Streptococcus thermophilus strains are among the most widely employed starter cultures in dairy fermentations, second only to those of Lactococcus lactis. The extensive application of this species provides considerable opportunity for the proliferation of its infecting (bacterio)phages. Until recently, dairy streptococcal phages were classified into two groups (cos and pac groups), while more recently, two additional groups have been identified (5093 and 987 groups). This highlights the requirement for consistent monitoring of phage populations in the industry. Here, we report a survey of 35 samples of whey derived from 27 dairy fermentation facilities in ten countries against a panel of S. thermophilus strains. This culminated in the identification of 172 plaque isolates, which were characterized by multiplex PCR, restriction fragment length polymorphism analysis, and host range profiling. Based on this characterisation, 39 distinct isolates representing all four phage groups were selected for genome sequencing. Genetic diversity was observed among the cos isolates and correlations between receptor binding protein phylogeny and host range were also clear within this phage group. The 987 phages isolated within this study shared high levels of sequence similarity, yet displayed reduced levels of similarity to those identified in previous studies, indicating that they are subject to ongoing genetic diversification. Streptococcus thermophilus is believed to have emerged at the beginning of human dairy activity, around 7000 years ago [Streptococcus salivarius; however, phylogenetic analysis of concatenated sequences of four genetic loci of S. salivarius, Streptococcus vestibularis, and S. thermophilus  demonstrated that S. thermophilus shared no common alleles with the two commensal species, defining it as a distinct species within the salivarius group [S. salivarius and 27 S. thermophilus strains using identical primers identified 6% allelic variation among S. salivarius strains and typically <1% allelic variation among S. thermophilus strains. This low allelic variation among the S. thermophilus strains was somewhat surprising given the range of geographical isolation sources and the time span over which the strains had been isolated (over a period of 40 years) [S. thermophilus populations emerged relatively recently and that limited diversity exists among this species. The limited genetic diversity of this species may also have implications for the diversity of phages that may infect its member strains.ears ago ,2. This S. thermophilus were limited to two groups, termed the cos and pac groups. These were defined as distinct groups based on their modes of packaging (the use of cohesive ends or head-full packaging based systems) and the number of major structural proteins observed in SDS-PAGE profiles [cos phage group are most frequently reported in studies of thermophilic dairy fermentations [pac phage group contains both virulent and temperate members, although the incidence of lysogeny in S. thermophilus is reported to be rather low at less than 10% [pac phages resemble cos phages with long non-contractile tails and isometric capsids. A comparative genome analysis of phages of these groups highlighted the genomic plasticity of dairy streptococcal phages, with modular genetic exchanges being a notable feature of the evolution of the genomes of these phages [S. thermophilus phages, that is, the 5093 and 987 groups, were identified in 2011 and 2016, respectively. Their genomes appear to have acquired genetic material from prophages of non-dairy streptococci in the case of the 5093 phages [Lactococcus lactis for the 987 phages [cos and pac phages, as 5093 phages possess globular appendages attached to their tail tips, whereas the 987 phages possess considerably shorter tails than the other phage groups with a broad appendage at the tail tip that resembles the so-called “baseplate” of lactococcal P335 phages [S. thermophilus isolated from Gruyère cheese in France in 1978 included images of a phage displaying the characteristics of 987 phages (short tails of 130 nm with broad baseplate appendages at the tail tip) [S. thermophilus phages highlights the need for continued efforts in the identification of phage populations in dairy processing facilities.For several decades, reports of phages infecting profiles ,4,5,6. Mntations ,8,9,10. ntations . In contthan 10% ,13. Morp3 phages , and fro7 phages . The mor5 phages ,15,16. Wail tip) . Phages S. thermophilus strains. Members of all four S. thermophilus phage groups were identified and the genomes of representative isolates of each of the groups were sequenced in order to assess the extent of genetic diversity of phages in our collection.In the present study, 35 samples of whey derived from 27 thermophilic dairy fermentation facilities using distinct defined starter cultures were evaluated for the presence of phages against a panel of Streptococcus thermophilus from the UCC culture collection were routinely grown at 42 °C from single colony or from bacterial stocks maintained at −20 °C (25% w/v glycerol) in M17 broth  supplemented with 0.5% lactose . Strains were characterised phenotypically as likely exopolysaccharide (EPS) producers in cases where “slimy” colony morphologies were observed on agar plates and/or where a ropy phenotype was observed after overnight incubation in broth. Secondly, the strains’ growth characteristics were also noted after overnight incubation as either sedimenting (S) or non-sedimenting (NS). The strains and their characteristics are listed in Fifty-two strains of S. thermophilus strains. This was achieved using the standard double agar spot assay method [2, and 10 g/L (solid) or 4 g/L (semi-solid) agar-agar . Lytic phage activity was confirmed through the development of a clear zone of lysis within the bacterial lawn following overnight incubation at 42 °C. Phage positive samples were subsequently enumerated by plaque assays using the relevant whey–strain combination. Following overnight incubation, plaque morphology was assessed and isolates were deemed distinct based on size  and/or halo formation. Individual plaque isolates were propagated on the relevant host strain in 10 mL LM17 broth supplemented with 10 mM CaCl2 at 42 °C, filtered through a 0.45 µm filter, and stored at 4 °C.Thirty-five whey samples sourced from 27 distinct dairy fermentation facilities spanning ten countries on four continents were analysed for the presence of phages with lytic activity against 52 y method , for whiS. thermophilus phages, that is, cos, pac, 5093, and 987 groups. The primers used for the phage typing were based on those used in previous studies [cos phages were as follows: cosFOR (5′-ggttcacgtgtttatgaaaaatgg-3′) and cosREV (5′-agcagaatcagcaagcaagctgtt-3′) with an expected product size of 170 bp; those based on the pac phages were pacFOR (5′-gaagctatgcgtatgcaagt-3′) and pacREV (5′-ttagggataagagtcaagtg-3′) with an expected product size of 427 bp; those based on the 5093 phages were 5093FOR (5′-ctggctcttggtggtcttgc-3′) and 5093REV (5′-gcggcaaccatcttagaccag-3′) with an expected product size of 983 bp; and those based on the 987 phages were 987FOR (5′-ctaagcgtttgccactgtcag-3′) and 987REV (5′-gctgccgttgtttgaaaac-3′) with an expected product size of 707 bp. A PCR cycle of 95 °C for 10 min, followed by 30 cycles of , and a final single extension of 72 °C × 10 min was employed. The resulting amplicons were applied to a 1% agarose gel and visualized by UV transillumination.All bacteriophage isolates were subject to a multiplex PCR reaction to determine if they could be classified among the currently recognised groups of  studies . The priEcoRI and EcoRV according to the manufacturer’s instructions . Then, 10 µL of restricted product was applied to a 1% agarose gel at 85 V for 45 min and visualized by UV transillumination.Restriction fragment length polymorphism profiling of select phage isolates was applied where distinction of phage isolates based on source or PCR typing could not be made. This was performed using 7 plaque forming units per ml (pfu/mL) and their host ranges were subsequently assessed on 51 additional strains from the collection listed in Finally, to characterise the phages, a host range analysis of the individually propagated plaque isolates was performed. All bacteriophages were propagated to a titre of (at least) 108 pfu/mL), which was first treated with 1 μg/mL DNAse at 37 °C for 30 min, after which 50 µL of 0.5 M EDTA was added. After centrifugation at 13,200× g for 15 min, the lysate was transferred to a new tube, after which polyethylene glycol 8000 and sodium chloride were added to a final concentration of 10% and 0.5 M, respectively, and the resulting suspension was then incubated at 4 °C overnight. Subsequently, the suspension was centrifuged at 17,700× g for 15 min and the supernatant was removed. The PEG-precipitant was resuspended in 0.5 mL of TE buffer (pH 9.0) and treated with 40 μL of 20 mg/mL proteinase K for 20 min at 56 °C, and subsequently with 1.5% SDS for a further 10 min. Potassium acetate was added to a final concentration of 1 M, followed by incubation on ice for 20 min before centrifugation at 13,200× g for 10 min. The supernatant was then phenol/chloroform-extracted  (at least) twice and the aqueous phase was precipitated with 2.5 volumes of ice-cold 96% ethanol and 0.1 volume of sodium acetate (pH 4.8). Subsequent to centrifugation, the pellet was washed in 70% ethanol and resuspended in 100 μL of TE buffer (pH 8.0). Where phage titre permitted (above 108 pfu/mL), DNA extraction was performed using the Phage DNA Isolation Kit , according to the manufacturer’s instructions.Bacteriophage DNA extraction was performed using methods adapted from the work of . Phage Dhttps://blast.ncbi.nlm.nih.gov/Blast.cgi). The proposed functional annotations were further confirmed by performing structural homology searches via HHpred [https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi). The annotated genomes were inspected, edited, and finalised using the Artemis visualisation tool [Thirty-nine isolates were selected for genome sequencing based on having unique/overlapping host ranges, restriction profiles, and geographical source of isolation. Genome sequencing of the selected phage isolates was performed using Illumina MiSeq sequencing technology . De novo sequence assemblies and automeated gene calling was performed using the MEGAnnotator pipeline  and assea HHpred  and querion tool .Genbank accession numbers for the sequenced phage genomes are presented in S. thermophilus phages, was performed using ClustalW software. The alignment was employed to generate an unrooted phylogenetic tree using the “itol” software (http://itol.embl.de/), applying the neighbor-joining method.Phage genomes were compared using nucleotide BLAST analysis of entire genomes, and comparison of the encoded individual proteins was performed by all-against-all, bi-directional BLAST alignment  with an cos), SW13 (pac), SW16 (987), and SW27 (5093) were selected as representatives of the each of the four groups of S. thermophilus phages isolated in this study. Purification of the above-mentioned phages was achieved by double filtration of a high titre lysate (108–9 pfu/mL) through a 0.45 µm filter. Adsorption of purified phages to freshly prepared carbon film floated from a freshly coated mica sheet and negative staining with 2% (w/v) uranyl acetate were performed as described previously [Phages SW11 . This may indicate that the strains assessed in this study may not represent the full diversity required to capture the entire population of phages that may be present or that the numbers were too low at the time of processing. However, it is interesting that one sample (whey no. 17) was considered phage-negative by the starter culture provider, while it was found to contain phages against strains from our collection.Samples of whey from 27 factories in ten countries  that wer2 and 109 pfu/mL (The presumptive phage-positive samples (25 samples) were confirmed by plaque assays and sample-specific titres on the identified host strains were observed between 109 pfu/mL . This wiS. thermophilus phages have reportedly narrow host ranges [Most of the phage-positive samples contained phages that could infect one to three strains  were sourced from Italian factories, cos phages were observed almost ubiquitously across all geographical locations, while the remaining phage types were shown to be associated with a confined selection of Italian, Turkish, and/or Argentine factories. Five samples  harboured phages belonging to at least three of the four phage groups. Phages were not identified in the samples originating from Egypt and Congo, although just one sample was available from factories located in these countries.The 172 phage isolates reported above were first characterized by multiplex PCR to establish the diversity of phage genotypes present in our collection. This PCR-based approach permitted the classification of the isolated phages as belonging to the s phages . Among tcos phages, nine pac phages, eight 987 phages, and four 5093 phages) were selected for host range analysis based on having distinct primary host strains; geographical sources; plaque morphologies; phage typing reactions; and, where relevant, distinct RFLP profiles. Of the 52 strains tested, 23 were sensitive to infection by at least one of the 79 cos phage isolates; eight were sensitive to infection by at least one of the pac phages; and, interestingly, the 987 and 5093 phages infected only one strain each. Interestingly, at high titres, the 987 and 5093 phages could cross-infect the host strain of the opposite phage type (987 and 5093) at plaquing efficiencies of approximately 10−4 for the 987 phages and 10−5 for the 5093 phages. The highly confined host range of the 987 and 5093 phages among the collection of strains may underpin the relatively low frequency with which they are encountered in industry. While the cos and pac phages exhibit a typically narrow host range of one to three strains, a small number of isolates were observed to infect four or more strains (three pac isolates and one cos isolate). In order to define the variety of host-range interaction types present in the collection, the number of distinct host range profiles was assessed for the cos and pac isolates. For the cos phages, 39 distinct host range profiles were observed among the 79 isolates tested, while six distinct  host ranges were observed for the eight pac phages.Where (i) multiple phage isolates were propagated on a single host strain and/or (ii) where plaque morphologies of isolates propagated on the same host were highly similar, restriction fragment length polymorphism (RFLP) analysis was employed to identify unique isolates. In the vast majority of cases, the isolates from the same host and those exhibiting similar plaque morphology also displayed the same RFLP profile. To further characterize the phages within our collection, 100 isolates  or “sedimenting” (S) depending on the appearance of the culture after overnight incubation. Host range analysis highlighted that while NS strains dominated our collection (42 of 52 strains), the primary selection of cos, eight pac, four 5093, and eight 987 group phages) were selected for genome sequence analysis (cos phages exhibiting the longest genomes (34–37 kb)  . Consistisolates . More dePhylogenetic analysis of the phage genomes highlighted the close relationship between the 987 group phages in particular , with 99Similar to the 987 phages, limited genetic diversity was observed among the 5093 isolates , with 99pac phages isolated and sequenced in this study, there appears to be a significant degree of genetic diversity among these isolates. The phages were isolated from five distinct whey samples from Italy (four) and Turkey (one). Given that the eight sequenced isolates represent four host range profile groups, it is perhaps unsurprising that the region encoding the host adhesion device  is among the most significant areas of sequence dissimilarity, both within the isolates from this study as well as in comparison with the well-studied and related pac phages  in four distinct whey samples in three geographically disparate locations . However, these isolates all exhibit a broad host range infecting five strains, which may underpin the success of this phage in various fermentations.Despite the limited number of S. thermophilus phages, the cos group is most frequently encountered in the industry and is the most extensively studied. The current study proved no exception in that 147 of 172 isolates were shown to belong to the cos group (of which 19 were sequenced)  host range profile and the genetic diversity observed in the phylogenetic tree (cos type phages incorporates (parts of) the TMP, Dit, Tal–RBP and additional accessory proteins. The TMP is responsible for phage length determination of phages [cos phage DT1 was previously observed to harbor a CHAP (cysteine histidine-dependent aminohydrolase/peptidase) domain and an SLT (soluble lytic transglycolase) domain at the carboxy terminus of its encoded TMP. All sequenced members of the cos group in this study also harbor these domains at the C-terminus of their respective TMPs. The presence of these lytic domains within the TMP appears to be unique to the cos phages as this was not observed among the TMPs of any of the other phage groups. The Dit proteins of the cos and pac phages contain additional domains relative to those of other phage groups, which, in Lactobacillus and lactococcal phages, constitutes an “evolved” Dit [cos isolates, it would be expected that the components of the adhesion device exhibit sequence divergence.Among quenced) . The gentic tree  and the tic tree  further tic tree  with an f phages , while rf phages  and cellf phages ,31 have ved” Dit ,32. Thespac phages isolated from the Italian samples (four phages) group quite closely and infect three distinct strains, while the isolate from the Turkish sample (SW13) appeared to be distinct . The latter phage infects five S. thermophilus strains from our collection, which is a relatively broad host range by comparison with the majority of dairy streptococcal phages. Phages SW31, SW32, and SW33 are identical to SW13, and thus also exhibit the same host range. Therefore, there are five genetic lineages among the pac isolates and, while the primary/propagating hosts of four of these five distinct pac phages are unique, their host range profiles contain several incidences of one or more overlapping strains preventing a clear link between the RBP phylogeny and host specificity from being derived. Interestingly, the Tal–RBPs of the cos phage isolates display a very clear correlation between Tal–RBP phylogeny and primary host and/or host range. For example, SW2 and SW5 share an identical primary host strain (UCCSt86); SW1, SW9, and SW10 share UCCSt23 as their primary host; and SW6 and SW30 both infect strain UCCSt95, among other similarly related groups. This corroborates the primary role of the Tal–RBP of cos phages in host recognition and binding.The sequences of the Tal–RBPs of all phages sequenced as part of this study were compared and reference sequences for each of the four groups were included . The RBPs of the 987 and 5093 groups were almost identical within their respective groups . This iscos, pac, 987, and 5093) was selected for purification and subsequent morphological analysis by transmission electron microscopy. All four phages were observed to exhibit the typical characteristics of their groups. The diameter of the capsids of the phages ranges between 55 and 59 nm. SW11 (cos phage) exhibits a tail of 228.1 ± 9.1 nm (n = 17) with a feather-like tail fibre appendage of 72.2 ± 5.2 nm (n = 11) protruding through the tip. This remarkably delicate structure has been observed in previous members of this group. SW13 (pac phage) possesses a long non-contractile tail of 231.9 ± 5.5 nm (n = 15) and also appears to possess a short tail fibre of 79.5 ± 10.1 nm (n = 8) at the distal end of the tail. SW27 (5093 phage) exhibits the typical globular appendages attached to the distal end of the tail—a feature that appears to be typical of the 5093 phages. The tail of this phage is 242.1 ± 6.9 nm (n = 23) with a tail tip appendage width of 27.4 ± 2.6 nm (n = 23) and length of 19.5 ± 2.7 nm (n = 23). SW16 (987 phage) possesses a relatively short tail of 124.9 ± 3.2 nm (n = 18) and a broad adhesion device at the tail tip  that is more akin to the lactococcal P335 phages than to those of other S. thermophilus phages and that is typical of members of the 987 phage group, owing to the apparent ancestral acquisition of lactococcal genetic elements (n = 6).A representative of each of the four groups of phages  increase in the frequency of isolation of 987 and 5093 phages in recent thermophilic phage surveys is an interesting observation. It is tempting to speculate that the (currently) preferred organoleptic properties of dairy products are influencing starter/strain selections, which, in turn, may selectively enhance the proliferation of certain phages such as the 987 and 5093 groups.cos phages in a manner reminiscent of the lactococcal 936 group phages [cos phages [cos and pac phages typically exhibit limited overlap in the host range [pac isolates in this study. This may be because of the requirement of additional components of the adhesion device for effective recognition and binding. An interesting observation is the identification of so-called “evolved” Dits encoded by the cos and pac phages. Such evolved Dit proteins have been identified in phages of Lactobacillus and Lactococcus, and in the case of Lactobacillus casei phages, these components were observed to harbor carbohydrate domains that were capable of replacing RBP functions. The diversification of the Dit proteins of dairy streptococcal cos and pac phages exemplifies the continuous evolution of the adhesion devices of phages infecting lactic acid bacteria in order to adapt to new hosts or to provide selective advantages in increasing the binding frequency/efficiency in order to ensure the continued success and proliferation of a given phage. From an industrial perspective, this diversification represents a constant threat to production practices. While plant design, personnel training, and sanitisation regimes may be improved and controlled, it is nearly impossible to remove phages from a plant environment. Therefore, regular phage monitoring within factories or by starter culture providers and wide-ranging phage surveys, such as the one reported here, are vital to ensure that we continue to understand the phage landscape in the dairy processing environment. This will support the production of consistent dairy fermented products with the desired organoleptic properties.The specificity of phage–host interactions is primarily dictated by phage-encoded Tal–RBPs , althougp phages ,35. Thiss phages ,36. The s phages ,9,15. Itst range , a findicos and pac phages [S. thermophilus phages, those that cannot be typed by the updated PCR set-up will be readily identified and selected for further analysis, thus refining and expediting diversity analyses. Such tools are essential to rapidly detect and classify of problematic phages in a given factory. In our study, the multiplex PCR successfully typed each of the 172 isolates, indicating that the primers are effective in classifying recent global isolates, thus validating their usefulness in practice.The first reported members of the 987 group were identified because the isolates could not be typed using the multiplex PCR designed to classify c phages . SubsequS. thermophilus strains are renowned for their lack of genetic diversity; however, phenotypically, some diversity is observed with some strains being ropy and slimy, characteristics generally associated with EPS production. In contrast, other strains display a sedimenting phenotype, while others grow homogeneously throughout the growth medium. These growth characteristics are indicative of varying surface properties and structures among S. thermophilus strains. Such phenotypic characteristics are often used as selectable traits in the dairy industry, for example, EPS production and higher viscosity would be desirable in strains applied to yogurt production. The majority of strains (42 of 52) in our collection exhibit an NS phenotype and 18 of these strains were susceptible to infection by at least one phage isolate. Eight strains displayed an S phenotype, with a further two exhibiting features of both sedimentation and growth in the medium. All ten of these strains were infected by phages within the isolated bank. It is interesting to note that all S strains were shown to be sensitive to phage infection, although it is impossible to conclude if S strains may typically be more phage sensitive than NS strains; however, it may be an interesting feature that may readily be assessed in an industrial context where much larger strain collections are available.S. thermophilus phage groups in this study is a reminder of the ongoing requirement for phage monitoring programs to understand the risk factors that may be present in any given factory and against any panel of starter cultures. Through such studies, tools to rapidly detect these industrial parasites may be developed, providing the opportunity to limit the proliferation of phages and to implement suitable strategies to ensure the consistent production of end-products with the desired characteristics and quality. This is imperative to the continuing success of the dairy fermentation industry.The isolation of members of all four


Ursus thibetanus) in Nepal. We characterized the diet of black bears in two seasons  and two study areas . We then conducted nutritional analysis of species consumed by black bears in each study area, in combination with nutritional estimates from the literature, to estimate the proportions of macronutrients  in the seasonal bear foods and diets, as well as their macronutrient niche breadth. We found that bamboo (Arundinaria spp.) had the highest relative frequency in both study areas and seasons. Ants and termites were found in DHR diets, but not KSL diets. One anthropogenic crop was found in DHR summer diets (Zea mays) and two were found in KSL summer diets . Other than insects, no animal prey was found in either diet. The proportions of macronutrients in diets  were relatively high in carbohydrate for both study areas and seasons: DHRsummer 24.1P:8.7L:67.2C; KSLsummer 16.7P:8.2L:75.1C; DHRautumn 21.1P:10.5L:68.4C; KSHautumn 19.0P:11.0L:70.0C. Macronutrient niche breadth was 3.1 × greater in the DHR than KSL during summer, and 4.0 × greater in the autumn, primarily due to the higher proportion of lipid in ants and termites relative to plant foods. Within‐study area differences in niche breadth were greater during summer than autumn; in the KSH the macronutrient breadth was 1.4 × greater in summer, while in the DHR it was 1.1 × greater in summer. Similarity in dietary macronutrient proportions despite differences in foods consumed and niche breadth are suggestive of foraging to reach a preferred macronutrient balance.Relatively little is known about the nutritional ecology of omnivorous Asiatic black bears ( Diet itself can be thought of as being composed of mixtures of foods  consumed by an animal  of foods consumed by a species has been used to characterize a species niche, including classification as a specialist or generalist  consumes a wide variety of foods varying in both physical and nutritional properties, and can thus be considered a generalist in terms of both food exploitation and food composition  is similar to the brown bear in that they have an omnivorous diet, consuming a wide range of seasonally and regionally available foods diverse in macronutrient composition  and rice fields in the Annapurna Conservation Area of Nepal creating food‐related human‐wildlife conflict  and the Kailash Sacred Landscape (KSL), during the summer and autumn seasons. We used nutritional geometry , rhesus macaque (Macaca mulatta), wolf (Canis lupus), red panda (Ailurus fulgens), wild boar (Sus scrofa), common leopard (Panthera pardus), Himalayan goral , and Himalayan serow  and Himalayan tahr  was in the Nepalese part of the KSL, which includes the Baitadi, Bajhang, Darchula (with the exception of the Api Nampa Conservation Area), and Humla districts  in the DHR. That is, potential and known habitat of Asiatic black bear was identified based on local knowledge, observations, or signs . Man‐made and game trails in black bear habitat were walked to identify and collect scats. Scats were identified by the first author (SP) and experienced field assistants. We collected samples within the elevation range of approximately 1,800–3,500 m for both study areas. Black bears in central Nepal have been shown to prefer habitats in the range of 1,600–3,200 m  and October to November (autumn) of 2016 following the procedure used by Panthi, Aryal, Raubenheimer, Lord, and Adhikari  Panthi, We used microhistological fecal analysis  and autumn (November) of 2016. For each plant, we collected samples from a minimum of five different sites. Samples were air dried and kept in plastic bags for transport to the laboratory. Samples of the same species were combined from all sites in each study area for proximate analysis of the composite samples at the Nepal Environmental and Scientific Services laboratory (Kathmandu), and the Nepal Agricultural Research Council, Animal Nutrition Division . Proximate analysis included estimation of crude protein (micro‐Kjeldahl method), ether extract , crude fiber (digestion method), moisture (gravimetric), and ash (gravimetric). Available carbohydrate was estimated by subtraction. We did not collect ants or termites for proximate analysis; thus, we obtained representative average estimates from the literature  had the highest RF% of all food items for both study areas and seasons (Tables Z. mays) were identified in the DHR summer diet, but not in the autumn diet. Six species of plants were identified in autumn DHR diets , soft mast (n = 3), and two anthropogenic crops . In KSL autumn diets, one species of soft mast (Ficus semicordata) and hard mast (Quercus incana) continued to be found in the diet, with no evidence of anthropogenic crops consumed. No insects were found in the KSL diet, and bamboo had a lower RF% for both seasons compared to the DHR. Other than insects in the DHR diet, no animal prey was found in scats in either study area.In KSL summer diets Table , we iden3.2Arundinaria spp., Berberis aristata) had noticeably lower crude protein, and higher crude fiber, content compared to plants from the DHR  were shown to have similar digestive efficiencies , which further demonstrates a link between high‐carbohydrate foods and bear‐human conflict (Coogan & Raubenheimer, It is possible that the differences in the dietary balance of carbohydrate and protein in summer diets between subpopulations may lead to body composition differences between bears. In laboratory studies of mice models, for example, higher proportions of dietary carbohydrate relative to protein tend to result in a higher body fat composition, while higher proportions of protein tend to be associated with greater lean mass (e.g., Solon‐Biet et al., We found intraspecific variation in plant nutritional characteristics between sites. The source of this intraspecific variation between study areas is unknown, but could reflect differences in site‐level characteristics affecting plant nutritional composition and genetic factors. The nutritional differences observed may also reflect the habitat degradation occurring the KSL (Uddin et al., In conclusion, we present seasonal dietary and nutritional information of the Asiatic black bear in Nepal, which furthers our understanding of their behavior and nutritional ecology. Conducting work on the nutritional ecology of free‐ranging carnivores is a challenging task (Machovsky‐Capuska, Coogan, Simpson, & Raubenheimer, None declared.SP and AA conceived the project and designed the study. SP collected the data, analyzed scat samples, and estimated diets. SCPC interpreted the data, analyzed macronutrient proportions, and led the writing of the paper. All authors critically reviewed the paper.


RPL3L, the first ribosomal gene implicated in atrial fibrillation to our knowledge. Analysis of 167 RNA samples from the right atrium reveals that the splice-donor variant in RPL3L results in exon skipping. We also observe an association with a missense variant in MYZAP (OR = 1.38), encoding a component of the intercalated discs of cardiomyocytes. Both discoveries emphasize the close relationship between the mechanical and electrical function of the heart.Most sequence variants identified hitherto in genome-wide association studies (GWAS) of atrial fibrillation are common, non-coding variants associated with risk through unknown mechanisms. We performed a meta-analysis of GWAS of atrial fibrillation among 29,502 cases and 767,760 controls from Iceland and the UK Biobank with follow-up in samples from Norway and the US, focusing on low-frequency coding and splice variants aiming to identify causal genes. We observe associations with one missense (OR = 1.20) and one splice-donor variant (OR = 1.50) in  RPL3L, the first ribosomal gene implicated in atrial fibrillation, and MYZAP, an intercalated disc gene.Rosa Thorolfsdottir et al. report a genome-wide association study of atrial fibrillation in 29,502 cases and 767,760 controls from Iceland and the UK Biobank. They identify a significant association with coding variants in  It is associated with increased mortality and morbidity, particularly stroke and heart failure, and is responsible for substantial health care costs1. Atrial fibrillation is a complex disease that is characterized by both mechanical and electrical abnormalities of the atria that may be detected prior to diagnosis of the arrhythmia itself. The role of atrial myopathy and fibrosis in the development of atrial fibrillation is increasingly recognized and it has been postulated that these processes may contribute to cardioembolic stroke in the absence of arrhythmia2. Thus, identification of the early stages of atrial myopathy may allow for therapy to prevent progression to atrial remodeling, atrial fibrillation, and stroke3.Atrial fibrillation is the most common arrhythmia of clinical significance with an estimated number of 33.5 million individuals diagnosed with atrial fibrillation globally in the year 20104. Most of the associated variants are non-coding and the causative genes remain unknown but the closest genes reveal a polygenic process, implicating transcription factors, cardiac ion channels, myocardial, and cytoskeletal proteins in the pathogenesis of atrial fibrillation. In the pre-GWAS era, linkage mapping and candidate gene sequencing linked a number of rare sequence variants to atrial fibrillation, mostly in single cases or familial atrial fibrillation, including variants in cardiac ion channel genes4. These variants explain a small proportion of atrial fibrillation cases, and for many, the genetic evidence is not robust.Genome-wide association studies (GWAS), assessing primarily common sequence variants, have yielded over 30 genetic loci that associate with atrial fibrillation8. All three variants are in structural genes, the myosin sarcomere genes MYH65 and MYL47 and the cytoskeletal gene PLEC8. These findings support the notion of an important relationship between myocardial mechanical integrity and the development of arrhythmias.In the past few years, through GWAS based on whole-genome sequencing, we have identified three low-frequency coding variants that associate with atrial fibrillationHere, we continue our search for variants associated with atrial fibrillation to shed further light on the pathophysiology of this common arrhythmia. We performed an atrial fibrillation GWAS using data from Iceland and the UK Biobank, focusing on rare and low-frequency coding and splice variants, with follow-up of the most significant variants in samples from Norway and the US.9, focusing on variants annotated as having moderate or high impact on protein function 10. To account for the expected impact, we applied the significance thresholds of P < 5.1 × 10−8 for moderate and P < 2.6 × 10−7 for high-impact variants11.We performed a meta-analysis on atrial fibrillation including 14,710 cases and 373,897 controls from Iceland and 14,792 cases and 393,863 controls from the UK BiobankRPL3L on chromosome 16, with the missense variant p.Ala75Val  and the splice-donor variant c.1167+1G>A . The two RPL3L variants are not correlated , and when conditioned on each other, both associations with atrial fibrillation remained  that is primarily expressed in skeletal muscle and heart unlike most ribosomal proteins, that are ubiquitously expressed12.We found two novel genome-wide significant atrial fibrillation associations in the gene MYZAP on chromosome 15  , RPL3L c.1167+1G>A , and MYZAP p.Gln254Pro  , the Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk (FOURIER) trial, the Vanderbilt atrial fibrillation Registry, and the Tromsø Study. Joint analysis of all data sets yielded genome-wide significant association with atrial fibrillation of all three variants, MYH6, PLEC, and MYL4 (recessive model), previously reported by us, were genome-wide significantly associated with atrial fibrillation in this data set8.Three other moderate or high-impact coding variants in the genes 8. For example, a sequence variant associated with atrial fibrillation in the sodium channel gene SCN10A has extensive and strong effects on ECG measures but a relatively small atrial fibrillation effect compared to the most significant common atrial fibrillation variant near PITX2 that has minimal effect on ECG measurements   and commonly with effects that are proportional to the atrial fibrillation effectsRPL3L variants and blood cells and found no associations. Similarly, since mutations in intercalated disc genes, albeit not MYZAP, have been associated with cardiomyopathies in man14 we assessed the link between the MYZAP variant and cardiomyopathies in our database, but found none.To gain a better understanding of the new atrial fibrillation variants, we tested them for association with other phenotypes in deCODE´s genotype/phenotype database under both additive and recessive models but found no other associations passing Bonferroni correction. Association results for available relevant phenotypes including risk factors of atrial fibrillation are listed in Supplementary Tables RPL3L. Two of the 167 individuals carry this variant. Non-carriers only produce the primary RPL3L isoform, but both carriers also produce an alternative isoform that skips exon 9   10 code I.48 and ICD 9 code 427.3) according to electronic medical records at Landspitali, The National University Hospital, in Reykjavik, Iceland, and Akureyri Hospital, the two largest hospitals in Iceland, between 1987 and 2017. In total, 14,710 out of the 15,552 cases had genotypes and were included in the analysis. Controls were 373,897 Icelanders recruited through different genetic research projects at deCODE genetics. All participating subjects who donated blood signed informed consent. Personal identities of the participants and biological samples were encrypted by a third party system. The study was approved by the Icelandic Data Protection Authority and the National Bioethics Committee of Iceland (no. VSNb2015030021).9. The atrial fibrillation population from UK Biobank consisted of 14,792 cases and 393,863 controls, all individuals of European ancestry recruited between 2006 and 20109. Atrial fibrillation was ascertained based on ICD diagnoses. These are primary or secondary ICD-9 or ICD-10 diagnoses codes a participant has had recorded across all their episodes in hospital. Self-reported diagnoses were excluded from our analysis. Further details on the recruitment and variables collected in the UK Biobank study can be found in previous publications27.The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across UK, aged between 40 and 69 at recruitment. The study has collected extensive phenotypic and genotypic information on its participants, including ICD coded diagnoses from inpatient and out-patient hospital episodesWe genotyped novel atrial fibrillation variants in an atrial fibrillation sample set (759 cases and 759 controls) from the Vanderbilt Atrial Fibrillation Registry, a clinical and genetic registry at the Vanderbilt University Medical Center in Nashville, Tennessee. At enrollment into the registry, a detailed medical and drug history was obtained from all patients and patients were also asked to complete a symptom questionnaire. Patients with history of atrial fibrillation only associated with cardiac surgery were excluded from this study. Written informed consent was obtained from all patients under a protocol approved by the Vanderbilt University Institutional Review Board.29. A subset of over 12,000 participants of European descent from the FOURIER trial have been genotyped by us by whole-exome sequencing, chip-typing, and imputation. Detailed phenotypic information was gathered on all FOURIER study participants, including atrial fibrillation disease status. The Fourier atrial fibrillation sample set consists of 1238 cases and 11,562 controls of European descent, excluding all Icelandic participants.We also followed-up novel atrial fibrillation variants in an atrial fibrillation sample set originating from the Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk (FOURIER) trial . FOURIER is a randomized placebo-controlled, double-blind, parallel-group, multinational trial testing the hypothesis that adding the drug evolocumab to statin therapy will reduce the incidence of major adverse cardiovascular events in patients with clinically evident cardiovascular disease (CVD). The whole study group consisted of 27,564 patients recruited at 1242 cities in 49 countries from 2013 to 2015. Eligible patients were between 40 and 85 years of age and had clinically evident atherosclerotic CVD. The design of the trial has been described in detail elsewhere30. So far, more than 40,000 individuals have been examined.The population is being followed-up on an individual level with registration and validation of diseases and death and an endpoint registry has been established for CVD. Discharge diagnosis lists of CVD have been retrieved from the University Hospital of North Norway in Tromsø, and medical records for all individuals with a CV discharge diagnosis  have been reviewed. Atrial fibrillation has been registered based on ICD-9 and ICD-10 codes since 1986 as part of the ongoing CV endpoint registration in the Tromsø Study. People with postoperative atrial fibrillation only (≤28 days after the procedure) are registered, but are not included as cases. For the current project, one sex-matched and age-matched control for each case of atrial fibrillation from was drawn from the population-based Tromsø 4 survey. Participants in the Tromsø Study gave informed, written consent. The study was approved by the Regional Committee for Medical Research Ethics. The atrial fibrillation sample set consists of 714 cases and 698 controls.The Tromsø Study is a population-based prospective study with repeated health surveys in the municipality of Tromsø, Norway31. At each time point, the entire adult population (≥20 years) was invited to participate by completing questionnaires, attending clinical examinations, and interviews. Participation rates have generally been high: 89.4% , 69.5% , and 54.1%  in HUNT1, HUNT2, and HUNT3, respectively31. Taken together, the health studies included information from over 120,000 different individuals from Nord-Trøndelag. Biological samples including DNA have been collected for ~70,000 participants. Atrial fibrillation was defined based on ICD-10 codes collected from local hospitals and out-patient clinics between 1999 and 2016. Cases (6493) were defined as individual with one or more ICD-9 or ICD-10 codes specific for atrial fibrillation (“I48” or “427.3”) whereas controls  were all individuals without a code specific for atrial fibrillation.The Nord-Trøndelag Health Study (HUNT) is a population-based health survey conducted in the county of Nord-Trøndelag, Norway. Individuals were included at three different time points during ~20 years (HUNT1 (1984–1986), HUNT2 (1995–1997), and HUNT3 (2006–2008))n = 5626). The ischemic stroke or TIA diagnoses were based on standard WHO criteria and imaging evidence (either CT or MRI), and were clinically confirmed by neurologists. A total of 1369 individuals with ischemic stroke were classified as having cardioembolic stroke based on a neurologist review of medical records and classification according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST)32. The controls used in the various case–control analyses of this study consisted of disease-free controls randomly drawn from the Icelandic genealogical database and individuals from other genetic studies at deCODE. We also assessed the association of novel atrial fibrillation variants with SSS among 403 cases and 403,181 controls in the UK Biobank.Novel atrial fibrillation variants were tested for association with other phenotypes in the deCODE genetics phenotype database which contains extensive medical information on various diseases and other traits. The pacemaker population sample set includes 3578 individuals who received a pacemaker implantation  at LUH between 1997 and 2015. The SSS sample set includes 3578 individuals who received the diagnosis of SSS  at LUH in Reykjavik between 1987 and 2015. Ischemic stroke cases were identified from a registry of individuals diagnosed with ischemic stroke or transient ischemic attack (TIA) at LUH during the years 1993 to 2014  obtained in Landspitali, The National University Hospital, the largest and only tertiary care hospital in Iceland, have been digitally stored since 1998. We have analysed 434,000 ECGs from 88,217 individuals obtained between 1998 and 2015. To assess the effect of atrial fibrillation variants on ECG traits, and thus cardiac electrical function, in the absence of atrial fibrillation, we excluded ECGs from individuals with atrial fibrillation and pacemakers and used 289,297 sinus rhythm (heart rate 50–100 beats per min) ECGs of 62,974 individuals for the primary analysis. The ECGs were digitally recorded with the Philips PageWriter Trim III, PageWriter 200, Philips Page Writer 50, and Phillips Page Writer 70 cardiographs and stored in the Philips TraceMasterVue ECG Management System. These were ECGs obtained in all hospital departments, from both in patients and outpatients. Digitally measured ECG waveforms and parameters were extracted from the database for analysis. The Philips PageWriter Trim III QT interval measurement algorithm has been previously described and shown to fulfill industrial ECG measurement accuracy standards35. Variants that did not pass quality control were excluded from the analysis according to GATK best practices7. Information about haplotype sharing was used to improve variant genotyping, taking advantage of the fact that all sequenced individuals had also been chip-typed and long-range phased36.In Iceland, the study is based on whole-genome sequence data from the whole blood of 15,220 Icelanders participating in various disease projects at deCODE genetics. In addition, 151,677 Icelanders have been genotyped using Illumina SNP chips and genotype probabilities for untyped relatives has been calculated based on Icelandic genealogy. The sequencing was done using Illumina standard TruSeq methodology to a mean depth of 35 (SD 8). Autosomal SNPs and INDEL’s were identified using the Genome Analysis Toolkit version 3.4.0E(θ/chip data)) is estimated by the observed variance in the imputed expected counts and Var(θ) was estimated by p(1 − p), where p is the allele frequency.The informativeness of genotype imputation (imputation information) was estimated by the ratio of the variance of imputed expected allele counts and the variance of the actual allele counts:10. A total of 32.5 million variants passed the quality threshold and were imputed into 151,677 Icelanders who had been genotyped using Illumina chips.Variants were annotated using Ensembl release 80 and Variant Effect Predictor (VEP) version 2.837. With a set of 1.1M variants we regressed the χ2 statistics from our GWAS scan against LD score and used the intercept as a correction factor. The LD scores were downloaded from a LD score database . The estimated correction factor for atrial fibrillation based on LD score regression was 1.39 for the additive model in the Icelandic sample and 1.04 in UK Biobank.To account for inflation in test statistics due to cryptic relatedness and stratification, we applied the method of LD score regression38, in the first 50,000 participants, and with Affymetrix UK Biobank Axiom array in the remaining participants39; 95% of the signals are on both chips. Imputation was performed by Wellcome Trust Centre for Human Genetics using a combination of 1000Genomes phase 340, UK10K41, and Haplotype Reference Consortium (HRC) reference panels42, for up to 92,693,895 SNPs43.In the UK Biobank, genotyping was performed using a custom-made Affymetrix chip, UK BiLEVE AxiomFor genotyping of single variants in atrial fibrillation sample sets from the Vanderbilt registry, FOURIER trial, and Norway, we used the Centaurus (Nanogen) or KASP platforms.44 based on effect estimates and standard errors from the Icelandic and the UK Biobank study. Only sequence variants from the Haplotype Reference Consortium panel (HRC)42 were included in the meta-analysis and variants from deCODE and the UK Biobank imputation were matched on position and alleles. Standard errors were calculated in the following way:We performed a meta-analysis on 14,710 atrial fibrillation cases and 373,897 controls from Iceland and 14,792 atrial fibrillation cases and 393,868 controls from the UK Biobank. We used logistic regression to test for association between SNPs and atrial fibrillation and other phenotypes in the Icelandic study, treating phenotype status as the response and allele count as a covariate. We used allele counts from genotyping or integrated over possible genotype counts based on imputation. Other available individual characteristics that correlate with phenotype status were also included in the model as nuisance variables. In Iceland these covariates were: sex, county of birth, current age, or age at death (first and second order terms included), blood sample availability for the individual and an indicator function for the overlap of the lifetime of the individual with the time span of phenotype collection. In the UK biobank study 40 principal components were used to adjust for population stratification and age and sex were included as covariates in the logistic regression model. Only white British individuals were included in the study. For the meta-analysis we used a fixed-effects inverse variance methodP-value smaller than 1 we calculate the standard error as follows:σ givesP = 1, then f) and imputation information (I) and σ as follows:N) is proportional to imputation information (I) if we are always basing the analysis on the same set of individuals. Therefore, if we fit the following linear model:P < 0.9 then we can predict σ for a marker with P close to 1.For a 11.We corrected the threshold for genome-wide significance for multiple testing with a weighted Bonferroni adjustment using as weights the enrichment of variant classes with predicted functional impact among association signals estimated from the Icelandic data−7 for high-impact variants (N = 8464) and 5.1 × 10−8 for moderate-impact variants .With 32,463,443 sequence variants in the Icelandic data the weights given in Sveinbjornsson et. al. were rescaled to control the family-wise error rate. This yielded significance thresholds of 2.6 × 10Conditional analysis of the region around novel atrial fibrillation variants, was performed by adding the top variant or variants as a covariate while testing every SNP in the region for association with atrial fibrillation in the Icelandic data.8, ECG measurements were adjusted for sex, year of birth, and age at measurement and were subsequently standardized to have a normal distribution. For individuals with multiple ECG measurements, the mean standardized value was used. We assume that the quantitative measurements follow a normal distribution with a mean that depends linearly on the expected allele at the variant and a variance-covariance matrix proportional to the kinship matrix45. The Benjamini–Hochberg FDR procedure controlling the FDR at 0.05 at each marker was used to account for multiple testing.We tested atrial fibrillation variants for association with 122 ECG measurements using linear regression, treating the ECG measurement as the response and the genotype as the covariate. Following the procedures described in Thorolfsdottir et al.RPL3L (c.1167+1G>A), the RNA samples from cardiac atria were used to identify a novel isoform and quantify expression at the transcript level. RNA sequencing libraries were inspected for sequencing and alignment integrity using parameters retrieved from RNA-SeQC46, Picard CollectRnaSeqMetrics (http://broadinstitute.github.io/picard/), and FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Genotype concordance was determined by comparing imputed genotypes to those derived from RNA-seq. Genome alignments were found using STAR47 aligning to GRCh38 with ensemble v8748 gene annotations. Alignments of RNA-seq reads of carriers of the variant contained several reads that spliced over exon 9 in transcript ENST00000268661 of RPL3L. Neither of the two other transcripts of RPL3L showed any expression in all samples (See Supplementary Fig. 49 and the modified transcriptome. The expression of the annotated and novel transcript was corrected w.r.t. the size factor computed from the gene expression analysis. Finally the proportion of novel isoform usage was computed by dividing the estimated expression of the novel isoform with the sum of the expression of both isoforms. Due to the small number of carriers, two samples out of 167, we opted for a conservative test for computing the significance of the observed event, that the carriers have a ratio of 50% vs near 0% for non-carriers. The test used was the two-sided Mann–Whitney U test, which only takes the relative ranks of the samples into account and not the underlying values. The P-value computed was P = 0.0052, the lowest possible P-value that can be obtained using this statistical test with n1 = 165 and n2 = 2.RNA sequencing was performed on samples from cardiac right atrium of 167 Icelanders (see Supplementary Table The Icelandic population WGS data has been deposited at the European Variant Archive under accession code PRJEB8636. The authors declare that the data supporting the findings of this study are available within the article, its Supplementary Data files and upon request.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1


Hermetia illucens) have been identified as potential alternatives for the traditional protein sources used in livestock feed due to their rich nutrient content and the fact that they can be reared on organic side streams. However, black soldier fly larvae are very sensitive to external environments such as temperature and rearing medium. Currently, little is known about the combined influence of temperature and organic waste streams that are readily available in the urban environments of sub-Saharan Africa. Therefore, the aim of this study was to investigate the influence of temperature and organic waste streams on the development of black soldier fly larvae reared on two different organic substrates, i.e., brewers’ spent grain and cow dung. The results show that black soldier fly larvae reared on brewers’ spent grain were more efficient and tolerated a wider range of temperatures in comparison with those reared on cow dung.Rapid population growth and urbanization, continued economic growth, shifts in dietary patterns towards more animal source foods are major challenges that sub-Saharan Africa is currently facing. These challenges exert a high demand on agricultural production. Insect species such as the black soldier fly  have been identified as potential alternatives to the conventionally used protein sources in livestock feed due to their rich nutrient content and the fact that they can be reared on organic side streams. Substrates derived from organic byproducts are suitable for industrial large-scale production of insect meal. Although efficient in waste management and in feed production, BSF larvae are very sensitive to the external environment such as temperature and rearing medium. Therefore, we studied the effect of temperature and substrate type, i.e., brewers’ spent grain (SG) and cow dung (CD), on the development and survival of BSF larvae. Both organic substrates were readily available in Nairobi, Kenya, the location of the experiments. In our experiment, 100 3–5-day-old BSF larvae were placed into containers that contained either SG or CD and further treated at temperatures of 15 °C, 20 °C, 25 °C, 30 °C, and 35 °C. The duration of larval development was recorded, and the prepupae were removed, weighed, and placed individually in separate, labeled, 35-mL plastic cups filled with moist sawdust. After emergence, 10 2-day-old adults  from every replica per substrate were transferred into a cage (40 × 40 × 40 cm) and allowed to mate for 24 h at their respective temperatures. The laid egg batches were collected and counted, and the adult flies’ longevity was recorded. The data were subjected to a two-way analysis of variance (ANOVA) using the general linear model procedure. BSF larvae reared on SG developed faster than those reared on CD; the former also favored higher temperatures for their larval development and emergence into adults. The optimum range was 25–30 °C. With increasing temperatures, the longevity of adult BSF decreased, while the fecundity of females increased. Thus, it is possible to take advantage of the readily available SG waste streams in the urban environments of Kenya to produce BSF larvae-derived livestock feed within a short duration of time and at relatively high temperatures. In 2014, 54% of the world’s population resided in urban areas, while in 1950, this number only constituted 30%; by 2050, two-thirds of the world’s population will live in urban areas . TherefoHermetia illucens L. (Diptera: Stratiomyidae), the common house fly Musca domestica L. (Diptera: Muscidae), and the yellow mealworm Tenebrio molitor L. (Coleoptera: Tenebrionidae) [The current combination of inefficient production and unsustainable consumption patterns points to the need to adopt cost effective production systems, in which alternative protein sources for animal feed with lower ecological footprints are used in order to achieve more sustainable agricultural production and improved food security while safeguarding the already fragile ecosystems and natural resources in the developing world ,23. Massionidae) ,29,30,31ionidae) ,33,34. Fionidae) ,36,37,38Insects are known to inhabit a wide variety of environments, including extreme ones, due to their adaptive behavioral and physiological mechanisms . HoweverTherefore, this study sought to investigate the influence of temperature on selected life-history traits of BSF reared on two different and readily available urban organic waste streams in the urban environment of a large city in SSA. This comparison allowed us to determine which of the two organic waste streams performs best, as well as the accompanying optimum temperatures. Information from this study is important for improving rearing methods in SSA, as well as for creating cost-effective and environmentally sustainable alternative livestock feeds that can buffer the impact of climate change, especially for small-scale livestock producers who are not connected to international feed markets and local feed producers who can neither afford nor implement sophisticated climate-controlled production facilities.icipe), in Nairobi, Kenya.The study was carried in the laboratories of the International Centre for Insect Physiology and Ecology  and brewers’ spent grain (SG), were both sourced locally. Fresh CD was collected from Farmers Choice slaughterhouse in Kahawa West in Nairobi; the bovines originated from different ranches in Kenya where they had been raised on natural grassland. SG was sourced from Tusker House, Kenya Breweries Ltd. off Thika Road in Nairobi after the fermentation of the barley in the beer production process. The substrates were chosen based on their availability in Nairobi with a view of their potential future use for large-scale industrial BSF larvae production. Fresh CD and SG substrates were oven dried at 60 °C for 48 h and then stored for subsequent experiments in a refrigerator at −20 °C.icipe. Adult BSF were housed in an outdoor, metal-framed cage with 1.5 mm screen mesh (1.8 × 1.8 × 1.8 m) with direct access to daylight to encourage mating. The flies were supplied with water to prolong their life. Corrugated cardboard and some SG were placed within the cage to attract adult females for oviposition. The colony was maintained in the insectary for over 8 generations before use in this experiment.The stock population of BSF populations was maintained at the insectary in First, 10 batches of eggs were collected from the stock colony and placed into smaller containers (15 × 9.4 cm) containing an oviposition substrate of moist-to-liquefied SG (100 g). Each setup was closely monitored 3 times a day to ensure egg hatching. After hatching, 100 3–5-day-old larval instars were transferred into different, clear, plastic, 500-mL containers with the 2 test substrates, CD and SG. Each container contained 100 g of the test substrate, which was mixed with water to achieve a moisture content of 70% by weight . The expEach treatment was monitored daily, and the duration of the larval development was recorded. The recording of larval development stopped when all the larvae reached the prepupal stage. The prepupae were removed, weighed, and placed individually in separate, labeled, 35-mL plastic cups filled with moist sawdust. Each cup with the prepupae was covered with a breathable lid and returned to its respective temperature regime for daily monitoring and subsequently recorded for the numbers of puparia formed. Further, adult emergence was monitored daily. Upon emergence, 10 2-day-old adults  from every replicate per substrate–temperature treatment were transferred into a cage (40 × 40 × 40 cm) and allowed to mate for 24 h at their respective temperatures. Thereafter, an oviposition device with a small bowl of moist chicken manure and 2–3 cardboards were placed in each cage to provide sites for oviposition. A 10% sugar solution in water was provided daily in a vial through a filter paper inserted into the vial’s lid. The laid egg batches were recorded, and the numbers of eggs per batch were counted under a microscope. The adult flies’ longevity was recorded daily until all the caged flies were dead. p < 0.05 level. For temperature-driven models, a parametrized square function [b is the temperature for the minimum development time and c is the minimum development time.R Statistics  and Stata Statistical Software  were used for analyses. The collected data were subjected to Levene’s test for normality, followed by a two-way analysis of variance (ANOVA) using the general linear model (GLM) procedure. Where significant differences existed, Tukey Honest Significant Difference (HSD) or Least Significant Difference (LSD) post hoc was used to separate the means at the function  was fittfunction :y = a at 31.9 °C (range 30.2–33.7 °C) for CD-fed immatures and 16.1 days (range 9.0–23.2 days) at 30.3 °C (range 29.4–31.3 °C) for SG-fed ones . For theTemperature has proven to be a key factor in the development and survival of insects . MoreoveSeveral factors may have contributed to the differential development observed between the two rearing substrates. The most important contributing factor was the difference in the quality of the nutritional content between the rearing substrates. Several studies emphasized the importance of nutritional components, such as proteins and carbohydrates, in the development of insect larvae ,58,59. TFood availability and access to nutritional resources are other crucial factors affecting larval and adult life history traits ,65. For Anastrepha obliqua and found a decrease in the duration until adult emergence with increasing temperatures. Moreover, moist sawdust, the pupation substrate used in our study, may have also collectively accelerated the developmental time of pupae to adult emergence. Our results are further supported by Holmes et al. [Pupation, a complex process involving significant morphological and physiological transformations, is essential for holometabolous insects . Therefos et al. , who alss et al. .Adult longevity significantly decreased with increasing temperatures, with BSF adults derived from larvae previously reared on SG recording higher longevity. This confirms our previously stated assumption regarding the influence of the nutritional content of the rearing substrate, as well as access to nutritional resources, on larval and adult life-history traits and explains why we observed –even at a similar temperature range (25–30 °C)—shorter adult longevity in comparison with Myers et al. , who alsOur study is the first to provide information regarding the influence of temperature on the life history of BSF reared on two diets readily available in urban SSA. Such information is necessary for developing BSF potential in the developing world, both as a tool for the bioconversion of organic waste and as an alternative protein source in feed stock. Our results demonstrate that both temperature and substrate type significantly influence the development, longevity, and fecundity of the flies. Black soldier flies needed less time to develop on higher temperatures during their immature and pupal stages, while adults’ longevity decreased at higher temperatures. Similarly, BSF reared on spent grain outperformed the ones reared on cow dung by surpassing them in weight and requiring less time to develop. Regardless of the waste stream used, BSF production systems have to be designed in a manner that provides BSF with adequate access to fresh nutritional content. Also, considering that the BSF could be used to feed livestock that are part of the human food chain, it is important to assess the potential risks associated with contamination by pathogens and the bioaccumulation of heavy metals. Moreover, the influence of the rearing substrate influenced the fecundity of adult flies, with the ones reared on spent grain as larvae producing more eggs, underlining the importance of the nutritional quality of the rearing substrate. Considering that BSF is produced for feed and not only for the purpose of organic waste recycling, this interesting information suggests the need to introduce starter-culture production facilities with customized rearing substrates for the production of BSF eggs as compared with organic waste recycling facilities. Hence, future research should focus on the development of adapted technologies in terms of the following: (1) rearing temperatures, (2) feeding methods, and (3) substrate hygiene and safety measures for small- to medium-scale industrial mass production systems of insects, such as BSF, into which commonly available urban organic waste streams can be fed. The availability of such production systems would considerably lower the cost of livestock feeds and consequently would make animal protein more affordable for the growing urban populations in SSA, thereby improving food security and nutrition, especially for women, children, and other vulnerable members of society.


For mammals and reptiles, high richness was associated with rugged terrain, while bird richness was highest in coastal lowland woodlands. On average, detectability of diurnal birds was higher per day of surveys (0.33 ± 0.09) compared with nocturnal birds per night of spotlighting (0.13 ± 0.06). Detectability of reptiles was similar per day/night of pit trapping (0.30 ± 0.09) as per night of spotlighting (0.29 ± 0.11). On average, mammals were highly detectable using motion-sensor cameras for a week (0.36 ± 0.06), with exception of smaller-bodied species. One night of Elliott trapping (0.20 ± 0.06) and spotlighting (0.19 ± 0.06) was more effective at detecting mammals than cage (0.08 ± 0.03) and pit trapping (0.05 ± 0.04). Our estimates of species occupancy and detectability will help inform decisions about how best to redesign a long-running vertebrate monitoring program in the Top End of northern Australia.Understanding where species occur and how difficult they are to detect during surveys is crucial for designing and evaluating monitoring programs, and has broader applications for conservation planning and management. In this study, we modelled occupancy and the effectiveness of six sampling methods at detecting vertebrates across the Top End of northern Australia. We fitted occupancy-detection models to 136 species  of 242 recorded during surveys of 333 sites in eight conservation reserves between 2011 and 2016. For modelled species, mean occupancy was highly variable: birds and reptiles ranged from 0.01–0.81 and 0.01–0.49, respectively, whereas mammal occupancy was lower, ranging from 0.02–0.30. Of the 11 environmental covariates considered as potential predictors of occupancy, topographic ruggedness, elevation, maximum temperature, and fire frequency were retained more readily in the top models. Using these models, we predicted species occupancy across the Top End of northern Australia (293,017 km Monitoring is important for determining the status and trends of species as well as their response to threats or management intervention. Despite the importance of monitoring, most programs are under-resourced , placinghttp://nrmaps.nt.gov.au/nrmaps.html). These monitoring data have been instrumental for setting conservation priorities in the region  with camera trapping and  with cage trapping meant a species was detected  in the second and fifth week with cameras, and only in the first day/night with cages.We compiled a list of covariates that were: 1) thought to broadly influence occupancy and detectability of birds, mammals and reptiles across the study region; and, 2) available at the broad spatial scale of the study. These included climatic variables , terrain variables , environmental variables , and fire variables. Fire variables included point-based metrics  and neighbourhood-based metrics  to capture the spatial homogeneity of fires across space and time . We acknWe tested site covariates for collinearity  and discψ) over long time periods; and, 2) the probability a species was present within the site and observed in any given survey visit (ρ). The first binomial process, occupancy, was considered a Bernoulli random variable, such that:ψ is the probability of species occupancy at site i and zi denotes the true state of occurrence. We then assumed the observation of a species at site i is the outcome of another Bernoulli random variable, with the product of zi and detection probability ρij the success probability. The detection/non-detection data yij observed at site i during survey j can be described as:yij is the observed ‘presence-absence’ data and ρij is the probability of detection. We modelled the influence of covariates on occupancy and detectability using the logit link function in Eqs We modelled occupancy and detectability for each species assuming the result from any given survey was the outcome of two binomial processes acting simultaneously , 31: 1) Gehyra pamela, Pseudothecadactylus lindneri, Oedura gemmata, Banded Fruit-dove, Chestnut-quilled Rock-pigeon) that have ranges restricted to the Arnhem Plateau, which is only a small fraction of the study region. We created a ‘plateau’ layer as a proxy for this geographic range by placing a 5 km buffer around the Arnhem escarpment, and only used data collected from within this area when fitting models. This meant that our estimates of occupancy were relative to the expected geographic range of species rather than to all sites in the study region, with predictions of the seven species above confined to our plateau layer.Prior to model fitting, we identified seven species  there was perceived to be a severe mismatch between predicted occupancy and current beliefs about where the species occurs; or, 2) if predicted occupancy unrealistically approached 0 and/or 1 , which can result from too few detections . ConsideWe assessed model fit of the top-ranked model with sum of squared errors, Freeman-Tukey Chi-squared test, and Pearson’s Chi-squared test . GoodnesFor species containing covariates in their ‘best-model’, we generated occupancy maps across the study region by predicting occupancy to mapped covariate raster layers at 1 km resolution . Rather We built relative species richness maps by stacking occupancy maps for those birds, mammals, and reptiles for which we could fit models, and summed the predicted occupancy value in each cell across species . We did Ctenotus storri) to 0.49 ± 0.07 (Carlia amax)  were recorded during surveys of eight protected areas  Tables, ia amax) . By compia amax) .For birds, elevation was retained in 34% of occupancy models, followed by maximum temperature (27%), fire frequency (22%), fire patchiness (16%), distance to watercourse (16%), fire extent (13%), and time since fire 13%, . For repHeteronotia planiceps) to 0.69 ± 0.11  to 0.57 ± 0.22 (Oedura marmorata). Of three reptiles detected using both methods, two were more detectable by spotlighting  or day/night of searches  was lowest for mammals (0.13 ± 0.05), and higher for birds (0.23 ± 0.07) and reptiles (0.28 ± 0.10), although estimates varied widely between species and between sampling methods  Tables. lighting .Detection probability for mammals using a night of pitfall trapping was very low, ranging from 0.01 ± 0.01 (Grassland Melomys) to 0.09 ± 0.06 (Red-cheeked Dunnart) . In contFire frequency was retained in 7% of bird, 3% of reptile, and 16% of mammal detectability models, with an exclusively negative effect on reptile and mammal detectability, and mixed effect on birds  Tables. For mammals, our relative species richness map predicted higher richness in rugged terrain along the edge of the Arnhem plateau and in rugged areas in the south west, as well as across coastal lowlands in the north and west . This paFew studies have modelled the distribution of birds , mammalsFor mammals, fire and landscape variables were retained in a higher proportion of occupancy models than broad-scale climatic variables. It should be noted, however, that we fitted occupancy models to fewer mammals compared to other species groups, which meant that the relative contribution of covariates in percentage terms was highly sensitive to the choice of model for each mammal species. There is mounting evidence that contemporary mammal declines in northern Australia have been influenced by fire frequency  and exteHigher predicted mammal richness in rugged terrain followed by coastal lowlands to the north and west is in broad agreement with current hypotheses about where mammals are persisting in the Top End. Monitoring and distribution data indicate that range contractions have occurred from south to north, with earlier and more severe mammal declines inland , and greUnlike birds, most studies of reptile distributions across northern Australia have focused on the influence of habitat variables at finer scales than we used in our analysis ,51, howeIn comparison to mammals and reptiles, relative bird richness is predicted to be highest across a large proportion of the study region due to the broad distributions of many birds throughout northern Australia . The relUnsurprisingly, we found high variability in the detection probability of birds, reptiles, and mammals using our standardised monitoring protocol. Contrary to our expectations, fire frequency had a negative influence on the detectability of mammals, reptiles, and birds, while ruggedness had both positive and negative effects on detectability. The role of site covariates such as these on detectability therefore requires further consideration using finer-scale covariate data. Camera trapping was relatively effective at detecting mammals compared with live trapping, which agrees with recent research for similar species . ImportaWe made several assumptions that may have influenced occupancy and detectability estimates. Firstly, the initial set of environmental covariates considered in the most complex models were identical for birds, reptiles and mammals. Alternatively, we could have specified unique models for each species group, or for each species, however, this would have increased the complexity of our analysis. Secondly, we ignored within year variability in detection probability or any differences in detectability between observers. Expanding our detectability models to test for observer and seasonal effects warrants further investigation. Thirdly, a mismatch in the initial resolution of covariate raster layers may have introduced uncertainty into the analysis. Finer-resolution mapping of the coarsely mapped covariates, particularly fire, will likely improve our understanding of their relationship with occupancy and detectability. Finally, the home range of species varied considerably relative to the area of surveyed sites, which influences our interpretation of occupancy. For species with home ranges similar in size to the sampling unit of 100 x 100 m , occupancy can be interpreted as the probability of a species being present at a site. However, many of the birds and large mammals will likely have home ranges much larger than this area, meaning occupancy for these species is better interpreted as probability of use. This assumes that the lack of closure between repeat surveys is random with respect to movement in and out of sites .There are several caveats with our relative species richness maps. Firstly, species richness maps were generated by summing the mean predicted occupancy across species. This approach ignores the variance around each species occupancy estimate, assumes all species are given equal weight, and can potentially over-predict richness at species-poor sites and under-predict richness at species-rich sites . SecondlWe generated relative occupancy and species richness maps by predicting our models to environmental covariates mapped across the Top End. In doing so, we extrapolated our predictions outside the geographic area that was sampled. This is common practice in species distribution modelling, provided care is taken to avoid extrapolating to sites where the response of species to environmental covariates might differ . We wereThe utility of models as a planning tool in conservation is influenced by the selection of relevant predictors . Our modOur species-level occupancy and detectability modelling combined with our species richness maps will inform redesign of the Three Parks Program. Firstly, our occupancy and species richness maps can provide a starting point from which to assess how existing or proposed monitoring sites align with species distributions. Decision-makers can then consider how new sites could be positioned to maximise either the spatial coverage of species or the number of species detected at sites. Furthermore, the location of sites can be compared with the expected or known drivers of occupancy to ensure adequate sampling across gradients, and relocated if necessary to stratify more effectively, thereby improving the ability to study faunal responses those drivers. For example, comparison between covariate values at sampled sites with values across the entire study region in this study suggests that the existing sites are fairly representative of some covariates . Thus, aOur detectability estimates for the six sampling methods will help evaluate which methods are worth retaining in the revised Three Parks program. Detectability estimate will also inform how much sampling effort is required to detect species with a given level of confidence. The results presented here can also inform similar monitoring decisions for related species outside of the study region that lack rigorous detectability estimates. An added benefit of estimating occupancy and detectability is that the statistical power of a revised monitoring program can be assessed. Statistical power is related to the number of sites, initial occupancy, detectability, the effect size, and the Type I error rate . Future Our work demonstrates that high resolution, broad-scale occupancy-detection modelling can be achieved using repeat presence-absence data that accounts for imperfect detection. Modelling repeat presence-absence data is an advance on the more widely used presence-only models  for The value of occupancy and detectability models is that they allow inference and prediction based on multiple, additive effects at multiple scales, thereby providing a solid basis for the spatial and temporal allocation of monitoring effort. We provided a snapshot of the current occupancy and detectability for a large proportion of the mammal, reptile and bird community across the Top End of northern Australia at a scale relevant for informing the redesign of a long-running monitoring program. Our results indicate where vertebrate species are likely to occur with respect to existing monitoring sites, and high variability in detectability between species and sampling methods. This information is crucial to the design of future monitoring programs across the Top End of northern Australia.S1 FigCovariates  at 1 km resolution used to model occupancy and detectability of 242 birds, mammals and reptiles recorded at 333 sites across the Top End of northern Australia.(TIF)Click here for additional data file.S2 FigProportion of models per animal group with 1–8 covariates included in the best model. Note, maximum covariate count for detectability models is 3, and method was included in detectability models for species with multiple methods of detection, but was not included in the covariate count presented here.(TIF)Click here for additional data file.S3 FigOccupancy maps for mammals with covariates in the best model. Light grey represents zero occupancy, while blue represents an occupancy probability of 1.(TIFF)Click here for additional data file.S4 FigOccupancy maps for reptiles with covariates in the best model. Occupancy maps for reptiles with covariates in the best model. Light grey represents zero occupancy, while blue represents an occupancy probability of 1.(PDF)Click here for additional data file.S5 FigOccupancy maps for birds with covariates in the best model. Occupancy maps for birds with covariates in the best model. Light grey represents zero occupancy, while blue represents an occupancy probability of 1.(PDF)Click here for additional data file.S6 FigFrequency histograms of covariate values occurring within the truncated  mapping region, showing the representativeness of sampling sites for birds (red), mammals (blue), and reptiles (orange) in comparison to the spectrum of environmental conditions to which species occupancy was predicted.(TIFF)Click here for additional data file.S1 TableSummary of the method of detection pooled to generate detection histories per animal group per location: Djelk Indigenous Protected Area (DIPA); Fish River Station (FRS); Garig Gunak Barlu National Park (GGBNP); Gregory National Park (GNP); Kakadu National Park (KNP); Litchfield National Park (LNP); Nitmiluk National Park (NNP); Warrdeken Indigenous Protected Area (WIPA). Note, species-specific detection histories were generated from a subset of all methods available per animal group.(PDF)Click here for additional data file.S2 TableCovariates with a Spearman’s correlation coefficient greater than 0.7 are shown in bold, with one of a pair excluded from the analysis.(PDF)Click here for additional data file.S3 TableModel coefficients for the occupancy component of the reptile models. Note, species containing only dashes were recorded during surveys but were unable to be modelled.(PDF)Click here for additional data file.S4 TableNote, species containing only dashes were recorded during surveys but were unable to be modelled.(PDF)Click here for additional data file.S5 TableNote, species containing only dashes were recorded during surveys but were unable to be modelled.(PDF)Click here for additional data file.S6 TableOccupancy and detectability estimates (over one day/night) for 83 birds modelled using diurnal active searches and spotlighting averaged across monitoring sites.(PDF)Click here for additional data file.S7 TableOccupancy and detectability (over one day/night) estimates for 33 reptiles modelled using pitfall trapping and/or spotlighting data averaged across monitoring sites.(PDF)Click here for additional data file.S8 TableOccupancy and detectability for 20 mammals modelled using live trapping and spotlighting averaged across 326 sites, and camera trapping averaged across 168 sites in northern Australia. Detectability estimates for live trapping and spotlighting is over the period of a day/night, and one week for camera trapping. * denotes introduced species.(PDF)Click here for additional data file.S9 TableNote, species containing only dashes were recorded during surveys but were unable to be modelled.(PDF)Click here for additional data file.S10 TableNote, species containing only dashes were recorded during surveys but were unable to be modelled.(PDF)Click here for additional data file.S11 TableNote, species containing only dashes were recorded during surveys but were unable to be modelled.(PDF)Click here for additional data file.