Input: Claim: Restoration of mangrove forests enhances coastal resilience against climate change. [SEP] Text: Responses to Sea-Level ChangeMean sea levels are currently rising as a result of both thermal expansion of ocean water due to increasing temperatures and the melting of polar and land ice [3]. The rise in sea level is not globally uniform as a result of water buildups in some areas, differences in water density due to varying sea temperatures, and the weight of ice sheets. Over geological time, as sea level has waxed and waned in relation to alternating periods of glaciation and warming, mangroves have had to adjust (or not) to rises and falls in sea level. Adjustments in Relation to Sea LevelA number of local and regional short- and long-term processes affect mangrove surface elevation relative to sea level. Eustatic changes in sea level, isostatic changes, subsidence, and changes in the subsurface volume of fluids (e. g. , oil, water) are the major long-term processes; short-term processes include accretion of fine sediment p9. 2 cal kp BP (Fig. 1). However, very abrupt changes in the vertical pattern of peat and carbonate deposits suggest that the mangroves drowned fairly quickly (Fig. 1), supporting other evidence of rapid sea-level change about 9–11 kp BP [12]. Buried mangrove peats lie in other coastal waters, such in the Caribbean [13] and the northern [23]. And while the thicknesses of these peat deposits indicate that mangroves were able to keep pace with the rise in sea level for long time periods, the rate of sea level increased beyond a critical threshold at which mangroves were not able to keep up [8•]. Fig. 1Photograph of a 1. 5-m-long sediment core taken in 22 m water depth at the group (Latitude 17° 13. 5′ S, Longitude 146° 05′ E) on the in February 2003. Note the abrupt demarcation halfway up the core between brown mangrove peat and more recent fine carbonate gray sand. Mangrove peat on the shelf is typically aged at 9. 3–9.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: Virtual marketplaces that leverage the Internet’s accessibility and anonymity are conduits for the distribution, trafficking and sale of a variety of prohibited services, products and commodities, including illicit drug trafficking, antiquities, human trafficking and illegal wildlife trafficking (Rosen & Rosen and Smith2010, Latonero2011, Campbell2013, Mackey & Mackey and Liang2013, Herrel & Van der Herrel and Van der Meijden2014, Guan & Guan and Xu2015, Mendel & Mendel and Sharapov2016, Mackey et al. Reference Mackey, Kalyanam, Katsuki and Lanckriet2017). Combating illegal online SNS wildlife sales is a race against time with criminal actors. By using big data and advanced data mining approaches, rapid detection of wildlife-related cybercrime is possible, particularly when leveraging computational methods of natural language processing, machine learning and deep learning informed by in-depth manual annotation to characterize content and interactions between sellers and buyers (Mackey et al. Reference Mackey, Kalyanam, Katsuki and Lanckriet2017, Li et al. Reference Li, Xu, Shah and Mackey2019, Xu et al. Reference Xu, Li, Cai and Mackey2019). Our study represent one phase of this solution through developing an approach to collecting data from one of the world’s most popular SNS platforms by simulating user searches and developing a manually labelled dataset that can be used for training in a machine learning inference phase. Importantly, this methodology can be used to eventually develop surveillance that occurs close to real time in order to detect, classify and report illegal wildlife posts for action by platforms in conjunction with law enforcement and conservation groups, as has been pursued for illicit online drug sales (Mackey et al. Reference Mackey, Kalyanam, Klugman, Kuzmenko and Gupta2018). Such solutions are crucial in the face of an evolving and growing wildlife cybercrime landscape. However, these efforts need to be supported with meaningful collaboration from international organizations, government agencies, technology companies, academia and non-governmental organizations and a decrease in demand for these products from the general public. Coordination of efforts to address Internet and SNS-based wildlife trafficking should start with more robust surveillance in multiple languages, including incorporating localized keywords and vocabulary specific to wildlife trade, while also being adaptive to changes in selling strategies and language.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Association between pig phenotypes and gut microbiotaWe identified several ASV whose relative abundances correlated significantly with feed efficiency and fatness traits (S2). A total of 16, 33, and 93 ASV were significantly associated with feed efficiency and fatness traits at 73, 123, and 158 days, respectively. They belonged mainly to 4 phyla: Firmicutes, Bacteroidetes, Proteobacteria, and Spirochaetes. At the genus level, 14 were classified as Ruminococcus, 12 as Clostridium, 10 as Eubacterium, 6 as Lactobacillus, 5 as Bacteroides, and 4 as Prevotella. Twelve ASV were classified as belonging to 7 other minor genera, and 79 were unassigned. We identified 20, 1, 20, 26, 61, 6, and 8 taxa that were significantly associated with 1, RF2, FCR, backfat, and loin depth, respectively, at the three different time points. The interaction effects between ASV and breed were significant for 2 and 48 ASV at 73 and 158 days (S3), respectively, while no significant interaction effects were observed at 123 days. The significant ASV belonged to the Firmicutes and Bacteroidetes. Eight of these belonged to the Faecalibacterium genus, 4 to the Eubacterium, 4 to the Bacteroides, 4 to the Oscillibacter, 4 to the Ruminococcus, 2 to the Anaerovibrio, and 24 were unassigned. Of the ASV identified, 22, 22, and 4 were significantly associated with 1, and FCR, respectively, at 73 and 158 days. We found across time points that the genus Peptococcus and Turicibacter were positively correlated (rs ~ 0. 27) with backfat at 123 and 158 days. The genera Faecalibacterium and Oscillibacter were negatively correlated with feed efficiency (rs = − 0. 22) at 73 days. A negative correlation (rs ~ − 0. 20) was also found between Anaerovibrio, Catenibacterium, Dorea, and Roseburia and fatness traits at 73, 123, and 158 days (Additional file 3).

Output: contrast


Input: Claim: Restoration of mangrove forests enhances coastal resilience against climate change. [SEP] Text: 1 ka BP [87]Full size image Pollen records reveal a response scenario of oscillations in mangrove succession in synchrony with changing sea level, such as on the island of Borneo [15], on the Caribbean coast of Colombia [16], on the in [17], in [21] and off the Galápagos [24], and tropical [14]. Mangrove change also occurs when fluctuating sea-level changes coincide with seismic events, resulting in abrupt changes in mangrove succession and recovery [20, 25•]. This pattern suggests a lowering of surface elevation most probably by seismic activity, but perhaps further affected by hurricanes [26] and peat collapse [8•]. Mangroves have responded not only to sea-level change, but also to changes in long-term shifts in temperature and precipitation. Excellent records of such climatic shifts come from Oman on the [19] and along the southwest coast of India [22, 27]. Modern EvidenceMangroves continue to respond to changes in sea level [8•]. Modern responses have been well-documented during the past 50 years using a variety of techniques including time series analysis of photos, remote sensing images, and digital terrain models to estimate mangrove expansion or contraction [10, 28, 29, 30, 31•], as well as methods to estimate modern rates of sediment accretion [5–7, 9, 32]. The modern evidence implies that mangrove responses to sea level rise correspond roughly to the patterns of surface elevation change described by McIvor et al. [8•]. For instance, along the Pacific coast of Mexico, rise in sea level accompanied by warm waters of El Niño events have drowned mangroves fringing the shoreline, but has resulted in a net increase in mangroves driven inland [29]. Local variability plays a key role in predicting whether or not mangroves of a specific region will survive or not, as local factors such as geomorphology are important. Also, mangroves occupy a range of tidal settings making it difficult to offer simple prognostications. The situation is similar on the Islands of Micronesia [32] where mangrove sedimentation is sufficient to offset elevation losses in some locations, but not in others; low intertidal mangroves are more susceptible to loss of elevation and subsequent flooding than in more landward zones.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: , Zommers, Z. , D’cruze, N. , and Macdonald, D. (2013). Rough trade: animal welfare in the global wildlife trade. BioScience, 63(12):928–938. Online: Bazurto, K. (2018). Tr fico de fauna silvestre en la reserva ecológica manglares churute 2015-2017. Tesis de grado. online: Universidad de Guyaquil. Berkunsky, I. , Quillfeldt, P. , Brightsmith, D. , Abbud, M. , Aguilar, J. , Alemán, U. , Aramburú, R. , Arias, A. , Balas, R. , Balsby, T. , Barberena, J. , Beissinger, S. , Rosales, M. , Berg, K. , Bianchi, C. , Blanco, E. , Bodrati, A. , Bonilla, C. , and Masello, F. (2017). Current threats faced by neotropical parrot populations. Biological Conservation, 214:278–287. Online: Bertolino, S. (2009). Animal trade and nonindigenous species introduction: the world-wide spread of squirrels. Diversity and Distributions, 15(4):701–708. Online: Biggs, D. , Cooney, R. , Roe, D. , Dublin, H. , Allan, J. , Challender, D. , and Skinner, D. (2017). Developing a theory of change for a communitybased response to illegal wildlife trade. Conservation Biology, 31(1):5–12. Online: International (2017). Iucn red list for birds. Web Page. Bush, E. , Baker, S. , and Macdonald, D. (2014). Global trade in exotic pets 2006-2012. Conservation Biology, 28(3):663–676. Online: Cedeño, E. (2020). Análisis del tráfico de fauna silvestre en la parroquia abdón calderón como una herramienta de gestión ambiental. Tesis de grado. online: del Sur de Manabí.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: We discovered within breed and across time points that the genus Oscillibacter was negatively correlated (rs ~ − 0. 30) with feed efficiency (RF1, RF2, and FCR), backfat, and loin depth in DR at 73 days. The genera Blautia, Dorea, Eubacterium, Faecalibacterium, Lactobacillus, and Ruminococcus, on the other hand, were positively correlated with feed efficiency (rs ~ 0. 15) and fatness traits (rs ~ 0. 22) in Duroc pigs at 123 and 158 days. Similarly, a negative correlation was obtained between the genus Sarcina and growth (rs = − 0. 38) as well as IMF (rs = − 0. 23) at 158 days (Fig. 7a–c). Fig. 7Heat maps showing Spearman correlations and level of significance between genera, feed efficiency, and fatness traits of Duroc, Landrace, and in three time points of the growth trial (73 days (T1) [a], 123 days (T2) [b], and 158 days (T3) [c]). Correlations were analyzed between ASV at the genus level. The color represents the correlation while the saturation represents the level of significanceFull size imageThe genus Oscillibacter was negatively correlated (rs ~ − 0. 17) with ADFI and feed efficiency in LR at 73 and 123 days. This maternal line also had a positive correlation (rs ~ 0. 17) at 123 and 158 days between ASV classified as genera Clostridium and feed efficiency. In addition, we found a positive correlation (rs ~ 0. 20) between four genera (Corynebacterium, Lactobacillus, Finegoldia, and Psychrobacter) and both ADFI and feed efficiency in LW pigs at 123 days, while the genus Desulfovibrio was negatively correlated (rs ~ − 0. 33) with ADFI, feed efficiency, and backfat at 73 and 158 days. We found within time points and across breeds that the genus Oscillibacter was negatively correlated with ADFI and feed efficiency for DR, LR, and LW pigs at 73 days.

Output: contrast


Input: Claim: Restoration of mangrove forests enhances coastal resilience against climate change. [SEP] Text: For a large number of species, leaf anatomy, vascular vessel densities, diameter, grouping and length, and fiber wall thickness are affected by variations in salinity and flooding. Vessel density, for instance, increases in most species from low to high salinity, from high- to low-flooding level, and from short- to long-flooding period [45]. A few species (e. g. , Avicennia germinans, Laguncularia racemosa) have also demonstrated modification to bark anatomy in response to prolonged flooding, typically formation of hypertrophied lenticels, adventitious roots, and increased aerenchyma development in the bark. Over the past 165 years, specific leaf area of A. marina has correlated positively with atmospheric CO2 concentration and latitude, suggesting thicker, heavier leaves in future because of net photosynthetic carbon gain [46•]. Mangroves of the future may very well look and function differently, and be denser in terms of number of individuals per unit area [47, 48]. But, are mangrove forests keeping pace with current rates of sea level rise? A statistical analysis (Fig. 2) of mangrove sedimentation rates (mm year−1) versus mean sea level rise (mm year−1) suggests that, on average, accretion rates are keeping pace with sea level rise (see regression equation and line, Fig. 2). However, roughly one-half of the data are below the 1:1 relationship (red dotted line) which indicates that these mangroves are not keeping pace; nearly all of these data were derived from mangroves inhabiting low islands and coastal lagoons in the Caribbean and the Pacific, and in subsiding river deltas, such as the Mekong [18] and the Sundarbans [49]. Conversely, the three most rapid accretion rates were from Chinese, Indian, and Brazilian mangroves inhabiting highly populated and impacted catchments. The large scatter of data points underscores how mangroves of disparate coastal settings respond so differently to the same rate of sea-level rise in different parts of the world. These century-scale accretion rates derived from radionuclide distributions (e. g. , 210Pb) are unlikely to be reliable indicators of the net result of elevation capital, the accretion rate, and water level.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4477375/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractIllegal wildlife trade (IWT) is a huge gray industry with a market valuation of billions of dollars. Not only does this trade pose a serious threat to the safety of human and animal life, it is also one of the major challenges facing biodiversity today and can lead to a range of serious problems including the spread of zoonotic pathogen and invasive alien species. Although policymakers have been able to respond relatively quickly, the current regulation and crackdown on illegal wildlife trade is still inadequate. Using the as an example, this study employs a statistical modeling approach that employs principal component analysis and hierarchical analysis to effectively delineate and assess power, resources, and interests in the global wildlife conservation enterprise. We conducted an in-depth analysis of the impact of multiple national indicators and policy assumptions on the illegal wildlife trade, and used ARIMA models to analyze trade data before and after the implementation of interventions to predict trends over the next 14 years. By visualizing sales data and active trade pairs over the years, this study provides a scientific basis for government decision-making. The results of the study show that wildlife trade is significantly affected by specific regional and national indicators. In addition, our proposed modeling framework reveals previously overlooked influences in the illegal versus legal wildlife trade and promotes the development and implementation of effective interventions to reduce the damage of illegal trade to wild populations. We expect this study to contribute to the development of global wildlife conservation and promote more international cooperation and policy innovation. Biological sciences/EcologyEarth and environmental sciences/BiogeochemistryEarth and environmental sciences/Climate sciencesEarth and environmental sciences/EcologyEarth and environmental sciences/Environmental sciencesEarth and environmental sciences/Environmental social sciencesHealth sciences/Health occupationsPhysical sciences/Energy science and technologyPhysical sciences/Mathematics and computingHierarchical analysiswildlife tradeFiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 101. IntroductionThe value of each service provided by the ecosystem is substantially affected by human activities, and conversely, its changes will also affect the relevant human decisions. [1]Illegal wildlife trade refers to the commercial trade in wildlife and its derivatives without official authorization.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The genera Anaerovibrio, Clostridium, Faecalibacterium, Eubacterium, and Ruminococcus, showed negative correlations with feed efficiency and backfat in both LR and LW at 73 days. ASV classified as Dorea, Eubacterium, and Lactobacillus were positively correlated with feed efficiency in DR and LW at 123 days. Additionally, the genus Blautia was positively correlated with feed efficiency in DR and LR at 123 days. ASVs classified as Dorea and Lactobacillus were positively correlated with ADFI and feed efficiency in DR and LW at 158 days. DiscussionIn the past decade, many studies have investigated the potential impact of the swine microbiome on different phenotypes [9, 11]. However, few studies have compared the gut microbiomes of different pig breeds [21, 24]. To the best of our knowledge, this is the first large-scale study that explores the relationship between breed, gut microbiome composition, and feed efficiency. The data used in this study were collected from DR, LR, and LW, which are the most common commercial breeds worldwide due to their favorable lean growth, maternal behavior, feed efficiency, and production traits [25, 26]. The gut microbiome of all three breeds was dominated by the phyla Firmicutes and Bacteroidetes, consistent with previous findings [9, 19, 27]. However, the most abundant bacterial family in the breeds characterized here was Lactobacillaceae, rather than Clostridiaceae and Prevotellaceae, as reported by Lu et al. [9]. Several factors potentially contributed to gut microbiota variation, among them breed, age, body weight, and diet [17]. Furthermore, previous studies revealed a significant effect of host genetics and environmental factors (e. g. , pen, kinship) on gut microbiome composition [14, 28]. Evidence of host genome influence on microbiome composition has been reported in humans by Goodrich et al. [29], and studies of the pig gut microbiome have reported non-zero heritability estimates of certain microbial taxa [30]. A genome-wide association study has also recently been conducted to explore potential links between single-nucleotide polymorphisms within the pig genome, and the abundance of taxa was shown to be significantly associated with growth and fatness parameters [31].

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: Figures AbstractSeveral odontocete species depredate catch and bait from fishing gear, resulting in their bycatch and causing substantial economic costs. There are no known mitigation methods for odontocete depredation in pelagic longline fisheries that are effective, do not harm odontocetes and are commercially viable. Understanding odontocetes’ depredation strategies can contribute to mitigating this human-wildlife conflict. Using observer data from the Hawaii-based tuna longline fishery, this study summarized teleost and elasmobranch species-specific mean posterior odontocete depredation rates using a simple Bayesian binomial likelihood estimator with a Bayes-Laplace prior. Depredation rates of species with sufficient sample sizes ranged from a high of 1. 2% (1. 1 to 1. 3 95% highest posterior density interval or HDI) for shortbill spearfish to a low of 0. 002% (0. 001 to 0. 003 95% HDI) for blue shark. Depredation of catch is a rare event in this fishery, occurring in about 6% of sets. When depredation did occur, most frequently odontocetes depredated a small proportion of the catch, however, there was large variability in depredation rates between teleost species. For example, bigeye tuna was two times more likely to be depredated than yellowfin tuna (odds ratio = 2. 03, 95% CI: 1. 8–2. 3, P<0. 0001). For sets with depredation, 10% and 2% of sets had depredation of over half of the captured bigeye tuna and combined teleosts, respectively. All elasmobranch species had relatively low depredation rates, where only 7 of almost 0. 5M captured elasmobranchs were depredated. Odontocetes selectively depredate a subset of the teleost species captured within sets, possibly based on net energy value, chemical, visual, acoustic and textural characteristics and body size, but not median length, which was found to be unrelated to depredation rate (Pearson’s r = 0. 14, 95% CI: -0. 26 to 0. 50, p = 0. 49).

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: This type of illegal activity is widespread globally and constitutes a major challenge in wildlife conservation. Illegal trade and overhunting pose a great threat to endangered species, cause serious damage to biodiversity, and are one of the key factors leading to species extinction[2]. As shown in Figure 1, the heat map of illegal wildlife trade in global public health problems shows that North America, especially the and Canada, is the major illegal exporter. Asia, in particular China, is the main importer. The volume of illegal trade imports from the is also considerable. As can be seen from Figure 2, animal furs and live animals are favored in the illegal wildlife trade market because of their uniqueness and high economic value. The willingness of certain affluent individuals to pay high sums of money to acquire these rare items to satisfy their personal hobbies has become one of the incentives for poachers to engage in illegal hunting, thus making wildlife trapping one of the ways in which outlaws can make high profits[3][4]. And wild animal trade, including capture and transport of animals from faraway lands to European and American markets for public exhibition, became a well-established business by the nineteenth century[5]. Table 1 Sources and main areas of distribution of zoonotic pathogens involved Range of transactions Erysipelothrix rhusiopathiae Wild boar E virus Wild boar, deer encephalitis virus (JEV) Wading birds such as egrets and herons South-and the Kyasanur forest disease and small mammals Nipah virus Cercopithecine herpes virus Macaques, rhesus monkeys virus(HeV) Fruit bat (NiV) Fruit bat and squirrels virus disease (EVD) Wild fruit bats West African, Liberia and States is not only a major producer of such products, but the scale of illegal wildlife trade within its borders is also extremely large, which further justifies the use of the as a study case. In addition, the illegal import and export of wildlife as a food source may also pose a public health risk. Most infectious diseases are zoonotic, & ldquo;jumping & rdquo; from animals to humans, with COVID-19 no exception[6]. A wide range of zoonotic pathogens closely related to wildlife have the potential to spread from the exporting region to consumers in different regions.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: A significant association between the pig genome and the relative abundance of six relevant genera was also found by Crespo-Piazuelo et al. [28]. Landrace pigs had a more diverse gut microbiome compared with the other two breeds, in agreement with Pajarillo et al. [21]. Some researchers have suggested that very low diversity in a microbiome is a good predictor of poor health status [32, 33]. Landrace pigs were also characterized by higher feed efficiency compared to DR and LW, which is consistent with findings reported by Quan et al. [34] who suggested a higher Shannon index in high feed efficiency crossbred pigs. The gut microbiome composition of DR pigs was significantly different from that of LR and LW animals, while the LR and LW microbiomes were more similar. At the genus level, Catenibacterium, Clostridium, and Turicibacter significantly influenced the separation between sire and maternal lines across the time points considered. The association between the genus Catenibacterium and DR compared with LR and LW was also previously reported by Pajarillo et al. [21]. In mice, it has been demonstrated that prevalent bacterial genera with a higher abundance in the same breed usually have a phylogenetic or functional association with their host [35]. Comparing the two maternal lines, LR pigs had a significantly higher proportion of Bacteroides than LW pigs across time points, instead of Lactobacillus as reported by Pajarillo et al. [21]. had higher ADFI and FCR compared to LR. It is known in pigs that the type and quantity of feed consumed can affect both host biology and the relative abundance of different bacterial species in the gastrointestinal tract. For example, Frese et al. [36] indicated that the diet composition can widely modify the gut microbiome composition (e. g. , increase of Prevotella), which reflected the different functional capacities of the microbial community in different time points. A variation across time point was observed with an increase in abundance of some taxa associated with Firmicutes and Bacteroidetes in the gut of DR pigs compared to LR and LW. A significant change in the ratio of Firmicutes to Bacteroidetes in the gut of developing pigs over time and after cohabitation was observed [37, 38].

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: Several species of odontocetes (Odontoceti, toothed whales), including the false killer whale (Pseudorca crassidens), short-finned pilot whale (Globicephala macrorhynchus) and killer whale (Orcinus orca), depredate catch and bait in pelagic longline fisheries, which can result in their bycatch by becoming hooked or entangled in line [20, 21]. A U. S. central north tuna longline fishery with vessels based primarily from Hawaii experiences odontocete depredation and bycatch primarily by false killer whales [22]. Under the U. S. Protection Act, a Team was established in 2010 because the five-year running average of the estimated annual false killer whale mortalities and series injuries (M&SI) exceeded a threshold Removal (PBR) level. The team was tasked with reducing M&SI to below PBR within six months and to a level approaching zero, defined as <10% of PBR, within five years, and was further tasked with having no increase in M&SIs for a high seas false killer whale stock [23]. To date, existing measures recommended by the team and adopted by the US fisheries management authority (‘weak’ circle hooks, permanent static closed area, fleetwide bycatch cap that triggers an area closure, prescribed handling and release practices [23]) have not achieved <10% of PBR, and M&SIs on the high seas have increased [22]. More effective false killer whale depredation and bycatch mitigation methods are needed. Unlike for some other threatened taxonomic groups exposed to bycatch in pelagic longline fisheries, there are no known odontocete methods across the tiers of a sequential bycatch mitigation hierarchy (avoid, minimize, remediate and offset) that are: (1) effective, (2) do not harm odontocetes, and (3) have acceptable costs to commercial viability (economic viability, practicality and crew safety) [15, 21, 24, 25]. There is low strength of evidence of the efficacy and commercial viability of bycatch and depredation mitigation approaches for odontocetes in pelagic longline gear.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: These diseases usually originate in animals and can be transmitted to humans across species barriers, with most of their sources associated with wildlife[12-17]. There is therefore an urgent need to enact measures to restrict the illegal wildlife trade. We must reduce the negative impacts of the illegal wildlife trade in order to ensure the sustainable development of wildlife resources and to maintain the ecological balance. 1. 2 Basis of previous research Biological resources are indispensable and valuable to humankind and have played a key role in the development of human civilization. However, illegal wildlife trade, covering smuggling, poaching, illegal capture or collection of endangered species and their derivatives, has evolved into a serious global problem. Such trade not only poses a serious threat to biodiversity, but also may have unpredictable and disastrous consequences for human societies[18]. Considering the illegal nature of illegal wildlife trade and the intricate criminal networks behind it, it is extremely challenging to effectively characterize, quantify and regulate it. Therefore, we must adopt a more rigorous and prudent attitude and utilize rational thinking and official means to crack down on these illegal behaviors in order to protect the safety of our biodiversity and ecological environment. We found that the meat trade is still poorly quantified, which limits the effectiveness of state authorities and other relevant agencies in regulating and controlling the illegal wildlife trade. The lack of precise data makes it difficult to track and quantify each trade, so regulators usually have to rely on officially released data or databases to analyze in order to draw relatively accurate conclusions. This situation reduces the reliability of the data and can lead to insufficient public attention to the problem of illegal wildlife trade. For example, in the United States, our findings are comparable to the lower bound of global estimates, suggesting that the global economic value of the illegal wildlife trade may be underestimated[19]. Patel and Nikkita Gunvant, in their study Characterization of Trade Networks, used data from two databases, CITES and HealthMap, to quantify the illegal wildlife flora and fauna trade in relation to several key factors and compared product types, origins, destinations, and trade routes in legal and illegal trade. These analyses have been very helpful in identifying priority locations for resource allocation[20]. In addition, many of the conclusions were drawn by synthesizing and analyzing news reports and other people's research findings, lacking an independent validation process and failing to test the accuracy of the conclusions by rederiving others' mathematical models. For example, the study, "of the Trade: of U. S.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Changes in the abundance of Firmicutes and Bacteroidetes have been associated with a modification in the carcass fat deposition [39]. Bacteroidetes had a negative correlation with fat mass and had the ability to utilize various sugar derivatives from vegetables in pigs [40]. At the genus level, the microbiome shift over time was mainly related to Oscillibacter, Prevotella, Campylobacter, and Treponema. The relative abundance of Oscillibacter and Prevotella increased as the pigs aged in DR compared to LR and LW. The proportion of Campylobacter increased over time in LR compared to LW, while Treponema decreased. An increase in the average proportion of Prevotella in the 15 weeks after weaning was also observed by Lu et al. [9]. Regarding a possible biological function of these genera in the gastrointestinal tract, Oscillibacter was reported as a probiotic and producer of anti-inflammatory metabolites [41], while Prevotella is known to be involved in the degradation of both plant-based dietary polysaccharides [42]. A higher relative amount of Prevotella was also reported in both high and low feed efficiency Landrace pigs by Tan et al. [19]. Lastly, taxa classified as Treponema were the main driver of the enterotype-like group within the pig gut microbiota associated with growth traits [43], which was involved in cellulose and lignin degradation [16]. It is expected that host genetics has the potential to meaningfully influence the gut microbiota [9, 12, 35], and consequently feed efficiency, by favoring or disfavoring microbes that significantly contribute to nutrient digestion and energy harvest. Therefore, the gut microbiome composition could be associated with intestinal morphology and physiology that can impact the production traits such as growth and feed intake [44]. Characterizing the relationship between gut microbial composition and feed efficiency revealed a positive association between four genera (Lactobacillus, Blautia, Dorea, and Eubacterium) and feed efficiency. In agreement with our results, Verschuren et al. [45] found a positive effect of Lactobacillus on the feed efficiency of three-way cross pigs. Wang et al. [46] reported an effect of Lactobacillus on growth and fat deposition in broiler chickens.

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: Length measurement methods of eye-to-fork was used for billfishes and fork length for all other species, as these produced the largest sample sizes for species within these groups. The expected depredation rate (or proportion) for each of the 50 species was estimated using a Bayesian binomial likelihood estimator that here accounts for the zero recorded depredation events [47]. A simple binomial estimator is the most appropriate likelihood for these data that comprise the number of depredated observations and the number of not-depredated observations per set (or “successes” and “failures”)—see Lin and Chu [48] for further discussion. Moreover, 40% of the 35 species-specific records had zero depredations per set and hence the use of a Bayes-Laplace prior to account for those zero observations explicitly when using a binomial estimator ([47]; and see Gilman et al. [49] for an example for shark at-vessel mortality rates). The mean posterior rate and a 95% highest posterior density interval (HDI) was summarized by sampling from a binomial likelihood with a Bayes-Laplace prior [47] using the binom package for R [50] as proposed elsewhere by Roda et al. [51] and Gilman et al. [49]—rather than just using the raw study-specific summaries. The relationship between predicted mean depredation rates and median lengths was assessed for teleosts with > 1,000 capture records by calculating the Pearson product-moment correlation coefficient [52] using the corr() and cor. mtest () functions in the corrplot R package [53]. There were insufficient sample sizes available to estimate expected marine mammal depredation rates by length class within species (of 1,038,586 species-level catch records with length values, only 44 were identified as having marine mammal depredation)–observers are typically unable to make common length measurements (total length, eye-to-fork length, fork length) of catch depredated by marine mammals because the depredated catch tends to be retrieved with only the fish head up to the gills, or just the lips and upper jaw of the fish remaining [15, 16].

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: Four indicators, such as the percentage of wildlife trade shipments rejected or deemed illegal out of the total shipments collected, the estimated number of elephants illegally killed per year, the number of wildlife seizures in the world, and the number of poaching incidents in Africa, were used as independent variables in the evaluation indicator system. Because of data anomalies and gaps in these data, we pre-processed the data using interpolation and outlier removal. 3. 2 Defining a system of evaluation indicators In view of the large number of secondary indicators in the evaluation index system, we used the principal component analysis (PCA) method for data dimensionality reduction in order to extract key components. Considering that there are differences in the quantitative levels of the collected indicators, it is first necessary to standardize the data of these indicators and transform them into standardized indicators with no quantitative levels. Through this process, we successfully extracted four key components from a large number of secondary indicators and clearly presented the results of these components through visualization techniques, thus facilitating a more intuitive understanding of the analysis conclusions. 3. 3 (AHP) is a new, simple and practical modeling methodology designed to deal with complex and ambiguous decision-making problems. It is particularly suited to system control environments where a large number of interrelated and interacting constraints exist, and where the lack of sufficient quantitative data often makes a fully quantitative analysis difficult. AHP is able to deal effectively with a wide range of interrelated and interacting constraints in the field of system analysis for social, economic and scientific management. In practical application, we compared the client's -Composite Index, and on a two-by-two basis. By building a judgment matrix and normalizing the data, we used the summation and product methods to determine the weights of each indicator. In addition, we conducted a consistency test ($CI=0. 003<1$) to ensure the reliability and validity of the model. This approach not only enhances the scientific nature of decision-making, but also improves the transparency and traceability of the decision-making process. 3. 4 Defining policy approaches Based on the selected national indicators and their weights, we developed three policy measures aimed at reducing illegal wildlife trade. To assess the potential effects of these policies, we conducted a predictive analysis of trends in illegal wildlife trade over the next 14 years using an ARIMA model. 4. Results4.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Some genera of the family Lactobacillaceae were involved in the production of antimicrobial bacteriocins, which are related to gut microbiome composition [47]. A species of the genus Lactobacillus with the ability to promote intestinal metabolism was identified in a cluster of highly feed-efficient DR and LR pigs [14, 19]. According to this study, Yang et al. [14] and Quan et al. [34] reported positive associations between taxa classified as Ruminococcaceae and feed efficiency in DR and crossbred pigs. Interestingly, taxa belonging to Ruminococcaceae are able to produce short-chain fatty acids fermenting dietary polymers, such as polysaccharides, that are not degradable by the host [48]. Fermentation products may influence several aspects of the gastrointestinal tract, such as transit time and nutrient digestion [49]. Moreover, previous studies in swine demonstrated that an increase in the production of short-chain fatty acids could improve the absorptive capacity of the intestine, promoting the growth of beneficial bacteria [49], thereby increasing feed efficiency [14, 34]. ConclusionsHere, an effect of breeds characterized by different feed efficiency on gut microbiome was discovered. Microbiome differences between breeds were found mainly associated with the genera Catenibacterium, Clostridium, and Bacteroides. These results suggest that host genetics has an essential effect on the structure and composition of the pig gut microbiome. Association analyses between the gut microbiome and feed efficiency revealed a positive association between Blautia, Dorea, Eubacterium, Lactobacillus, and feed efficiency in pigs. We provide evidence that the gut microbiome was correlated with feed efficiency and fatness traits, which might be relevant to understand how the intestinal microbial community influences the host production traits. Therefore, these results suggest that the intestinal microbial composition can provide important knowledge in order to improve the feed efficiency of pigs in pork industry. MethodsExperimental design and sample collectionAnimal use approval was not needed for this study because the data analyzed were from an existing database provided by Genetics (). Data points from Duroc (DR) (n = 190), Landrace (LR) (n = 221), and (LW) (n = 204) boars were used.

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: In other words, we were able to conduct analyses to determine whether catch with larger median lengths had different expected mean odontocete depredation rates than species with smaller median lengths, but not on whether larger individuals within a species had a different odontocete depredation rate than smaller individuals of that species. The proportion of total sets with odontocete depredation of ≥1 fish was determined. The proportion of the total catch of bigeye tunas (Thunnus obesus), yellowfin tunas (T. albacares) and combined teleosts that had odontocete depredation was determined for (1) sets with observed odontocete depredation of ≥ 1 fish, and (2) sets with observed odontocete depredation of ≥ 1 bigeye tuna, yellowfin tuna or teleost. We then also compared the depredation rate for bigeye and yellowfin tunas, the principal target species of this fishery, using the odds ratio [54] calculated by conditional maximum likelihood or Fishers exact procedure using the epitools R package [55]. 3. ResultsTable 1 presents the mean posterior depredation rates (percent of the catch that was depredated by marine mammals) with 95% HDIs and median and mean lengths. For species with >1,000 capture records, depredation rates ranged from a high of ca. 1. 2% (1. 1 to 1. 3 95% HDI) for shortbill spearfish to a low of 0. 002% (0. 001 to 0. 003 95% HDI) for blue shark. Bigeye tuna was at least two times more likely to be depredated than yellowfin tuna (odds ratio = 2. 03, 95% CI: 1. 8–2. 3, P<0. 0001). Elasmobranchs had extremely low marine mammal depredation rates, where of a total of 442,377 captured elasmobranchs identified to the species level and for which the observer determined whether marine mammal depredation occurred, 7 were depredated (2 bigeye thresher sharks and 5 blue sharks) (Table 1). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 1.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: The implementation of these policy programs will require coordinated cooperation between various sectors of the national government, as well as close cooperation with non-governmental organizations and the private sector. Through these integrated measures, illegal wildlife trade can be radically reduced, thereby conserving biodiversity and contributing to sustainable economic and social development. Figure 10 shows the prediction of the quantity of illegal wildlife trade over time based on real data versus the implementation of the policy program. As can be seen from the figure, the real data show that illegal wildlife trade is on an upward trend, and the volume of illegal trade increases year by year. In the prediction of ARIMA time series model, the MAPE value is 0. 067398%, which is much lower than the linear regression model, showing that the percentage error between the predicted value and the actual value of ARIMA model is extremely small, and its RMSE value is 0. 072324, which is significantly lower than that of the linear regression model, indicating that the ARIMA model has less error in prediction. Although the R-squared value of ARIMA is 0. 66939, which is lower than the linear regression model, showing that its fitting effect is not as good as the linear regression model. Whereas the linear regression model has a relatively high MAPE value of 1. 5278% and a root mean square error (RMSE) value of 0. 62662, its R-squared value of 0. 84305 indicates that the model explains 84. 305% of the variance of the target variable better, which usually indicates that the model is relatively well fitted. Thus, curbing illegal wildlife trade requires Fig. 10 Prediction of the Index by the ARIMA model with time growth a long-term commitment and sustained effort. Although the results may not be satisfactory in the short term, adopting the right strategies and sustained policy implementation are essential for the conservation of wildlife and their natural habitats. To ensure that these efforts produce tangible results, a comprehensive strategy of international cooperation, scientific approaches and community involvement is needed. In the long run, conserving wildlife is not only responsible for biodiversity, but also for the future of human existence. Therefore, despite the many challenges, we must persevere and continue to optimize and adjust our strategies with a view to achieving the ultimate conservation goals. 5.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: DNA extraction and 16S rRNA gene amplicon sequencing of fecalDNA was extracted from each rectal swab using the method reported in detail by Lu et al. [9]. Phased, bi-directional amplification of the V4 region (515–806) of the 16S rRNA gene was employed to generate indexed libraries for Illumina sequencing in the manner previously described [9]. All sequencing was performed at the at the Center for at in St. Louis (USA). The raw sequence data from the Illumina platform were converted into read files using MiSeq Reporter. Pairs of V4 16S rRNA gene sequences were first merged into a single sequence using FLASH v1. 2. 11 [50] with a required overlap of at least 100 and no more than 250 base pairs in order to provide a confident overlap. Sequences were oriented in the forward direction, and any primer sequences were trimmed; during primer matching, up to 1 mismatch was allowed. Sequences were imported into Ecology (QIIME2 version 2017. 12, for demultiplexing and the construction of an amplicon sequence variant (ASV) feature table using the 2 (DADA2) [51] with default settings and no truncation or length filtering (--p-trunc-len 0). Features present in only 1 sample were removed from the table. Finally, the Project (RDP) classifier (v2. 4) was retrained in the manner previously described [52] and used to predict taxonomic assignments for each ASV sequence using a confidence cutoff of 0. 8. Results were then exported for further analysis in the R environment [53]. Data editingFeeding efficiency traits and body weight were obtained from the FIRE system records. To obtain an accurate prediction of individual feed intake and body weight, data editing was required [5]. In this study, feed intake records were edited on the basis of the procedure proposed by [5, 54], while body weights were edited following the procedure reported by [26, 55]. We fit a robust regression model using the “mass” [56] package in R [53], with age and squared age as covariates. Each data point was assigned a weight (from 0 to 1) to minimize the influence of extreme values. Data with weights of less than 0. 5 units were treated as outliers and removed before any statistical analysis [55].

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: Study sample sizes, mean posterior depredation rates and 95% highest posterior density intervals (HDIs), and length summaries for fish species with >100 catch records for which an observer determined whether there was marine mammal depredation in the Hawaii longline tuna fishery (2003–2023). There were 179,853,812 observed hooks in 73,217 sets. Lengths are eye-to-fork for billfishes and fork length for all other species. Sorted from high to low mean depredation rate within 5 taxonomic groups. 1 presents the predicted mean depredation rate (proportion of the catch with marine mammal depredation) and 95% HDIs for the four species of tunas included in Table 1, the five species of billfishes included in Table 1, and the six species of other teleosts with the highest predicted mean depredation rates and with sufficient sample sizes of >1,000 capture records. Species are sorted from lowest to highest depredation rate within each of the three taxonomic groups. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 1. Mean posterior depredation rates (proportion of the number captured that was depredated by odontocetes) and 95% highest posterior density intervals (HDIs) for tunas, billfishes and six other teleost species with the highest predicted mean depredation rates and with >1,000 capture records in the Hawaii tuna longline fishery (2003–2023). Dot size is proportional to catch for that species relative to the catch for all three taxonomic groupings. with this species-selective depredation, there was high variability in the species-specific set-level marine mammal depredation rates. For example, of sets with ≥ 1 depredated bigeye tuna, the main target species of this fishery, 94% had depredation of over half of the captured bigeye tuna. This rate was 42% for yellowfin tuna (i. e. , of sets with ≥ 1 depredated yellowfin tuna, 42% had depredation of over half of the captured yellowfin tuna), and 2% for combined teleost species.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: ConclusionThis study mainly used principal component analysis (PCA) and hierarchical analysis (AHP) to effectively a composite index of sustainable development, a comprehensive index of economic sustainability in the global wildlife conservation endeavor, a composite socio-economic development index with a composite index of economic openness. were classified and evaluated. Through exhaustive analysis, we reveal the correlations between different indicators, providing a solid scientific basis for the development of targeted wildlife trade policies. Of particular note, our analysis highlights the key role of the in the illegal wildlife trade and points to it as a major importing and exporting region, a finding that prompts us to focus our research on interventions targeting this region. The interventions implemented were tailored to the key factors identified and were designed to significantly reduce illegal wildlife trade. By analyzing data on illegal wildlife trade before and after the implementation of the interventions using an ARIMA model, we predicted trends over the next 10 years. The results of the analysis showed that despite the interventions having some positive impact on curbing illegal trade, the overall score for illegal wildlife trade is still trending upwards. This suggests that current interventions need to be strengthened, or that we need more diverse and robust measures to address this challenge. In addition, by visualizing data on sales and active trade pairs over the years, we not only clearly identified hotspots of illegal trade, but also were able to effectively track the effects of interventions. This visualization approach not only helps policy makers to deeply understand the dynamics of the illegal wildlife trade, but also provides an intuitive platform for the public to understand, and enhances the awareness and motivation of the public to participate in the protection of wildlife. Therefore, while our intervention programs have had some positive impact, there is still room for improvement. Future work should focus on developing and implementing more effective strategies to further reduce illegal wildlife trade globally. In addition, ongoing monitoring and evaluation is essential to gain a deeper understanding of the long-term effects of interventions. Our study provides valuable data and analytical tools for wildlife conservation policy development and is an important contribution to global wildlife conservation efforts. Through the data-driven analysis of this study, we not only clearly demonstrate the feasibility and necessity of the project, but also successfully validate the accuracy of the model in simulating real-world situations. Our simulation results, presented through a highly realistic visualization interface, provide decision makers with a powerful tool to help them fully understand the applicability of the model in real-world applications and its limitations.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Microbiome composition differences across breedsThe ASV for 73, 123, and 158 days were analyzed using a negative binomial model with the “edgeR” [58] package in R, including the fixed effects described in the model (1). Orthogonal contrasts were fitted as reported above to obtain ASV significance among breeds. The Benjamini–Hochberg method was used for multiple-testing correction [59]. A false discovery rate of 5% was used to declare whether or not an ASV was significantly different between breeds. Jensen-[60] was calculated for each breed at the three separate time points according to the relative abundance of each genus in each sample using the “dist. JSD” function from the phyloseq package in R [61, 62] to identify enterotypes among the samples. Based on the obtained distance matrix, the samples at each time point were clustered via partitioning around medoids by using the “pam” function in the R package “cluster” [63]. The optimal number of clusters was chosen by maximizing the Calinski–Harabasz index [64], using the “index. G1” function in the R package “clusterSim” [65], and the Silhouette index [66], using the “silhouette” function in the R package “cluster” [63]. To identify genera that had a significant effect on the division of the enterotypes at each time point, the LDA based on the non-parametric Kruskal–Wallis sum-rank test was performed [67] with an alpha value of 0. 05 for the factorial Kruskal–Wallis test among classes and a threshold of 2 on the logarithmic LDA score for discriminative features using the galaxy/hutlab website ( associationWe investigated the effect of ASV at 73, 123, and 158 days on performance traits and residual feed intake using the following model with the in SAS 9.

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: Similarly, for sets with depredation of ≥ 1 fish of any species, 10%, 4% and 2% of these sets had depredation of over half of the captured bigeye tuna, yellowfin tuna and combined teleosts, respectively. For sets with depredation of ≥ 1 fish of any species, 4%, 2% and 0. 1% of these sets had odontocete depredation of all the captured bigeye tuna, yellowfin tuna and combined teleosts, respectively. Of the sets in the study sample, 6. 2% had observed odontocete depredation of ≥ 1 fish. There was no significant relationship between median length and mean depredation rate: Pearson’s r = 0. 14 (95% CI: -0. 26 to 0. 50), p = 0. 49. The species-specific median length of the catch was not an informative predictor of odontocete depredation rate of teleosts. 4. Discussion and conclusionsMarine mammal depredation is a rare event in this fishery, occurring in about 6% of sets. When depredation did occur, most frequently a small proportion of the total catch was depredated. However, there was large variability in odontocete depredation rates between teleost species, while all species of captured elasmobranchs had negligible odontocete depredation. This is consistent with previous studies of the observer program database for the Hawaii tuna longline fishery [44, 45]. Findings suggest that odontocetes selectively depredate a subset of the teleost species captured within sets. Odontocetes are not indiscriminately depredating the catch down the line, but are selectively depredating certain teleost species. Different species of pelagic marine predators, and sizes within species, have different prey p1. Bowen WD. Role of marine mammals in aquatic ecosystems. Prog Ser. 1997;158: 267−274. View 2. Whales as marine ecosystem engineers. Environ. 2014;12: 377−385. View 3. . , Bakker E, et al. Global nutrient transport in a world of giants. . 2016;113: 868–873. pmid:26504209 View 4. . , Williams T. Causes and consequences of marine mammal population declines in southwest Alaska: A food-web perspective. .

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: We expect this research to contribute to the advancement of global wildlife conservation and to promote international cooperation and policy innovation. 6. DiscussionsIn this study, the core influencing factors of the composite index of sustainable development, the composite index of economic sustainability, the composite index of socio-economic development, and the composite index of economic openness in the global wildlife conservation endeavor are explored in depth through the combined application of principal component analysis (PCA) and hierarchical analysis (AHP). The application of these statistical methods not only reveals the correlation between the indicators, but also provides solid data support for the development of targeted policies. In particular, in analyzing the case of illegal wildlife trade in the U. S. , we find that despite the implementation of several interventions, the predictive model shows that illegal trade is still on the rise. This demonstrates the limitations of current intervention strategies and the need for future strategy adjustments. Curbing illegal wildlife trade is a long-lasting battle, and we should be brave enough to take the first step. Although illegal trade may still rise in the short term, in the long term, protecting wildlife is protecting human beings themselves. While interventions have had some impact on curbing illegal wildlife trade in the short term, long-term projections show that they have failed to fundamentally reverse the growth of illegal wildlife trade. This situation may be due to a complex combination of factors, including not only insufficient enforcement and possible omissions and sheltering behaviors in practice, but also deficiencies in international cooperation, especially communication and collaboration at the transnational level, which needs to be strengthened. In addition, the different regulatory policies formulated by countries may also lead to a series of contradictions and conflicts. Therefore, these external factors should be taken into account more comprehensively in the process of future policy formulation in order to effectively promote the sustainable development of global wildlife conservation. In view of the continuing upward trend in the illegal wildlife trade, it is recommended that future interventions should be more diversified and targeted. First, support to source countries should be strengthened to enhance their wildlife conservation capacity. This includes strengthening the construction and management of nature reserves and wildlife sanctuaries, as well as supporting and promoting sustainable agriculture, forestry and fisheries practices through educational programs and public awareness-raising activities to reduce the destruction of wildlife habitats. Secondly, public education should be conducted in the country to reduce the demand for illegal wildlife products.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: et al. Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters. 2020;10:10134. A healthy gastrointestinal microbiome is dependent on dietary diversity. Molecular Metabolism. 2016;5:317–20. 8. Host genetic and environmental effects on mouse intestinal microbiota. . 2012;6:2033–44. 3. Barcoded pyrosequencing-based metagenomic analysis of the faecal microbiome of three purebred pig lines after cohabitation. Biotechnol. 2015;99:5647–56. CAS PubMed 33. PubMed 3412. PubMed Pub6. J, Panagiotou G, et al. Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice. Acad Sci. 2016;113:E1306–15. CAS PubMed 94. CAS PubMed 7. PubMed Pub19. 1. J Anim Sci. 2018;96:1405–18. PubMed Pub8. Host-gut microbiota metabolic interactions. Science. 2012;336:1262–7. CAS PubMed 31. CAS PubMed 15. CAS 63. 43. PubMed Pub4. Team, R. Core. R: a language and environment for statistical computing. 2017. . Identification of errors and factors associated with errors in data from electronic swine feeders1. J Anim Sci. 2005;83:969–82. CAS PubMed 9. CAS PubMed 2002. 9. 4. 2011. . edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40. CAS PubMed 300. 60. 80. 1217. 27. 65. 60. PubMed Pub1. Descriptive statistics of feed efficiency and fatness traits of pigs. Additional file 2. Relative abundance of amplicon sequence variant (ASV) at genus level in the gut microbiome of pigs. Additional file 3. Spearman correlations between gut microbiome, feed efficiency, and fatness traits of pigs. Additional file 4: S1. Differences between the gut microbiome of three breeds of pigs. Additional file 5: S2. Results of ASV effect on feed efficiency and fatness traits of three purebred lines across three time points of the growth trial. Additional file 6: S3.

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: 2009;364: 1647–1658. pmid:19451116 View 5. . Megafaunal impacts on structure and function of ocean ecosystems. Environ Resour. 2016; 41: 83–116. View 6. . Functional roles and ecological importance of small cetaceans in aquatic ecosystems. Front. Mar. Sci. 2022; View 7. Read AJ. The looming crisis: Interactions between marine mammals and fisheries. J Mammal. 2008;89: 541–548. View 8. - Drivers and hotspots of extinction risk in marine mammals. . 2012;109: 3395−3400. pmid:22308490 View 9. Nelms S, Alfaro- Marine mammal conservation: Over the horizon. Res. 2021;44:291–325. View 10. . Impacts of fisheries bycatch on marine turtle populations worldwide: toward conservation and research priorities. Ecosphere. 2013;4: 1–49. View 11. Threats to seabirds: A global assessment. Biol Conserv. 2019;237: 525–537. View 12. Overfishing drives over one third of all sharks and rays toward a global extinction crisis. Curr Biol. 2021;31: 4773–4787. pmid:34492229 View 13. First human-caused extinction of a cetacean species? Biol Lett. 2007;3: 537–540. pmid:17686754 View 14. . Current global risks to marine mammals: taking stock of the threats. Biol Conserv. 2018;221: 44−58. View 15. . Review of cetacean interactions with longline gear. J Manag. 2006;8: 215–223. View 16. . No free lunch: Estimating the biomass and ex-vessel value of target catch lost to depredation by odontocetes in the Hawaii longline tuna fishery. Sci. 2023; View 17. . By-catch: problems and solutions. Bull. 2000;41: 204–19. View 18. . Demographic consequences of fisheries interaction within a killer whale (Orcinus orca) population. Mar Biol. 2017;164:170. View 19. é C, Gourguet S, et al.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: In addition, the use of modern technological tools, such as artificial intelligence and big data, can significantly improve the efficiency of monitoring and law enforcement. At the same time, governments should take economic measures to curb the profit margins of the illegal wildlife trade, for example, by formulating policies or coordinating the behavior of traders and factories to reduce the environmental damage caused by economic activities and the impact on wildlife habitats. Finally, strengthening transnational cooperation is crucial. International exchange of environmental technology and management experience should be promoted to open markets and encourage legal and sustainable trade in wildlife products. Ensuring consistency and enforcement of laws and policies across different countries is key to working together to combat illegal wildlife trade. Through these comprehensive strategies, the challenges posed by illegal wildlife trade can be addressed more effectively. Although this study provides valuable insights, there are still many issues in the field of research on illegal wildlife trade that deserve further exploration. Future research could focus on assessing the effectiveness of different interventions and exploring how to effectively integrate transnational resources to jointly address this global problem. In addition, further research should consider the impact of geopolitics, level of economic development and cultural differences on illegal wildlife trade. Governments should provide alternative employment opportunities in areas prone to illegal wildlife trade and enhance education and skills training to help local residents find sustainable livelihoods. In addition, more research should be done on illegal wildlife trade networks, especially those complex networks involving transnational criminal organizations. Understanding the modus operandi and financial flows of these networks can help relevant agencies develop more effective strategies to combat them. Meanwhile, taking into account technological developments, future research should also assess the role of digital platforms and social media in the illegal trade and its regulatory challenges. Furthermore, research should focus more on the perspectives of affected communities to understand the specific challenges and needs they face. Through fieldwork and in-depth understanding of the economic and ecological conditions of local populations, more precise and effective conservation strategies can be developed, while ensuring that these strategies are supported and engaged by local communities, thereby increasing the success and durability of policies. Through such a multi-dimensional and multi-strategy research approach, the challenges posed by the illegal wildlife trade can be addressed in a more comprehensive manner. In conclusion, this study provides an in-depth analysis of the many challenges facing the global wildlife conservation cause, not only revealing the seriousness of illegal wildlife trade, but also highlighting the complex difficulties encountered in the actual implementation of conservation policies.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Selection for environmental variance shifted the gut microbiome composition driving animal resilience access Published: 04 July 2023 Selection for environmental variance shifted the gut microbiome composition driving animal resilience Cristina Casto-Rebollo1, María José Argente2, María Luz García2, Pena3, Agustín Blasco1 & …Noelia Ibáñez-Escriche1 Show authors Microbiome 0. 15 g of cecum samples using the DNeasy Power(. , Hilden, Germany). DNA concentration and purity were estimated by measuring the 260/280 ratio with a Nanodrop ND-1000 and verifying by a Qubit™ 4 Fluorometer (). Whole bacterial genomes were sequenced at the and (Valencia, Spain) by Illumina NextSeq 500 in 150-bp paired-end reads. Average coverage was set to 4,000,000 million paired-end reads per sample with a minimum of 2,000,000 paired-end reads. The shotgun library was made by the (Illumina Inc. , ). Quality control of raw FASTQ files was done on FASTQ v0. 11. 06 software [19], and two raw FASTQ files were discarded from the analysis to low sequencing quality. Before analysing the whole metagenome data, the raw FASTQ files were preprocessed. The host genome (Oryctolagus cuniculus genome v. 2. 0. 101) was removed by a pipeline that included the Bowtie2 v4. 1. 2 [20], SAMtools v1. 2. 1 [21], and BEDTools v2. 29. 0 software [22]. The full pipeline is available in Additional file 1. Illumina adapter removal and quality trimming of reads were performed on Trimmomatic v0. 39 software [23] using “leading” and “trailing” settings of 8 bases with a minimum length of 96, a sliding window of 10, and a minimum quality score of Q15 (see Additional file 2). The cleaned FASTQ files were analysed with the “default” settings of the “seqmerge” mode of SqueezeMeta v1. 3. 1 software [24] (see Additional file 3).

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: When large marine predators feed on fisheries catches: global patterns of the depredation conflict and directions for coexistence. Fish Fish. 2021;22: 31–53. View 20. . Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions. Sci. 2012;28: E345–E374. View 21. . Mitigating bycatch and depredation of marine mammals in longline fisheries. . 2015;72: 1576−1586. View 22. . The challenges of managing depredation and bycatch of toothed whales in pelagic longline fisheries: Two U. S. case studies. Sci. 2021; View 23. NMFS. Taking of Incidental to Operations;. Fed Regist. 2012; 77: 71259. 24. FAO. Report of the on Means and Methods for in Fishing and Aquaculture Operations. FAO Fisheries and No. 1231. 2018; Food and of the United Nations, Rome. 25. Hamilton S, Baker GB. Technical mitigation to reduce marine mammal bycatch and entanglement in commercial fishing gear: lessons learnt and future directions. Biol Fish. 2019;29: 223−247. View 26. . What’s the catch? Patterns of cetacean bycatch and depredation in Hawaii-based pelagic longline fisheries. Mar Ecol. 2011;32: 380–391. View 27. A dynamic ocean management tool to reduce by-catch and support sustainable fisheries. 2018; pmid:29854945 View 28. G. ,. Acoustic detection of bottlenose dolphin depredation on nets and implications for conservation. Aquat Conserv. 2023;33: 179–190. View 29. . Individual and fleetwide bycatch thresholds in regional fisheries management frameworks. Biol Fish. 2023;34: 253–270. View 30. újica L, Reyes P. Significant conservation benefits obtained from the use of a new fishing gear in the toothfish fishery. . 2008;15: 79–91. View 31. . Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales. J Biol Ecol. 2012;432–433: 55–63. View 32. Garrison LP.

Output: pass


Input: Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: Through careful data collection and analysis, this study provides solid data support and a scientific analytical framework for the formulation of more effective conservation policies, and injects new vitality and hope into the cause of wildlife conservation. This study highlights that by continuously optimizing interventions, we can more precisely combat illegal wildlife trade and curb this serious threat to biodiversity. In addition, enhanced international cooperation has proven to be the key to addressing this global challenge. It is only through coordination and cooperation among countries that we can create a strong synergy to fight the illegal wildlife trade. Looking ahead, we firmly believe that through the joint efforts of the global community, we can gradually realize the goal of reducing illegal wildlife trade. We look forward to a world where all kinds of life are respected and protected globally, ensuring the safety and well-being of every living being on the planet. Through these efforts, we protect not only wildlife but also our shared natural heritage. Declarations0391. doi: 10. 1016/j. ese. 2024. 100391. PMID: 38328510; PMCID: PMC10847997. Symes W. S. , McGrath F. L. , Rao M. , Carrasco L. R. The gravity of wildlife trade. Biol. Conserv. 2018;218:268–276. doi: 10. 1016/j. biocon. 2017. 11. 007. Anagnostou M, Doberstein B. Illegal wildlife trade and other organised crime: A scoping review. Ambio. 2022 Jul;51(7):1615-1631. doi: 10. 1007/s13280-021-01675-y. Epub 2021 Dec 1. PMID: 34851513; PMCID: PMC9110579. . Economic value of illegal wildlife trade entering the USA. PLoS One. 2021 Oct 12;16(10):e0258523. doi: 10. 1371/journal. pone. 0258523. PMID: 34637471; PMCID: PMC8510001. Kawata, Ken. (2014). Journey from Wilderness: of Exhibits. Garten. 83. 10. 1016/j. zoolgart. 2014. 06. 001. . The role of meat appetite in willfully disregarding factory farming as a pandemic catalyst risk. Appetite.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The alpha- and beta-diversity were computed using the ALR at the species level to measure the differences in microbiota composition among the rabbit populations. The alpha-diversity was measured by Shannon’s (H′) and inverse Simpson indexes. The same indexes analysed the species diversity and evenness in the samples. Differences in the distribution of alpha-diversity among rabbit populations were considered when the p-value of a Mann–Whitney U-test was lower than 0. 05. Beta-diversity was measured by the Bray–Curtis dissimilarity matrix, and a nonmetric multidimensional scaling (NMDS) was carried out to retrieve the loadings of the first two dimensions. Differences in microbial species composition were tested by the permutational multivariate analysis of variance (; p-value < 0. 05) on the loadings of the two first MDS dimensions. A full record of the alpha- and beta-diversity calculation is included in Additional file 8. Results and discussionAfter the additive log-ratio (ALR) transformation, the partial least square-discriminant analysis (PLS-DA) identified 361 relevant variables, including the following: 116 164 COG IDs, 6 phyla, 15 families, 28 genera, and 32 species. Most models achieved a high classification performance of the rabbit populations in terms of resilience potential, given that rabbits with high V E are considered less resilient than those with low V E (Table 1) (see Additional files 4, 5 and 6). The best models were those using the KEGG and COG IDs for functional assignment and the species level for taxonomic assignment (Table 1). The models using counts from functional assignment allowed higher discrimination than those from the taxonomic assignment (Table 1). The taxonomic ranks were inferred from the functional assignment [24], having a lower statistical power for discriminating between the two rabbit populations (Fig. 1) due to the loss of information in the assignment. Table 1 PLS-DA model specifications using counts from genes and taxa of the resilient and non-resilient rabbit populationsFull size tableFig. 1Gut microbiome composition dissimilarity.

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: Interactions between marine mammals and pelagic longline fishing gear in the U. S. between 1992 and 2004. Fish Bull. 2007;105: 408–417. View 33. . (eds. ). Report of the Workshop on Cetaceans and Longline Fisheries. . 03–1. 2002; Aquarium Press, Boston. View 34. Doksæter L, Godø. , Donovan C, et al. Behavioral responses of herring (Clupea harengus) to 1–2 and 6–7 kHz sonar signals and killer whale feeding sounds. J Am. 2009;125: 554–564. pmid:19173441 View 35. . False killer whale (Pseudorca crassidens) echolocation and acoustic disruption: implications for longline bycatch and depredation. Zool. 2009;87: 726–733. View 36. McPherson G, Nishida T. of Efforts in the Indo-Pacific Region. 2010; Secretariat of the Pacific Community, Noumea, New Caledonia. 37. O’ Testing a passive deterrent on longline to reduce sperm whale depredation in the Gulf of Alaska. . 2015;72: 1667–1672. View 38. Effect of pelagic longline bait type on species selectivity: A global synthesis of evidence. Biol Fish. 2020;30: 535–551. View 39. Bayse SM, Kerstetter D. Assessing bycatch reduction potential of variable strength hooks for pilot whales in a western north Atlantic pelagic longline fishery. J N C Acad Sci. 2010;126: 6–14. View 40. . Catch rates with variable strength circle hooks in the Hawaii-based tuna longline fishery. Sci. 2012;88: 425–447. View 41. Longline hook testing in the mouths of pelagic odontocetes. . 2015;72: 1706–1713. View 42. . Results of the 1986–87 California sea lion-steelhead trout predation control program at the Hiram M. Chittenden Locks. 88–30. 1988;. View 43. and Non-of Marine Mammals: Report of the Technical Workshop, 10–13, September 2007. -OPR-39. 2008; Fisheries Service, Seattle. View 44.

Output: pass


Input: Claim: Rewilding projects aim to restore natural habitats for native species. [SEP] Text: $0. 00 -for: Search $0. 00 -for Habitats it comes to conserving endangered species habitats, you need to prioritize preserving natural habitat corridors that enable species migration and genetic exchange. Evaluating ecological integrity, considering factors like land use, climate, and topography, is vital. Habitat restoration techniques, like biological and physical methods, can facilitate ecological succession and improve soil quality. Species-specific conservation plans must address unique challenges, and community-based initiatives can foster a sense of ownership. As you explore the complexities of habitat conservation, you’ll uncover more nuanced strategies to protect vulnerable species and their habitats, and discover the intricate connections that govern their survival. Table of Contents hide --Plans Community--and Share this Habitat CorridorsWhen developing conservation strategies, preserving natural habitat corridors is crucial, as these connectors enable species migration, genetic exchange, and adaptation to environmental changes. You’ll find that corridors play a critical role in maintaining biodiversity, especially for species with large home ranges or those that require specific habitats. By preserving corridors, you’re allowing species to move freely, find food, and mate, which is necessary for their survival. As you work to preserve corridors, you’ll need to evaluate areas with high conservation value, such as riparian zones, mountain passes, or areas with unique geological features. Assessing the landscape’s ecological integrity is paramount, taking into account factors like land use, climate, and topography. You should also assess the corridors’ connectivity, ensuring that they’re wide enough to facilitate species movement and provide adequate habitat quality. Sustainable Land-Use PlanningAs you shift your focus from preserving natural habitat corridors to sustainable land-use planning, you’ll need to ponder the broader landscape context, integrating conservation goals with human activities to guarantee that development and growth occur in harmony with the environment. This requires balancing human needs with environmental concerns, ensuring that land-use decisions prioritize biodiversity conservation. You’ll need to identify areas of high conservation value and incorporate them into spatial planning, taking into account factors like ecosystem services, climate change, and socio-economic pressures. By adopting a landscape-scale approach, you can optimize land-use allocation, minimizing habitat fragmentation and promoting ecological connectivity. This might involve designating protected areas, establishing green infrastructure, or implementing agro-ecological practices.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Representation of the first (Comp 1) and second components (Comp 2) of the final partial least square-discriminant analysis (PLS-DA) models (Table 1) and alpha- and beta-diversity scores from the resilient (red) and non-resilient (blue) rabbit populations. PLS-DA plotting was performed using three different datasets: a phyla abundances, b species abundances, and c abundances. The alpha- and beta-diversity scores were calculated with the additive log ratio of each species abundance according to a reference species (Clostridium sp. ). Alpha-diversity was computed using d Shannon’s H index and e inverse Simpson index. Beta-diversity was computed by calculating f the Bray–Curtis dissimilarity matrix. Differences among populations were established with a p-value lower than 0. 05Full size imageLikewise, the higher (Fig. 1A) had less discrimination power than the lower taxonomic ranks (Fig. 1B). Clustering counts in the higher taxonomic ranks (phylum or family) could hide their variation between the populations due to grouping bacteria with dissimilarity in their functions. The results show that a few species (32) were relevant for the classification among the two rabbit populations, obtaining an accuracy of 0. 87 and a precision of 0. 89 (Table 1). These results were supported by the alpha- and beta-diversity scores, which did not differ between the two rabbit populations (Fig. 1 D–F), indicating that in general, both populations have a similar microbiota composition except for a few species identified by the PLS-DA. statistical analysis (see Additional file 7) showed that 303 variables (including both genes and taxa) from the initial 361 identified by PLS-DA analysis (Table 1) had a posterior mean of the differences among the rabbit populations of at least 0. 5 of the SD of the variable (see Additional file 9) in which the probability of differences being higher or lower than 0 (P0) was higher than 0. 97. results showed that most of the variables included in the PLS-DA models (Table 1) are key variables for discriminating between rabbit populations, with relevant differences in mean abundance (see Additional file 9).

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: of Whales (Pseudorca crassidens) under the Act. 2010;. View 45. . Patterns of depredation in the Hawaii deep-set longline fishery informed by fishery and false killer whale behavior. Ecosphere 2021; do: View 46. NMFS. . Version LM. 17. 02. 2017;. 47. . Comparison of Bayes-Laplace, Jeffreys, and other priors: The case of zero events. Am Stat. 2008;62: 40–44. View 48. Lin L, Chu H. Meta-analysis of proportions using generalized linear mixed models. Epidemiology. 2020;31: 713–717. pmid:32657954 View 49. Phylogeny explains capture mortality of sharks and rays in pelagic longline fisheries: a global meta‐analytic synthesis. Sci Rep. 2022; pmid:36307432 View 50. Dorai-Raj S. binom: Binomial confidence intervals for several parameterizations. R package version 1. 1–1. 1. 2022; 51. A of Fisheries Discards. FAO Fisheries and Paper 633. ISBN 978-92-5-131226-1. 2019; Food and of the United Nations, Rome. 52. Puth M, Neuhäuser M, Ruxton G. Effective use of Pearson’s product–moment correlation coefficient. Anim Behav. 2014;93: 183–189. View 53. Wei T, V. R package ’corrplot’: Visualization of a correlation Mmtrix (Version 0. 92). 2021; View 54. Agresti A. Categorical data analysis (second edition). 2002; Wiley: New York. 55. Aragon T. epitools: Epidemiology Tools. R package version 0. 5–10. 1. 2020; 56. Saulitis E, Matkin C, Barrett-. Foraging strategies of sympatric killer whale (Ocrinus orca) populations in Sound, Alaska. Sci. 2000;16: 94–109. View 57. Hart P. Foraging in teleost fishes. 58. Krivan V. Optimal foraging and predator-prey dynamics. Biol. 1996;49: 265–290.

Output: pass


Input: Claim: Rewilding projects aim to restore natural habitats for native species. [SEP] Text: ConservationBy preserving corridors of natural habitat, you can maintain ecological connectivity, allowing species to migrate, disperse, and adapt to environmental changes, thereby maintaining the resilience of ecosystems. This approach is vital for conserving biodiversity, as fragmented habitats can lead to population isolation, reduced gene flow, and increased extinction risk. By connecting isolated patches of habitat, you can facilitate the exchange of individuals, genes, and nutrients, thereby maintaining the health and viability of species populations. Ecological connectivity conservation involves identifying and protecting key habitat corridors, such as wildlife corridors, green bridges, and riparian zones. You can also restore degraded habitats, remove barriers to species movement, and implement conservation-friendly land-use planning. Effective ecological connectivity conservation requires collaboration among stakeholders, including researchers, policymakers, and land managers. Human-ResolutionAs human settlements and agriculture continue to encroach on natural habitats, resolving human-wildlife conflicts becomes essential for preserving biodiversity and promoting coexistence. You’ll need to address the root causes of these conflicts, which often stem from competition for resources like food, water, and space. One effective approach is to implement spatial planning strategies, such as zoning and land-use planning, to minimize overlap between human and wildlife habitats. You can also establish community-based initiatives that provide compensation for crop damage or livestock losses, encouraging local communities to tolerate wildlife presence. Additionally, using non-lethal deterrents, like fencing and repellents, can help mitigate conflicts. It’s vital to engage with local stakeholders, including farmers and herders, to develop context-specific solutions that balance human needs with conservation goals. Network ExpansionYou can expand the protected area network by identifying and acquiring critical habitats, corridors, and ecosystems that connect fragmented landscapes, thereby maintaining ecological integrity and promoting species migration. This strategy is vital for preserving biodiversity, as it allows species to move freely and adapt to environmental changes. By expanding protected areas, you can also reduce habitat fragmentation, which is a significant threat to many endangered species. When identifying areas for expansion, prioritize habitats with high conservation value, such as old-growth forests, wetlands, or coral reefs. These ecosystems often support a wide range of species and provide essential ecosystem services. Additionally, consider the connectivity of habitats, as corridors can facilitate species migration and genetic exchange. Engaging with local communities, indigenous peoples, and other stakeholders in the expansion process is vital to address their concerns and rights.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: These differences must have arisen because of the divergent selection for VE of LS, since the animals were coetaneous and kept under the same environmental conditions (diet, management, temperature, etc. ). Relevant results in the PLS-DA models using the taxonomic ranks are detailed below. The species Alistipes shahii (0. 60 unit of SD), Alistipes putredinis (0. 51), Odoribacter splanchnicus (0. 58), and Limosilactobacillus fermentum (0. 57) were more abundant in the resilient animals (Fig. 2), as were the higher taxonomic ranks of these species (Fig. 2): genera Odoribacter (0. 83), Alistipes (0. 75), Lactobacillus (0. 56), and Rikenella (0. 51); families Odoribacteraceae (0. 84) and Rikenellaceae (0. 74); and the phylum Bacteroidetes (0. 59). Health-beneficial properties have been reported from these taxa, in part due to their effects on the inflammatory and immune-adaptive response [33,34,35]. These effects on the immune system have been suggested to be mediated by short-chain fatty acids (SCFs) and Th17 cells. SCFs have anti-inflammatory properties [12, 36], and differentiation of Th17 cells is essential for the host to develop a correct tolerance to foreign and nonpathogenic commensal species, playing an important role in gut immunity [37]. Fig. 2Principal component analysis of gut microbiome composition. Representation of first (PC1) and second principal component (PC2) of the additive log-ratio transformation of the relevant variables for distinguishing between resilient and non-resilient populations. SD indicates the unit of standard deviations of each variable from the posterior mean of the differences between the marginal posterior distributions of the rabbit populations. The SD colour gradient highlights the degree of difference, blue and red being the greatest differences among the rabbit populations. The blue-shaded area indicates that higher microbiome abundance in the non-resilient population. The red-shaded area indicates higher microbiome abundance in the resilient population. SCP, proteins involved in signalling and cellular processing; ABC transp. , proteins of the family ABC transporter; quorum s. , proteins involved in quorum sensing.

Output: contrast


Input: Claim: Bycatch reduction devices are designed to minimize the unintended capture of marine species. [SEP] Text: pmid:8813025 View 59. Ward P. Empirical estimates of historical variations in the catchability and fishing power of pelagic longline fishing gear. Biol Fish. 2008;18: 409–426. View 60. . Effect of bait species and color on sea turtle bycatch in a pelagic longline fishery. Fish Res. 2009;97: 53–58. View 61. -Palm A, Ferter K. Toward more efficient longline fisheries: Fish feeding behavious, bait characteristics and development of alternative baits. Biol Fish. 2014;24:985–1003. View 62. . Effects of chemicals from longline baits on the biting behaviour of loggerhead sea turtles. Mar Sci. 2012;34: 1–5. View 63. False killer whales (Pseudorca crassidens) around the main Hawaiian Islands: Long-term site fidelity, inter-island movements, and association patterns. Sci. 2008;24: 591–612. View 64. Riccialdelli L, Goodall N. Intra-specific trophic variation in false killer whales (Pseudorca crassidens) from the southwestern Ocean through stable isotopes analysis. Mamm Biol. 2015;80: 298–302. View 65. . Killer whale depredation and associated costs to Alaskan sablefish, Pacific halibut and Greenland turbot longliners. . 2014; pmid:24558446 View 66. NMFS. (Pseudorca crassidens) Segment 5-Year Review: Summary and Evaluation. 2022;. 67. U. S. Assessments: 2022. -TM-NMFS-SWFSC-684. 2023;. View 68. Slooten E. Effectiveness of area-based management in reducing bycatch of the Dolphin. Res. 2013;20: 121–130. View 69. FAO. Report of the on Area-Measures in the Fishery Sector. FAO Fisheries and No. 1301. 2019; Food and of the United Nations, Rome. 70. Postrelease survival, vertical and horizontal movements, and thermal habitats of five species of pelagic sharks in the central Pacific Ocean. Fish Bull. 2011;109: 341–361. View 71. Foote KG.

Output: pass


Input: Claim: Lithium-ion batteries are commonly used in electric vehicles and consumer electronics. [SEP] Text: (2020). Optimal energy management of plug-in hybrid electric vehicles concerning the entire lifespan of lithium-ion batteries. Energies, 13(10), 2543; doi: 10. 3390/en13102543 Chol-Ho, K. , Moon-Young, K. , & Gun-Woo, M. (2013). A modularized charge equalizer using a battery monitoring IC for series-connected Li-Ion battery strings in electric vehicles. IEEE Trans. Power Electron, 28(8), 3779–3787; doi: 10. 1109/tpel. 2012. 2227810 Collin, R. , Miao, Y. , Yokochi, A. , Enjeti, P. , & Von Jouanne, A. (2019). Vehicle Fast-Charging Technologies. Energies, 12(10), 1839; doi: 10. 3390/en12101839 Daowd, M. , Omar, N. , Van den Bossche, P. , & Van Mierlo, J. (2011). A Review of Passive and Balancing based onSimulink. Int. Rev. Electr Eng, 6(7). 2974-2989 Dong, B. , Li, Y. , &Han. Y. (2015). for Equalization. IEEE Trans. Power Electron, 30(9), 4906-4913; doi: 10. 1109/tpel. 2014. 2364838 Deng, Y. , Li, J. , Li, T. , Zhang, J. , Yang, F. , & Yuan, C. (2017). Life cycle assessment of high-capacity molybdenum disulfide lithium-ion battery for electric vehicles. Energy, 123, 77-88. doi: 10. 1016/j. energy. 2017. 01. 096 Deng, Y. , Ma, L. , Li, T. , Li, J. , & Yuan, C. (2019). Assessment of Silicon-Nanotube-Based Lithium-for Electric Vehicles. ACS and Engineering, 7 (1), 599-610; doi: 10. 1021/acssuschemeng. 8b04136 Gallardo-Lozano, J. , Romero-Cadaval, E. , Milanes-Montero, M.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: and PilT proteins that conform the pilusFull size imageHarmful microbial species such as Acetatifactor muris (− 0. 72 of SD unit) and Eggerthella sp. (− 0. 63) were more abundant in the non-resilient rabbits (Fig. 2), which was consistent with their associations with autoimmunity and inflammatory diseases [38, 39]. Species like Megasphaera sp. (− 0. 75), Bacteroides rodentium (− 0. 70), Ruminococcus bromii (− 0. 67), and Bacteroides togonis (− 0. 63) also showed higher abundance in the non-resilient population (Fig. 2). A gut metabolomic study suggested differences in the feed efficiency between these rabbit populations [40] which could explain the differences in the abundance of Ruminococcus bromii. Ruminococcus bromii is a keystone in breaking down starch, allowing other gut microbiota species to cross-feed [41]. We found no evidence in the literature of possible negative effects of Megasphaera sp. , Bacteroides rodentium, Ruminococcus bromii, and Bacteroides togonis on host health, but the effect of microbial species on individual health remains unclear. There are discrepancies in the literature on the gut microbiota composition related to health and disease. For instance, species such as Alistipes putredinis have been identified as both beneficial and harmful species [33, 38], as have the genus Sutterella (0. 83) and the family Sutterellaceae (0. 60), with a higher abundance in resilient rabbits (Fig. 2). genus was identified in healthy and inflammatory bowel disease patients [42], particularly in those with ulcerative colitis [43]. The studies could only suggest the participation of the Sutterella genus in the modulation of the inflammatory response [43] through the alteration of IgA levels, an important immunoglobulin to neutralize pathogens and prevent infections [44]. All relevant species for the classification between the resilient and non-resilient animals can be found in the Additional file 9. Due to discrepancies in the effects of some of the identified microbial species on health and disease, in-depth research is needed to establish their true impact on animal resilience.

Output: contrast


Input: Claim: Community-based fisheries management has proven effective in sustaining local fish populations. [SEP] Text: AbstractAccurate and low-impact monitoring of scallop abundance is critical for stock assessment, especially in sensitive habitats. The possibility of using low-impact hyperspectral imaging (HSI) for differentiating scallop species in the marine environment was investigated. Live saucer (Ylistrum balloti) and mud (Ylistrum pleuronectes) scallops (N = 31) were scanned inside a sea simulator using a visible to near infrared (400–1000 nm) line-scanner HSI camera. Partial least square discriminant analysis (PLS-DA) was trained to distinguish between the species using their spectral signatures. Important wavelengths were identified and new models were developed using these wavelengths to reduce the model complexity and potentially increase the imaging speed when applied under at-sea conditions. The PLS-DA model distinguished between saucer and mud scallops using any area of the left valve that was exposed above the sediments, with 90. 73% accuracy when all 462 available wavelengths were used. Using the subset of important wavelengths (N = 13) reduced the classification accuracy to 84%. Overall, our results showed that HSI has potential for detecting, distinguishing and counting commercially important saucer scallops for low-impact monitoring and resource management, and to complement RGB imaging that relies solely on morphological properties. Graphical abstract Use our pre-submission checklist Avoid common mistakes on your manuscript. IntroductionScallops are economically valuable marine bivalves with approximately 400 known species, inhabiting coastal marine environments worldwide (Brand 2016; Delargy et al. 2023; Stewart and Howarth 2016). The combined factors of rising demand for wild-captured scallops and the impacts of climate change have added pressure on scallop stocks (Cheung et al. 2013; FAO 2020; Zang et al. 2023). Due to their commercial significance and ecological importance, accurate assessment and monitoring of scallop populations are crucial for their sustainable management. Trawling and dredging are the most used methods for surveying scallop populations. However, these methods can impose habitat impacts and incidental mortality on bycatch species (Delargy et al. 2023; Freese et al. 1999; Kaiser and Spencer 1996; Probert et al. 1997; Thrush and Dayton 2002). Visual survey alternatives, such as towed camera systems, remotely operated vehicles and autonomous underwater vehicles (Courtney et al.

Output: pass


Input: Claim: Lithium-ion batteries are commonly used in electric vehicles and consumer electronics. [SEP] Text: , Zhao, W. , Ziebert, C. , Uhlmann, N. , Rohde, M. ,& Seifert, H. (2017). of in 18650 Cylindrical Li-Using an Calorimeter. Batteries,3(4), 14; doi: 10. 3390/batteries3020014 Lipu, M. S. H. , Hannan, M. A. , Hussain, A. ,. , & Ayob, A. (2018). A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. J. Clean. Prod, 205, 115-133; doi: 10. 1016/j. jclepro. 2018. 09. 065 Li, W. , Chen, S. , Peng, X. , Xiao, M. , Gao, L. , Garg, A. ,& Bao, N. (2019). A for the Clustering of Similar-for the Design of a Lithium-Module for Electric Vehicles. Engineering, 5(4), 795-802; doi: 10. 1016/j. eng. 2019. 07. 005 Ma, Y. , Duan, P. , Sun, Y. , & Chen, H. (2018). Equalization of Lithium-on Control in Electric Vehicle. IEEE Trans. Ind. Electron, 65 (8), 6762-6771; doi: 10. 1109/tie. 2018. 2795578 Ma, L. , Nie, M. , Xia, J. , & Dahn, J. R. (2016). A systematic study on the reactivity of different grades of charged Li [NixMnyCoz]O2 with electrolyte at elevated temperatures using accelerating rate calorimetry. J. Power Sources, 327,145–150; doi: 10. 1016/j. jpowsour. 2016. 07. 039 Moghaddam, A. F. , & Bossche, A. (2018). Technique for on Inductor Balancing. 9th on Mechanical and (ICMAE), 274-278; doi: 10. 1109/icmae. 2018. 8467685 Moghaddam, A. F. , & Bossche, A. (2019).

Output: corroborate