Forest top canopy bacterial communities are influenced by elevation and host tree traits.

BACKGROUND: The phyllosphere microbiome is crucial for plant health and ecosystem functioning. While host species play a determining role in shaping the phyllosphere microbiome, host trees of the same species that are subjected to different environmental conditions can still exhibit large degrees of variation in their microbiome diversity and composition. Whether these intra-specific variations in phyllosphere microbiome diversity and composition can be observed over the broader expanse of forest landscapes remains unclear. In this study, we aim to assess the variation in the top canopy phyllosphere bacterial communities between and within host tree species in the temperate European forests, focusing on Fagus sylvatica (European beech) and Picea abies (Norway spruce). RESULTS: We profiled the bacterial diversity, composition, driving factors, and discriminant taxa in the top canopy phyllosphere of 211 trees in two temperate forests, Veluwe National Parks, the Netherlands and Bavarian Forest National Park, Germany. We found the bacterial communities were primarily shaped by host species, and large variation existed within beech and spruce. While we showed that there was a core microbiome in all tree species examined, community composition varied with elevation, tree diameter at breast height, and leaf-specific traits (e.g., chlorophyll and P content). These driving factors of bacterial community composition also correlated with the relative abundance of specific bacterial families. CONCLUSIONS: While our results underscored the importance of host species, we demonstrated a substantial range of variation in phyllosphere bacterial diversity and composition within a host species. Drivers of these variations have implications at both the individual host tree level, where the bacterial communities differed based on tree traits, and at the broader forest landscape level, where drivers like certain highly plastic leaf traits can potentially link forest canopy bacterial community variations to forest ecosystem processes. We eventually showed close associations between forest canopy phyllosphere bacterial communities and host trees exist, and the consistent patterns emerging from these associations are critical for host plant functioning.

High-throughput Soxhlet extraction method applied for analysis of leaf lignocellulose and non-structural substances.

The traditional Soxhlet extraction method is commonly employed to extract soluble components from non-soluble components in a solid matrix, for example, non-structural substances in biomass samples that can be separated from structural lignocellulosic compounds in biomass samples. Conventional laboratory procedures for such extractions typically involve a low sample throughput, with each run being performed individually, resulting in time-consuming and labour-intensive processes, making them impractical for analysing large sample sets. In research fields such as Earth Observation in Forest Ecosystems, extensive fieldwork sampling is required across large study areas, resulting in a substantial number of leaf samples, each with limited mass. In this study, an innovative adaptation of the conventional National Renewable Energy Laboratory (NREL) Soxhlet method is developed to create a high-throughput mini-Soxhlet apparatus that enables the simultaneous extraction of up to nineteen samples, each with a mass of 0.3 g per sample. With this adaptation, we measured the lignocellulose and extractive in 343 leaf samples collected from four temperate forest tree species. This modified approach enhances versatility and can be applied to all solid-liquid extractions and various types of vegetation tissues, such as tree leaves, shrubs, crops, feedstock, and other non-woody samples.•The solid-liquid extraction method has been implemented in a heating block facilitating 19 small flasks to measure multiple samples simultaneously while requiring only a small sample mass.•The apparatus set-up was constructed using an alumina heating block mounted on a standard laboratory heating plate. Boiling flask tubes were placed in the heating block and equipped with condenser caps and filters on glass rods on which the solid samples were placed.•The adjustments made the method suitable for application to diverse vegetation tissues and non-woody sample types. It holds particular appeal for research areas that necessitate a high sample number.

Habitat visibility affects the behavioral response of a large herbivore to human disturbance in forest landscapes.

Wildlife can perceive humans as predators and human disturbance, whether lethal (e.g., hunting) or non-lethal (e.g., hiking, biking, and skiing), triggers antipredator behavior among prey. Visibility is the property that relates habitat structure to accessibility of visual information that allows animals to detect predators and evaluate predation risk. Thus, the visibility of a habitat (hereafter referred to as habitat visibility) for prey species alters the perceived risk of predation and therefore has a strong influence on their antipredator behavior. Yet, knowledge of how habitat visibility affects the response of animals to different types of human disturbance is limited, partly, because it is challenging to measure habitat visibility for animals at a fine spatial scale over a landscape, particularly in highly heterogeneous landscapes (e.g., forests). In this study, we employed a newly described approach that combines terrestrial and airborne LiDAR to contiguously measure fine-scale habitat visibility in a forested landscape. We applied the LiDAR-derived habitat visibility to examine how habitat visibility in forests affects the summer space use of 20 GPS-collared female red deer (Cervus elaphus) modeled with integrated step-selection functions in the Bavarian Forest National Park, Germany when exposed to various types of human disturbance including recreational activities, forest roads, hiking trails, and hunting. We found that red deer in our study area avoided areas with higher all types of human disturbance, especially during daylight hours. Furthermore, habitat visibility significantly modified the use of space by red deer in response to human recreational activities, forest roads, and hiking trails, but not to the hunting area. Red deer tended to tolerate a higher intensity of human recreational activities and to use areas closer to forest roads or hiking trails when they have lower habitat visibility (i.e., more cover). Our findings highlight the importance of considering visual perception when studying the response of wild animals to human disturbance. We emphasize the potential to mitigate negative consequences of human disturbance on wildlife, through measures such as maintaining vegetative buffers around recreational infrastructure (e.g., roads and skiing tracks) in order to reduce habitat visibility around areas frequented by humans.

Evidence-based advice on timing and location of tsetse control measures in Shimba Hills National reserve, Kenya.

Controlling tsetse flies is critical for effective management of African trypanosomiasis in Sub-Saharan Africa. To enhance timely and targeted deployment of tsetse control strategies a better understanding of their temporal dynamics is paramount. A few empirical studies have explained and predicted tsetse numbers across space and time, but the resulting models may not easily scale to other areas. We used tsetse catches from 160 traps monitored between 2017 and 2019 around Shimba Hills National Reserve in Kenya, a known tsetse and trypanosomiasis hotspot. Traps were divided into two groups: proximal (<1.0 km)) to and distant (> 1.0 km) from the outer edge of the reserve boundary. We fitted zero-inflated Poisson and generalized linear regression models for each group using as temporal predictors rainfall, NDVI (Normalized Difference Vegetation Index), and LST (land surface temperature). For each predictor, we assessed their relationship with tsetse abundance using time lags from 10 days up to 60 days before the last tsetse collection date of each trap. Tsetse numbers decreased as distance from the outside of reserve increased. Proximity to croplands, grasslands, woodlands, and the reserve boundary were the key predictors for proximal traps. Tsetse numbers rose after a month of increased rainfall and the following increase in NDVI values but started to decline if the rains persisted beyond a month for distant traps. Specifically, tsetse flies were more abundant in areas with NDVI values greater than 0.7 for the distant group. The study suggests that tsetse control efforts beyond 1.0 km of the reserve boundary should be implemented after a month of increased rains in areas having NDVI values greater than 0.7. To manage tsetse flies effectively within a 1.0 km radius of the reserve boundary, continuous measures such as establishing an insecticide-treated trap or target barrier around the reserve boundary are needed.

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species.

Visibility (viewshed) plays a significant and diverse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine-scale visibility on animal behaviour has been impeded or limited. In this study, we demonstrate the application of the concept of three-dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne light detection and ranging (LiDAR) to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS-collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer. We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight. For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine-scale visibility by a forest-dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour.

The life cycle of the alphaviruses: From an antiviral perspective.

The alphaviruses are a widely distributed group of positive-sense, single stranded, RNA viruses. These viruses are largely arthropod-borne and can be found on all populated continents. These viruses cause significant human disease, and recently have begun to spread into new populations, such as the expansion of Chikungunya virus into southern Europe and the Caribbean, where it has established itself as endemic. The study of alphaviruses is an active and expanding field, due to their impacts on human health, their effects on agriculture, and the threat that some pose as potential agents of biological warfare and terrorism. In this systematic review we will summarize both historic knowledge in the field as well as recently published data that has potential to shift current theories in how alphaviruses are able to function. This review is comprehensive, covering all parts of the alphaviral life cycle as well as a brief overview of their pathology and the current state of research in regards to vaccines and therapeutics for alphaviral disease.

The Spectral Species Concept in Living Color.

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

50 Years of Cumulative Open-Source Data Confirm Stable and Robust Biodiversity Distribution Patterns for Macrofungi.

Fungi are a hyper-diverse kingdom that contributes significantly to the regulation of the global carbon and nutrient cycle. However, our understanding of the distribution of fungal diversity is often hindered by a lack of data, especially on a large spatial scale. Open biodiversity data may provide a solution, but concerns about the potential spatial and temporal bias in species occurrence data arising from different observers and sampling protocols challenge their utility. The theory of species accumulation curves predicts that the cumulative number of species reaches an asymptote when the sampling effort is sufficiently large. Thus, we hypothesize that open biodiversity data could be used to reveal large-scale macrofungal diversity patterns if these datasets are accumulated long enough. Here, we tested our hypothesis with 50 years of macrofungal occurrence records in Norway and Sweden that were downloaded from the Global Biodiversity Information Facility (GBIF). We first grouped the data into five temporal subsamples with different cumulative sampling efforts (i.e., accumulation of data for 10, 20, 30, 40 and 50 years). We then predicted the macrofungal diversity and distribution at each subsample using the maximum entropy (MaxEnt) species distribution model. The results revealed that the cumulative number of macrofungal species stabilized into distinct distribution patterns with localized hotspots of predicted macrofungal diversity with sampling efforts greater than approximately 30 years. Our research demonstrates the utility and importance of the long-term accumulated open biodiversity data in studying macrofungal diversity and distribution at the national level.

Inactivation of Venezuelan Equine Encephalitis Virus Genome Using Two Methods.

Venezuelan equine encephalitis virus (VEEV) is an Alphavirus in the Togaviridae family of positive-strand RNA viruses. The viral genome of positive-strand RNA viruses is infectious, as it produces infectious virus upon introduction into a cell. VEEV is a select agent and samples containing viral RNA are subject to additional regulations due to their infectious nature. Therefore, RNA isolated from cells infected with BSL-3 select agent strains of VEEV or other positive-strand viruses must be inactivated before removal from high-containment laboratories. In this study, we tested the inactivation of the viral genome after RNA fragmentation or cDNA synthesis, using the Trinidad Donkey and TC-83 strains of VEEV. We successfully inactivated VEEV genomic RNA utilizing these two protocols. Our cDNA synthesis method also inactivated the genomic RNA of eastern and western equine encephalitis viruses (EEEV and WEEV). We also tested whether the purified VEEV genomic RNA can produce infectious virions in the absence of transfection. Our result showed the inability of the viral genome to cause infection without being transfected into the cells. Overall, this work introduces RNA fragmentation and cDNA synthesis as reliable methods for the inactivation of samples containing the genomes of positive-strand RNA viruses.

Linking the past and present to predict the distribution of Asian crested ibis (Nipponia nippon) under global changes.

Understanding how species' ecological niches adapt to environmental changes through time is critical for predicting the effect of future global change on endangered species. Yet few studies have incorporated knowledge of past niche shifting into the assessment of species' future fate in a changing world. In this study, we integrated the ecological niche dynamics into the species distribution modeling of the Asian crested ibis (Nipponia nippon) in East Asia. Specifically, we compared historical and present ecological niches of crested ibis in four-dimensional environmental space based on species occurrence and environmental data. We then employed a multi-temporal ecological niche model to estimate the potential geographical distribution of crested ibis under future climate and land-use changes. Our results show that crested ibis retained similar though not identical ecological niches over time. Compared to the historical baseline range, the current suitable habitat for crested ibis has been reduced by 39.6%. The effects of human activity outweigh those of climate change regarding the distribution of crested ibis. We conclude that the ecological niche of crested ibis was tended to be conservative, and future potentially suitable habitat may encounter northeastward and northwestward shift, and possibly expand by 18.7% referred to the historical range. The findings of our study are of clear importance for the conservation and successful reintroduction of crested ibis in East Asia.

Satellite-based modelling of potential tsetse (Glossina pallidipes) breeding and foraging sites using teneral and non-teneral fly occurrence data.

BACKGROUND: African trypanosomiasis, which is mainly transmitted by tsetse flies (Glossina spp.), is a threat to public health and a significant hindrance to animal production. Tools that can reduce tsetse densities and interrupt disease transmission exist, but their large-scale deployment is limited by high implementation costs. This is in part limited by the absence of knowledge of breeding sites and dispersal data, and tools that can predict these in the absence of ground-truthing. METHODS: In Kenya, tsetse collections were carried out in 261 randomized points within Shimba Hills National Reserve (SHNR) and villages up to 5 km from the reserve boundary between 2017 and 2019. Considering their limited dispersal rate, we used in situ observations of newly emerged flies that had not had a blood meal (teneral) as a proxy for active breeding locations. We fitted commonly used species distribution models linking teneral and non-teneral tsetse presence with satellite-derived vegetation cover type fractions, greenness, temperature, and soil texture and moisture indices separately for the wet and dry season. Model performance was assessed with area under curve (AUC) statistics, while the maximum sum of sensitivity and specificity was used to classify suitable breeding or foraging sites. RESULTS: Glossina pallidipes flies were caught in 47% of the 261 traps, with teneral flies accounting for 37% of these traps. Fitted models were more accurate for the teneral flies (AUC = 0.83) as compared to the non-teneral (AUC = 0.73). The probability of teneral fly occurrence increased with woodland fractions but decreased with cropland fractions. During the wet season, the likelihood of teneral flies occurring decreased as silt content increased. Adult tsetse flies were less likely to be trapped in areas with average land surface temperatures below 24 °C. The models predicted that 63% of the potential tsetse breeding area was within the SHNR, but also indicated potential breeding pockets outside the reserve. CONCLUSION: Modelling tsetse occurrence data disaggregated by life stages with time series of satellite-derived variables enabled the spatial characterization of potential breeding and foraging sites for G. pallidipes. Our models provide insight into tsetse bionomics and aid in characterising tsetse infestations and thus prioritizing control areas.

Efficacy of statistical process control procedures to identify deviations in continuously measured physiological and behavioral variables in beef heifers resulting from an experimentally combined viral-bacterial challenge.

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with remote continuous data collection could accurately differentiate between animals experimentally inoculated with a viral-bacterial (VB) challenge or phosphate buffer solution (PBS). Crossbred heifers (N = 38; BW = 230 ± 16.4 kg) were randomly assigned to treatments by initial weight, average daily gain (ADG), bovine herpes virus 1, and Mannheimia haemolytica serum titers. Feeding behavior, dry matter intake (DMI), animal activity, and rumen temperature were continuously monitored remotely prior to and following VB challenge. VB-challenged heifers exhibited decreased (P < 0.01) ADG and DMI, as well as increased (P < 0.01) neutrophils and rumen temperature consistent with a bovine respiratory disease (BRD) infection. However, none of the heifers displayed overt clinical signs of disease. Shewhart and cumulative summation (CUSUM) charts were evaluated, with sensitivity and specificity computed on the VB-challenged heifers (n = 19) and PBS-challenged heifers (n = 19), respectively, and the accuracy was determined as the average of sensitivity and specificity. To address the diurnal nature of rumen temperature responses, summary statistics (mean, minimum, and maximum) were computed for daily quartiles (6-h intervals), and these quartile temperature models were evaluated separately. In the Shewhart analysis, DMI was the most accurate (95%) at deciphering between PBS- and VB-challenged heifers, followed by rumen temperature (94%) collected in the 2nd and 3rd quartiles. Rest was most the accurate accelerometer-based traits (89%), and meal duration (87%) and bunk visit (BV) frequency (82%) were the most accurate feeding behavior traits. Rumen temperature collected in the 3rd quartile signaled the earliest (2.5 d) of all the variables monitored with the Shewhart, followed by BV frequency (2.8 d), meal duration (2.8 d), DMI (3.0 d), and rest (4.0 d). Rumen temperature and DMI remained the most accurate variables in the CUSUM at 80% and 79%, respectively. Meal duration (58%), BV frequency (71%), and rest (74%) were less accurate when monitored with the CUSUM analysis. Furthermore, signal day was greater for DMI, rumen temperature, and meal duration (4.4, 5.0, and 3.7 d, respectively) in the CUSUM compared to Shewhart analysis. These results indicate that Shewhart and CUSUM charts can effectively identify deviations in feeding behavior, activity, and rumen temperature patterns for the purpose of detecting sub-clinical BRD in beef cattle.

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

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

Priority list of biodiversity metrics to observe from space.

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.

Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea.

Perturbations in early life gut microbiota can have long-term impacts on host health. In this study, we investigated antimicrobial-induced temporal changes in diversity, stability, and compositions of gut microbiota in neonatal veal calves, with the objective of identifying microbial markers that predict diarrhea. A total of 220 samples from 63 calves in first 8 weeks of life were used in this study. The results suggest that increase in diversity and stability of gut microbiota over time was a feature of "healthy" (non-diarrheic) calves during early life. Therapeutic antimicrobials delayed the temporal development of diversity and taxa-function robustness (a measure of microbial stability). In addition, predicted genes associated with beta lactam and cationic antimicrobial peptide resistance were more abundant in gut microbiota of calves treated with therapeutic antimicrobials. Random forest machine learning algorithm revealed that Trueperella, Streptococcus, Dorea, uncultured Lachnospiraceae, Ruminococcus 2, and Erysipelatoclostridium may be key microbial markers that can differentiate "healthy" and "unhealthy" (diarrheic) gut microbiota, as they predicted early life diarrhea with an accuracy of 84.3%. Our findings suggest that diarrhea in veal calves may be predicted by the shift in early life gut microbiota, which may provide an opportunity for early intervention (e.g., prebiotics or probiotics) to improve calf health with reduced usage of antimicrobials.