is maximised to reduce spatial autocorrelation, the full range of values of the metrics of interest are sampled and the confounding effects of correlated metrics are minimised. We show that our computational methods can optimise the placement of sample sites in fragmented landscapes to minimise, and in some cases to entirely avoid, over- or under-sampling of fragmentation metrics. Our method is flexible enough to cater for any continuous (e.g. maps of percentage tree cover) or categorical (e.g. habitat and land use types) fragmentation metric, and to simultaneously handle combinations of multiple fragmentation metrics and habitat types. We implement our methods as open-source code which includes options to mask invalid or inaccessible regions, update designs to adapt to unforeseen constraints in the field and suggest optimal numbers of sample sites for given design criteria. Using a case study landscape, we demonstrate how the approach improves on manually generated sampling designs. We also show that the methods are flexible enough to be applied to landscape studies beyond the field of habitat fragmentation. We introduce our package as a novel research tool that is able to streamline the experimental design process for biodiversity sampling and monitoring at landscape scales, leading to improved data quality and representativeness.