Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: Уже в раннем девоне наземная растительность состояла из растений разного систематического положения (псилофиты или риниофиты, печёночники, прапапоротники, плауновидные, членистостебельные, а также целый ряд растений, таксономическое положение которых не ясно). Возможность широкой эволюционной радиации5 первых наземных высших растений в условиях суши уже неоднократно отмечалась ранее [26].

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: Due to the variability in lifespan of gases in the atmosphere and the ability of gases to reflect and trap radiant energy, the average potential of a GHG to warm the earth‘s near-surface air is expressed in carbon dioxide equivalents (CO2-eq. ) emissions (its global warming potential). Methane and nitrous oxide are capable of trapping about 25 and 298 times more radiant energy respectively, over a 100-year time horizon, than one kilogram of carbon dioxide [6]. The dairy sector’s total CO2-eq. emissions are estimated to be 4% of total global GHG emissions, of which, about half are methane and a third nitrous oxide emissions [2]. The main benefits of selection to improve production efficiencies are by increased productivity and gross efficiency (i. e. the ratio of yield of milk to resource input) by firstly, diluting the maintenance cost of animals in the system and secondly, less animals are required to produce the same amount of product [1, 7]. Studies have found [8, 9] that more energy efficient animals produce less waste in the form of methane and nitrogen excretion per unit product. A study in the UK [4] calculated that the genetic improvement in dairy cows by economic and production efficiency in the last 20 years had reduced GHG emissions per unit product by 0. 8% per year and would continue to reduce emissions at a rate of 0. 5% per year over the next 15 years. A reduction of 0. 6% per year in GHG emissions per unit product was found in the US [1] over a 63 year period. Emissions of methane and nitrous oxide per unit product were estimated to have shown large declines of about 1. 3% and 1. 5% per year respectively over the last 20 years in the UK, and will continue to decline over the next 15 years albeit at a slightly slower rate per year [4]. These rates of decline for methane are similar to those reported in other studies [10] for enteric methane emissions per unit product of 1. 1% per year for cows selected on increased milk fat and protein production (Select line cows) and at 1. 4% per year for cows selected to represent the UK average for milk fat and protein production over a similar time period. In this review, we investigate the potential role of selective breeding in reducing GHG emissions. 1. 1. Selecting.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: The 2–3 kg size of hares required maximum camera sensitivity, also capturing blowing vegetation and ‘blank’ images, requiring more filtering time. However, operating 24/7, cameras appeared to avoid false negative detections. Image times conveyed peak nocturnal activity periods, even during extremely cold nights. There were two night-time peak activity phases, consistent with records for Irish hare Lepus timidus hibernicus Caravaggi et al. (2018). The narrow field of view captured no more than 2 hares at a time, perhaps under recording larger groups, as observed by the thermal imager. Camera traps require financial outlay, bear theft risk and need considerable field effort. Image review time is substantial yet can be reduced using image recognition software (Schneider et al. 2020). Figure 8. The regressed number of encounters (line) per camera (point shapes) is seen to decrease over the study period, end Nov 2017 to March 2018, taking into account ‘Site’ as a random effect. In our study, camera trap detections occurred at short ranges, so the detection probability histogram allocated 3506 encounters to just four distance bins, producing low variation of detection probability (cv = 0. 03). Camera trap density estimates showed less variation than the thermal imager. Our findings suggested high levels of precision could be achieved with half the camera installations as we had used, with field time of ∼ 20 days. Monitoring surveys are expected to fulfil the principal assumptions of distance sampling. However, for the camera trap analysis we noted certain factors can have a large effect on density estimates (Fig. 9). Figure 9. Analysis of alternate scenarios providing camera trap density estimates. The x-axis shows different data treatments or assumptions. The y-axis shows consequent density of hares km–2. Columns are density values with 95% confidence interval error bars. To maintain consistency amidst the comparisons, all scenarios used the same data filter with detection distances binned at 1, 2, 4 and to 5 m with the Hazard-rate model with simple polynomial, which in all cases achieved lowest AIC scores of candidate models. ‘Base scenario' was the scenario eventually chosen for our camera trap estimate for comparison with daylight visual sampling and thermal imager. This assumes correct measurements (metres) of distance to hare; k snapshot window = 1 second; diel period is sunset-to-sunrise (Fig.

Output: contrast


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: Таксономически богатые раннедевонские флоры известны из бассейна Днестра (Волыно-Подолия) [25], Восточной Сибири (Саяно-Алтайский регион) [27, табл. II, 4–6], Германии (район Рейнских Сланцевых гор) [28], Англии (Уэльс) [20], Шотландии, Центрального Казахстана, Южного Китая и некоторых других регионов. Высшие растения раннего девона образовывали устойчивые симбиотические связи с эндомикоризными грибами [29].

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: animals for reduced emissions Level of feed intake and its composition are important factors influencing methane and nitrogen losses. As the feed intake of an animal increases, the percentage of dietary gross energy (GE) intake lost as methane decreases by an average of 1. 6% per unit of intake [11]. A higher feed intake level increases its fractional passage rate through the rumen and reduces its retention time, rumen digestion (depending on the diet) and methane production [11, 12, 13]. As rumen retention time decreases with increased feed intake the rate of nitrogen excretion increases, increasing the potential for nitrous oxide emissions [9, 14]. However, if cows are to meet their genetic potential for milk production, they need to maximise their feed intake [15, 16]. At pasture the nutrient intake can vary and impair the milk production potential of the animal, particularly during the peak of lactation [17]. To meet the genetic potential for milk production forage based diets are supplemented with high energy dense feed in the form of concentrate. Supplementing the diet of high milk yielding dairy cows at pasture with concentrate was found [17] to result in a lower rate of pasture intake substitution and a higher response in improved milk yield compared to lower milk yielding cows. Cows with a high body weight have been found to have a greater bite weight when eating and therefore are more efficient in their use of time spent feeding [16]. The larger Holstein genotype has been found to produce between 8 to 11% less methane as a percentage of GE intake, on both a total mixed ration and pasture-based diet, than a small Holstein [18], presumably due to differences in level of feed intake. However, larger cows have greater maintenance requirements. For the same level of production, a smaller cow is obviously a more efficient converter of feed into milk. This is why selection programmes in both and Australia, in particular have focused on increasing the rate of genetic gain in traits that contribute to profitability per unit of feed eaten [19]. Selecting dairy animals for efficient feed use could bring both higher production and reduced resource requirements. In comparison to other mitigation strategies, selective breeding offers a medium to long-term approach to GHG mitigation, which can be cost effective [20].

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: These findings represent important indicative baselines for local monitoring and may inform assessments of other groups of native or reintroduced mountain hares. Notwithstanding its remarkable 150 year tenure, the mountain hare densities may be considered low compared to many populations in Scotland which commonly reach 20–50 hares km–2 (Watson et al. 1973, Newey et al. 2018). ConclusionsWe report the practical survey effort, scale, encounter rates, density estimates and measures of precision which may be helpful for the planning of studies of elusive or nocturnal animals in difficult terrain. Daylight visual sampling is low cost, is logistically simple, can rapidly cover much ground and can achieve precise density estimates, yet, transpiring by day, may fail to observe cryptic nocturnal animals, thereby reporting lower encounter rates and thus underestimating abundance. For somewhat more effort, a high power thermal imager achieves potentially more observations of nocturnal animals including at long distances and consequent higher density estimate precision. It is recommended when surveying accessible areas, with dependable fog and wind free weather. By contrast camera traps can provide constant monitoring and at night over long periods in all weathers. They are thus useful for long term surveys, placed in locations which are difficult to access frequently or where it would be hazardous to venture in darkness. Camera traps can achieve large numbers of detections, including at night, recording the peak activity levels of nocturnal animals. However between the methods, daylight visual sampling and thermal imager surveys both work well in applying the principles of distance sampling. Practically speaking, camera trap distance sampling operates effectively in achieving large data sets and can adopt distance sampling principles. However the consequent models need contemplation of additional assumptions and sensitivity modelling. Where there is insufficient empirical data, inferences may require subjective analytical decisions, potentially rendering camera trap distance sampling estimations less robust. AcknowledgementsWe thank and for fieldwork permissions and J. Mann of FLIR for advice on thermal imagers. We thank G. Pettigrew, M. Kocsis, J. Minshull, K. Boyd, C. Rice, D. Hogg and A. Oliver for support with fieldwork and M. Jones, A. Reuleaux and A. Hashmi for comments which improved earlier drafts.

Output: contrast


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: Экологически эти наземные сообщества уже были глубоко диверсифицированы и образовывали сложные катениальные системы [28, Fig. 6]. Формы роста растений начала девона варьировали от полуводных травянистых до невысоких кустарников. Сформировалось устойчивое чередование гаплоидных и диплоидных поколений (гаметофитов и спорофитов) [30].

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: In ruminants, measurements of methane output, nitrogen efficiency and overall feed efficiency are difficult and costly to obtain, which has limited the direct selection of these traits in the past. A large part of the variation in methane emissions from dairy cows has been found to be genetic, with a heritability of 0. 35 for methane output and 0. 58 for methane output per unit product [28], presumably due to a prediction of methane being used and its close unity correlation with feed intake. In comparison, a lower heritability of 0. 13 has been found [29] in sheep for methane output. Once individual measurements for total animal methane emissions become more affordable to carry out for a large number of animals, selecting animals on methane output will become possible. Variation in feed use efficiency and enteric methane emissions between-animals, breeds and over time means there is potential to reduce GHG emissions through genetic selection [30, 31, 32]. With a positive genetic correlation between feed efficiency and methane output, with an estimated range from 0. 18 to 0. 84 [28], it can be inferred that selecting cows that are more efficient will reduce methane production, possibly in the order of 1. 1% to 2. 6% per year. From a range of production and fitness traits, breeding studies [4, 14] found feed efficiency to have a large impact on reducing the GHG emissions from dairy systems compared to other production or fitness traits. Feed efficiency can be assessed by feed intake required per unit product (gross efficiency) or by net or metabolic efficiency commonly calculated as residual feed intake [4]. Residual feed intake is the difference between the observed and predicted feed intake; where the predicted feed intake is often calculated as energy requirements. Studies [31, 33, 34] looking at selecting beef cattle based on a lower residual feed intake (difference between actual and expected feed intake) found that growth performance was not compromised and the lower expected feed intake resulted in less methane produced. Heritability estimates for feed efficiency tend to be moderate (0. 16 to 0. 46; [4]). Low correlations between residual feed intake and other production traits imply that little or no genetic improvement has previously been made in residual intake in beef cattle as a result of selection on production traits [4]. In dairy cows, calculating residual feed intake accurately is difficult as changes in body tissue composition need to be fully accounted for.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: It's very difficult to get from the raw data to a clean data set because there are thousands of images to look at and keep track of. We wanted to work in a system that would improve that using artificial intelligence, and that's why we partnered with Google: so the individuals working with camera trap data wouldn't have to identify all the images by themselves, but a machine would do it first and help simplify the problem and speed up the data flow. The second barrier that we wanted to improve was having a place where people could put their data and make sure that it's available, shareable, and people can collaborate. There was no such place. Even if people were successful in organizing all the images from a camera trapping project, they ended up on an external hard drive somewhere. Some might be in cloud accounts, but a lot of these data sets are either in danger of disappearing or siloed. They're not talking to each other. The only way to bring that together was in this single platform in the cloud. The third barrier is that even if you speed up the processing of data and [you] can share it, there's still a lot of barriers in understanding what the data is telling you. You need a lot of technical knowledge and training to be able to make a camera trap data set and calculate how species in that data set are changing through time or what the species richness in the sample is or basic statistics like that. That requires a lot of work that most people don't have the skills to do. So, our solution was to create an analysis module that will automate all that analysis. Q: How will this platform change the way we study animals in the wild? A: will basically give us information that we don't have access to at all. For many animals, we don't really know how their population is changing or how many animals are in the population. Those are basic facts that you need in order to manage a conserved species that we don't have for most animals on the planet. will give us that for hundreds of species. The other thing I would say is that this data will help reveal a lot of the inner lives of animals. For example, how do animals use their time during the day? Are they moving, eating, sleeping? That helps us understand changes in the activity patterns of animals that usually happen when there's some change in the environment, either because of climate change or because of people hunting them.

Output: contrast


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: ИСТОЧНИК ФИНАНСИРОВАНИЯРабота выполнена в рамках Госзадания ГИН РАН № 0135-2019-0044, а также за счёт субсидии, выделенной в рамках государственной поддержки Казанского (Приволжского) федерального университета в целях повышения его конкурентоспособности среди ведущих мировых научно-образовательных центров. БЛАГОДАРНОСТИАвтор выражает искреннюю признательность коллегам С. М. Снигиревскому, А. О. Иванову, В. Н.

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: The ultimate aim of these collaborative research efforts is to develop genomic breeding values for dry matter intake or a feed conversion efficiency trait that could be used in breeding programs to improve efficiency and mitigate emissions. 1. 2. Breeding x feeding system Due to the profitability of Holstein cows, Holstein genes are present in a large proportion of dairy cows globally, particularly North American. American Holstein-Friesian cows have been found to show a better response in milk yield with a higher proportion of concentrate in their diet than smaller genotypes like the Holstein-Friesian, which have been selected for higher milk yield performance from pasture [16]. Cows which were ~88% Holstein and selected on increased milk fat and protein production (Select line cows) were found to grow faster and had increased kg milk per kg dry matter intake during their productive life when on a high energy dense diet, compared to cows selected to represent the UK average for milk fat and protein production on the same diet [10]. Select genetic line animals have a high genetic potential for mobilising body energy reserves for production, which has been found to have deleterious effects on health and fertility [3, 24], particularly later in life [7]. However, it was found [14] that Select line cows responded to a diet containing a low proportion of forage, rather than a high forage diet, by having a significantly shorter calving interval. Select line animals on a low forage diet also produced lower CO2-eq. emissions per energy corrected milk compared to non-select and cows on a high forage diet over their lifetime (Figure 1). Systems emissions can be minimised by improvements in herd health and fertility (improving longevity and productivity), and by reducing the number of replacement animals retained on the farm to reduce wastage [13, 40, 41]. Improving herd fertility in the UK back to 1995 levels could amount to a 24% reduction in methane emissions per cow by improved efficiencies of reduced herd replacements and calving interval length [40]. Cows of predominantly Holstein genes may be better suited to a high energy dense feeding system, typically found in the US, rather than a diet containing a high proportion of forage. In contrast, the performance of animals of origin had higher yields of milk solids and better fertility compared to animals of origin when compared on a range of grazing systems [42]. Therefore, selecting animals for an environment is important.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: There was a paper in Science a few months ago, using camera trap data, about how animals have become more nocturnal because of people. Camera traps can help us document and understand things like that. And as climate change continues, a lot of animals will have to change their activity. [They could] move into colder periods of the day or nighttime just because of heat stress or things like that. Q: Will the platform help people other than scientists? A: Yes, actually, a lot of our potential users, I hope, will be local and Indigenous communities. A lot of these people live in remote areas. They have access to biodiversity and they sometimes use wildlife as part of their livelihoods. They don't normally know the impact they're having, and they would like to know. That's one user group that we're very interested in getting to. Another group that I'm very interested in and very passionate about is citizen scientists. That is a very diverse group of people, from nature lovers who just want to put a camera trap in the property to see what they have, to wildlife-savvy land managers and hunters that are interested in knowing how the species there are faring and how to manage different species like deer or turkey. Camera trapping is not a citizen science activity now, and I want it to become one. I think the potential is enormous and it's a lot of fun for education. I live in in Maryland, and I put a camera trap in my backyard every winter. I am now analyzing the data, and it's amazing what you get in your backyard. I get deer and foxes and possums and squirrels—there's a lot of wildlife. Imagine if every kid in had a camera trap and put it in their backyard. We could put the data in and then kids would be doing little competitions to find out who's got more species in their backyard—and they would learn about wildlife. It could be a fantastic tool that really has the potential to bring people together and rally them around conservation. doi: 10. 1126/science. aba5966 Relevant tags: Plants & AnimalsAbout the author Share on X from news 23 Oct 2024 DNA ‘printing press’ could quickly store mountains of data F. Service 23 Oct 2024 Road metropolis unearthed in Uzbek mountains Lawler 23 Oct 2024 Predation, not fear of wolves, keeps elk from denuding View more Got a tip for Science's news department?

Output: contrast


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: Глинскому (Санкт-Петербургский государственный университет) и А. Ю. Иванцову (Палеонтологический институт РАН) за обсуждение полученных результатов и ценные сведения о геологии и стратиграфии палеозойских отложений Ленинградской области и сопредельных регионов. 1 Микрофиллы – листья высших растений, представляющие собой выросты стебля.

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: In a study in the US [1], good health and welfare in modern high input systems (cows of 90% Holstein genes) was reported, with better production efficiency and CO2-eq. emissions per unit product compared to the past. This may be explained by optimal nutrition being provided to these animals, which may not hold true for the same cows on a lower quality forage diet. Figure 1. Carbon dioxide equivalent (CO2-eq. ) emissions per kg energy corrected milk (ECM) for cows selected for increased milk fat and protein fed a low proportion of forage (●) and a high proportion (×) of forage in their diet and cows selected to represent the average for milk fat and protein production fed a low proportion of forage (■) and a high proportion (▲) of forage in their diet (from [5]). To bring about reductions in livestock GHG emissions, it has been suggested [21] that significant technological innovations will be required in the future, in addition to managing our consumption of meat and milk products. Technologies that can bring affordable efficiencies to production are being developed. Using genomic information, such as through genomic breeding values for feed related traits, described previously, and sexed semen [43] offer the potential for better selective breeding. Advertisement 2. ConclusionsReductions in GHG emissions by genetic selection of dairy cows in the past has been achieved largely by increased productivity and gross efficiency, whereby the maintenance cost of animals in the system has been reduced and less animals are required to produce the same amount of product. Based on current breeding goals, a similar rate of reduction in emissions intensity can be expected in the near future. Selecting dairy cows on feed efficiency, and possibly methane and nitrogen losses, will have a large impact on the environmental footprint of milk production, once implemented in breeding schemes. Further research and development of novel technologies to better understand the physiological and genetic differences between animals that lead to differences in energy and nitrogen efficiencies (or overall feed use efficiency) are still required. Dairy farming is a highly managed system and has the potential to make reductions in GHG emissions intensity though increased efficiencies, such as optimum animal performance (less non-productive and ill animals) and reduced inputs as shown in Figure 2, whilst still maintaining productivity. Figure 2.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: of body size are an important measurement in animal ecology, although they can be difficult to obtain due to the effort and cost associated with the invasive nature of these measurements. We avoid these limitations by using camera trap images to derive dimensions of animal size. To obtain measurements of object dimensions using this method, the size of the object in pixels, the focal length of the camera, and the distance to that object must be known. We describe a novel approach of obtaining the distance to the object through the creation of a portable distance marker, which, when photographed, creates a “reference image” to determine the position of the animal within an image. This method allows for the retrospective analysis of existing datasets and eliminates the need for permanent in-field distance markers. We tested the accuracy of this methodology under controlled conditions with objects of known size resembling Felis catus, our study species, validating the legitimacy of our method of size estimation. We then apply our method to measure feral cat body size using images collected in Tasmania, Australia. The precision of our methodology was evaluated by comparing size estimates across individual cats, revealing consistent and reliable results. The average height (front paw to shoulder) of the feral cats sampled was 25. 25 cm (CI = 24. 4, 26. 1) and the average length (base of tail to nose) was 47. 48 cm (CI = 46. 0, 48. 9), suggesting wild feral cats in our study area are no larger than their domestic counterparts. Given the success of its application within our study, we call for further trails with this method across a variety of species. 1 size is a frequently measured attribute that can provide information on the sex, diet, body condition, and potential predators of an animal (Cumming & Cumming, 2003; Gittleman & Valkenburgh, 1997). The most common methodologies used to measure body dimensions (hereafter, “size”) involve opportunistic measurements being taken during live trapping, tranquilizing, or killing of an animal (Richard-Hansen et al. , 1999). These methods allow for a range of biometric measurements, including weight, tissue and blood samples, and the specific dimensions of size. However, this direct contact with wild animals may alter their behavior, cause high levels of stress, and potentially result in injury or death (Zemanova, 2020).

Output: contrast


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: 2 Трилетные споры – споры сосудистых растений с плотной орнаментированной оболочкой тетраэдрической формы и Y-образной складкой на поверхности. 3 Фитолеймы – обугленные или слабо изменённые остатки ископаемых растений, иногда сохраняющие клеточное строение. 4 Танатоценоз – совокупность в каком-либо месте остатков мёртвых организмов.

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: Production efficiencies using management (---) that can reduce GHG emissions beginning with selective breeding of a genotype for a particular system (from [5]). Genetic improvement is a relatively cost-effective mechanism by which to achieve reductions in emissions, as the effect is cumulative and permanent. Many of the strategies for minimising emissions bring economic benefits to dairy farming through a reduction in production costs, with the added benefit of also reducing GHG emissions at little extra cost. The success of selective breeding as a mitigation strategy however, is dependent on producers being committed to its implementation. Until direct measurements on GHG traits become available on sufficiently large numbers of animals, selecting for improved efficiency, RFI, or a measure of gross efficiency (unit of output per unit of feed eaten) offer attractive ways of reducing GHG emissions. However, the direct impact on GHG emissions is currently approximate and more research is needed to accurately assess the impact. Furthermore, selection strategies in dairy cattle need to be considered in a multi-trait framework, so that unfavourable correlated responses to selection (such as reduced fertility and excessive mobilisation of body reserves) are avoided. AcknowledgementThis work was supported by funding from Dairy Australia, Meat and and the Department of Agriculture, Fisheries and Forestry under its Australia’s. Research on genomic breeding values for feed conversion efficiency was funded by the Dairy Future’s CRC and Foundation. 1. Capper J. L, Cady R. A, Bauman D. E2009The environmental impact of dairy production: 1944 compared with 2007. J. Anim. Sci. 87216021672. GerberP. VellingaT. OpioC. SteinfeldH. 2010Greenhouse gas emissions from the dairy sector- A life cycle assessment. Food and of the United Nations, Rome, Italy. 3. DillonP. . HoranB. 2006Consequences of genetic selection for increased milk production in European seasonal pasture based systems of milk productionLivest. Sci. 991411584. . AudsleyE. 2008The effect of genetic improvement on emission from livestock systems. In: Proceedings of the of Animal Production, 24-27 August, Vilnius, Lithuania, 28 5. BellM. 2011Modelling the effects of genetic line and feeding system on methane emissions from dairy systems. PhD Dissertation. of Edinburgh, UK. 6. 2007Series. . 7. WallE.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Live captures are also laborious and financially costly, meaning an intense sampling effort is required to obtain sufficient sample sizes (et al. , 2010; Mills et al. , 2016). In contrast, camera trapping offers a cost effective, non-invasive monitoring method that provides information on the presence, activity, and potential density of a target species (Kays et al. , 2020), although there are limited opportunities to gain qualitative biometric information through this approach. Considering the visual nature of the data provided by camera traps, there is the potential to opportunistically measure animal size using this non-invasive methodology. Past research has explored estimating animal size directly from camera trap images. A relatively simple method is described by Tarugara et al. (2019). They deployed carcasses atop fallen trees, and secured steel pegs 20 cm apart on the underside of these logs, so that as leopards (Panthera pardus) climbed the trees to retrieve the carcasses, their body dimensions could be estimated from this permanent scale. This methodology worked because the animal could be continuously captured in the same position and distance from the camera, allowing for the scale to provide an accurate reference point. However, such effective designs are not always possible, as animals may vary in their distance from the camera trap, limiting the information a permanent scale can provide. Leorna et al. (2022) demonstrate the utility of the pinhole camera approach in circumstances where a permanent scale cannot be used. They employed the pinhole method to provide accurate measurements of reindeer (Rangifer tarandus) body dimensions from camera trap images. To use the method, one must have measurements of the body dimension in pixels, the focal length of the camera, and the distance of the animal to the camera trap (Johanns et al. , 2022; Leorna et al. , 2022). Measuring the distance of an animal to the camera trap from within camera trap images can prove problematic. One approach has been to place distance markers at regular intervals in the camera's field of view for the duration of the monitoring period (Corlatti et al. , 2020; Hofmeester et al. , 2017). However, these markers are conspicuous and could increase the risk of theft or alter the behavior of the study species (Corlatti et al. , 2020). These markers also tend to bin distances at quite wide intervals (i. e.

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2022b), with little consideration of the diversity of the overall benthic community. Yet, research has shown the importance of assessing diversity within communities as a whole, rather than within single taxonomic groups, as species interactions may influence patterns of diversity (McDevitt-Irwin et al. 2021). The aim of the present study is to investigate patterns of diversity within MCEs over the depth gradient (15 m to 160 m) and between sites in the Chagos Archipelago, located in a particularly understudied region, the central Indian Ocean. The quantification of alpha and beta diversity indices will provide fundamental data on the patterns of species diversity and potential taxonomic overlap among communities over the depth gradient, and between shallow-water reefs and MCEs. Such data are essential to understand the role of MCEs in the functioning of coral reef ecosystems, ii. unravel the ecological contribution of MCEs to the coral reef system as a whole; and iii. develop conservation and management plans for these ecosystems. MethodsStudy areaThis study was conducted in the (6° S, 71°30’ E), central Indian Ocean. is a fully no-take MPA established in 2010, covering 640,000 km2 and incorporating shallow coral reefs, seagrass meadows, submerged banks and seamounts (Hays et al. 2020). has been uninhabited since the 1970s (with the exception of one atoll with strict environmental regulations), therefore human disturbance from pollution, agriculture and reef fisheries is minimal (Head et al. 2019; Hays et al. 2020). In this study, MCEs were investigated at two oceanographically contrasting sites around (6° 40’ S, 71 21′ E; Fig. 1): Rats (IDR, North-West coast) open to the wider ocean and (MA, North-East coast) somewhat sheltered from the Bank (Fig. 1). Both sites occur on steep slopes with rocky outcrops and strong currents prevailing from east to west and west to east, depending on the movement of the tide. Fig. 1Location of study sites within the Chagos Archipelago. A: with depth gradients extracted from GEBCO data between 10 and 200 m depth. B: central with the delimited in dark pink.

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: SimmG. MoranD. 2010Developing breeding schemes to assist mitigation of greenhouse gas emissionsAnimal43663768. van de HaarM. J. StPierre. N. 2006Major advances in nutrition: relevance to the sustainability of the dairy industry. J. Dairy Sci. 8912801291 9. Chagunda M. G. G, Römer D. A. M, Roberts D. J2009Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding periodLivest. Sci. 12232333210. BellM. WallE. RussellG. MorganC. SimmG. 2010Effect of breeding for milk yield, diet, and management on enteric methane emissions from dairy cows. Anim. Prod. Sci. 5081782611. Johnson K. A, Johnson D. E1995Methane emissions from cattle. J. Anim. Sci. 732483249212. Ulyatt M. J, Lassey K. R, Martin R. J, Walker C. F, Shelton I. D1997Methane emission from grazing sheep and cattleIn: Proceedings of the Society of Animal Production5713013313. TammingaS. BanninkA. DijkstraJ. ZomR. 2007Feeding strategies to reduce methane loss in cattleGroup report, Wageningen, The Netherlands. 14. BellM. WallE. RussellG. SimmG. StottA. 2011The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systemsJ. Dairy Sci. 943662367815. Veerkamp R. F1998Selection for of on : A ReviewJ. Dairy Sci. 811109111916. DillonP. 2006Achieving high dry-matter intake from pasture with grazing dairy cows. In: editors. for Dairy Cattle, the Key to a Chain. Wageningen UR 18Springer, Dordrecht, The Netherlands. 12617. KennedyJ. DillonP. DelabyL. FaverdinP. StakelumG. RathM. 2003Effect of genetic merit and concentrate supplementation on grass intake and milk production with Holstein-Friesian dairy cows. J. Dairy Sci. 86610621 18.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: The key advantages of our methodology for measuring body size in camera trap images are two-fold: (i) the distance marker does not need to remain at the field-site, thus lowering costs and mitigating theft risk, and (ii) as a consequence, this approach can be used to measure animal body size from historical datasets, by re-visiting the site and replicating the position of the previously placed camera, and taking a reference image with the distance marker in place. We were able to integrate our method of estimating body size into a pre-existing camera trap survey as our camera traps were still operational in the field. For researchers wishing to apply this method to a historical dataset, sufficient information must be available regarding the camera trap's location and positioning. This includes the height of the camera trap, angle, and tree/location of the post on which the camera was deployed. It is recommended that the height and angle of the camera trap are reported in all camera trap studies, along with habitat information (Meek et al. , 2014). Researchers who have photographed their camera trap deployments when recording this information will likely find it easier to apply our method to their historical datasets. The margin of error varied among individuals: some exhibited consistent size estimates across images, while others showed greater variability. This variability could be attributed to the differences in the position of the animal in each photograph (Tarugara et al. , 2019), or some foreshortening introduced by slightly oblique angles (e. g. , Figure 5, left panels). This corroborates the findings of Leorna et al. (2022), who noted a decline in measurement accuracy for reindeer as the distance to the camera trap increased or when photographed at an angle. Additional variation in our measurements was also introduced by the non-exact nature of our distance marker, which measured distance in intervals of 10 cm. This is because the resolution of the camera trap images was too low to examine more precise intervals (e. g. , 5 cm and 1 cm). Despite these potential sources of error, the confidence intervals were tight for most individuals, indicating that our method provided consistent measurements despite uncertainties in angles and distance among camera sites. Obtaining pixel measurements for each image was done by hand within out study, although the utilization of deep-learning image processing software by future researchers could provide an automated alternative.

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: C: displaying bathymetry data acquired using multibeam (between 10 and 200 m depth) and the two study sites: Rats (teal diamond) and (orange diamond)Full size imageBiological dataA Vehicle (model) with a Go4 (2. 7k, 24 fps, wide field of view) attached to it was used to collect benthic imagery from MCEs at the two study sites, IDR and MA. An information overlay on the ROV SD camera displayed time, depth, heading, pitch and roll. Full ROV specifications and details of the biological data collection are provided in detail in Diaz et al. , (2023a), but briefly, HD still images were extracted from the GoPro video transects when the ROV approached the seabed and was positioned at an oblique angle (altitude of <1. 5 m). The presence of a strong current at both sites, precluded linear benthic video transects. The entire MCE depth gradient was surveyed, with 6 depth zones chosen a priori to the study based on the literature, covering the shallow reef and the lower mesophotic zones: 15–20 m; 30–40 m; 60–70 m; 80–90 m; 110–120 m; 150–160 m. Three transects were surveyed per depth zone (average transect duration = 30 mins 3 s ± 6 mins 58 s), parallel to the slope at each of the two study sites. Ninety images were collected per depth zone (n=30 images/transect) with a total of 1,080 images from the two study sites. All data were collected during research expeditions in November 2019 (transects 1, 2, 3 for IDR; transect 1 for MA) and March 2020 (transects 2 and 3 for MA). Still images collected along each transect were manually annotated by a single observer using 2. 0, an online annotation platform (Langenkämper et al. 2017), which enables identification and enumeration of organisms. The image analysis procedures are provided in detail in Diaz et al. , (2023a). All specimens were identified according to both relevant literature and taxonomist’s support (Tables S1 and acknowledgements). During the study, a morphospecies catalogue was created for the region (Diaz et al.

Output: pass


Input: Claim: Livestock farming contributes significantly to global greenhouse gas emissions, particularly methane. [SEP] Text: R. F. 2011Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94612229. Hegarty R. S, McEwan J. C2010Genetic opportunities to reduce enteric methane emissions from ruminant livestock. In: Proceedings of the 9th on to Livestock Production, 16August, Leipzig, Germany. 30. 2002Potential to reduce greenhouse gas emissions from beef production by selection for reduced residual feed intake. In: Proceedings of the 7th on to Livestock Production, Montpellier, France, Communication 10-22102231. CorkellB. 2007Cattle selected for lower residual feed intake have reduced daily methane production. J. Anim. Sci. 851479148632. YanT. 2010Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cowsJ. Dairy Sci. 932630263833. . MirZ. PriceM. A. Arthur. P. F. MooreS. S. 2003Residual feed intake- what is it and how does it differ from traditional concepts of feed utilization. In: Proceedings of for Animal Science, Annual meeting, 1013June, Saskatoon, Saskatchewan, Canada. 34. . LiC. . MooreS. S. 2006Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 8414515335. Veerkamp R. F2002Feed intake and energy balance in lactating animals. In: Proceedings of the 7th on to Livestock Production, Montpellier, France, Communication 10-01100136. CoffeyM. P. SimmG. HillW. G. BrotherstoneS. 2003Genetic evaluations of dairy bulls for energy balance profiles using linear type scores and body condition score estimated using random regression. J. Dairy Sci. 862205221237. Pryce J. E, Harris B. L2006Genetics of body condition score in dairy cows. J. Dairy Sci. 894424443238. Lassey K. R2007Livestock methane emission: From the individual grazing animals through national inventories to the global methane cycle. Agricult. Forest Meteorol. 14212013239. PryceJ.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Our field trial provided the first estimates of the size of feral cats living in the dense rainforests and tall wet forests of south-east Tasmania, in areas remote from human settlements. The average estimates of height and length for cats in our study was 25. 3 cm (CI = 24. 4, 26. 1) and 47. 5 cm (CI = 46. 0, 48. 9), which is similar to that of typical domestic cats of 46 cm for length and 23–25 cm for height (Sunquist, 2002). This is particularly true when one considers that our accuracy trial indicated that our methodology consistently over-estimates dimensions. As such, our findings do not contain any evidence that supports the phenomenon of Australian “panthers” and “big cats,” which is commonly reported in the media, but not currently supported by any scientific literature (Menkhorst & Morison, 2012). However, we have only sampled a small pocket of the Tasmanian wilderness herein. The pinhole camera approach we employed provides an opportunity to substantiate claims of giant cats in Australia where shooting and trapping fail or are unavailable, particularly considering this method can be applied to historic data. 5 pinhole camera approach is a cost-effective method to estimate animal body size if using pre-existing camera trap surveys, allowing researchers to exploit past data. There are several caveats associated with our method. A researcher must return to the site of a camera trap survey and replicate deployment to obtain a reference image if using past data, and a wildlife image should only be used if the animal photographed is parallel to the camera-trap, such that the whole flank can be seen. This can limit the amount of data available to be used. Additionally, pixel measurements need to be taken consistently, although this step could be aided by the integration of a measurement AI. Nonetheless, this method provides a non-invasive alternative to live capture or killing that consistently provides precise animal dimensions. We encourage other researchers to test the pinhole approach with other models of camera trap, species, or captive populations to further validate this method. . Paton: Conceptualization (lead); data curation (equal); formal analysis (lead); investigation (lead); methodology (lead); resources (supporting); visualization (lead); writing – original draft (lead); writing – review and editing (equal). Barry W. Brook: Conceptualization (supporting); data curation (equal); supervision (equal).

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2023b) following the global standardised marine taxon reference image database created by Howell et al. , (2019); a total of 582 OTUs were identified in this study. Only one observer annotated the images to reduce the annotation bias. In addition, quality assurance was carried out by re-annotating 5% of the images by the same observer, as recommended in Schoening et al. (2016). A summary of the total number of morphospecies and individuals per site and depth zone are shown in Table S2. The ROV was not equipped with paired lasers during the surveys, thus calculation of the field of view within each image was not possible. To standardise data collection, the pilots positioned the ROV at a similar distance from the seabed, with the same angle of view, for each image collected. The mean time taken to complete a single transect was 30 min 3 s (± 6 min 58 s), with no significant differences observed in the time taken to complete a transect with depth zone and site (Kruskal–Wallis test, p-value > 0. 05. Transect by depth zone: chi-squared = 10. 244, df = 5, p-value = 0. 069; transect by site: chi-squared = 0. 0022733, df = 1, p-value = 0. 962). Thus, given the consistency in the speed of the ROV, it can be assumed a comparable area of seabed was surveyed within each transect and depth zone. To enable comparisons between sites and depth zones, this study focuses on the number of images surveyed and counts of benthic organisms rather than an area-based analysis. Furthermore, as suggested by Colwell et al. (2012) and Roswell et al. (2021), data were standardised by individuals and sampling coverage per depth zone using the iNext package in R software (Hsieh, Ma and Chao, 2020) (see next section). Statistical analysesTo assess the variation in alpha and beta diversity of MCEs over the depth gradient and between the study sites, data were analysed using diversity indices with the iNEXT (iNterpolation/EXTrapolation) (Hsieh, Ma and Chao, 2020) and betapart packages (Baselga and Orme 2012) in R software (Core 2020).

Output: pass


Input: Claim: The majority of organic farming avoids the use of synthetic chemicals like pesticides and fertilizers. [SEP] Text: The agricultural community/farmers are struggling to obtain higher rate of yield due to lack of poor knowledge about the soil and water nutrients and suitability of the organic crop for the soil. Most of the farmers use excessive chemical fertilizers in-order to increase productivity of their yield, without aware of side effects. The excess usage of chemical fertilizers by the farmers will have impact on the quality, fertility, and salinity of the soil. To overcome these issues and to promote concept we propose an IoT enabled sensor system for monitoring soil nutrient [NPK] and pH of irrigation water to reduce the manual laboratory method of testing and get the results via mobile application and to promote organic farming in the agricultural field. Smart organic farming based mobile application will further process these nutrients value to predict and suggests the suitable crop to grow and the usage of appropriate amount of fertilizer to maintain the soil fertility there by achieving optimum usage of chemical fertilizer because continuous and wrong usage of these chemical fertilizer have a harmful effect not only on soil but also on crops, we consume leading to unhealthy human life. The proposed mobile application also helps in establishing the connection between farmers and (APMC) in order to avoid fragmentation of profit shares and attain Pricing uncertainty and marketing of the yields by avoiding the middle man. APMC is a state government body which ensures safeguard to the farmers from exploitation by large retailers and suggest the kind of crop to be grown with organic farming. India is well known to produce organic fertilizer which is produced by the waste of slaughterhouses, plant and animal residues, biological products and other natural resources. Thus, the proposed work helps the farmers in adopting stress-free organic farming practice by self-testing their field soil parameters for generating quick soil analysis reports and also helps in connecting with APMC to know the suitable crop for their agriculture land based on the soil and water analysis (SWA) report, dispensing the required amount of organic fertilizer to the soil based on soil and water nutrients analysis using IoT enabled sensor, funding/insurance to the crops in case of occurrence of unpredictable natural disaster in future and direct marketing facility without middle man and maintain sustainable agriculture. In the present era, the industry is at 5. 0 levels but agricultural production is still at 2. 0 levels. In this 1. IntroductionIn the era technology enable modern agriculture, the people are keen about the organic farming due to environmental friendliness and more profitable agriculture. The people are diverting towards the organic farming as it uses less chemical-intensive pesticides.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Abstract Traditionally, spoor (tracks, pug marks) have been used as a cost effective tool to assess the presence and in some cases the individual identity of larger mammals. Automated camera traps are now increasingly utilised to monitor wildlife, primarily as the cost has greatly declined and statistical approaches to data analysis have improved. While camera traps have become ubiquitous, we have little understanding of their effectiveness when compared to traditional approaches using spoor in the field. Here, we 1) test the success of camera traps in recording a range of carnivore species against spoor in realistic field settings (dirt roads in a wilderness reserve); 2) ask if simple measures of spoor size taken by amateur volunteers are likely to allow individual leopards to be tracked in the field and 3) for a trained tracker, ask if this approach may allow individual leopards to be followed with confidence in savannah habitat. We found that camera traps under-recorded mammalian top and meso-carnivores when compared with spoor in the field, with camera traps more likely to underrecord the presence of smaller carnivores (civet 64%; genet 46%, Meller's mongoose 45%) than larger (jackal sp. 30%, brown hyena 22%), while leopard was more likely to be recorded by camera trap (all recorded by camera trap only). We found that amateur trackers could be beneficial in regards to recording leopard presence; however the large variance in measurements of spoor taken by volunteers suggests that this approach is unlikely to allow the collection of further information about individual leopards. Nevertheless, the use of simple spoor measurements in the field by a trained field researcher increases their ability to reliably follow a leopard trail in difficult terrain. This allows researchers to glean further data on leopard behaviour and habitat use without the need for complex spoor analysis. The successful conservation of any species is predicated on our ability to understand its abundance and distribution (Stander 1998, Hussain 2003, Gusset and Burgener 2005, Houser et al. 2009, Trolliet et al. 2014). While some taxa (e. g. birds) have the benefit of being relatively well studied, many species such as the larger mammalian carnivores are notoriously difficult to directly monitor in the field (Stephens et al. 2006).

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Alpha diversity analysesTo allow comparisons among sites and depth zones, despite possible differences between survey areas covered, a combination of asymptotic and non-asymptotic diversity estimations was applied, based on rarefaction and extrapolation analyses and Hill numbers, using the iNext package in R. Individual-based rarefaction and extrapolation (R/E) curves, as well as sample coverage-based R/E curves, were plotted, with non-overlapping 95% confidence intervals interpreted as a significant difference, as per García-Martínez, et al. , (2017) and Cumming et al. (2007). Individual-based R/E curves were used as they provide unbiased estimates of species diversity and enable comparison of morphospecies diversity between depth zones and sites, specifically when they reach an asymptote. The protocol proposed by Chao et al. (2020) was followed to assess and compare alpha diversity and sample completeness among our surveys. Firstly, Hill numbers, also known as the effective number of equally abundant species qD, were used (Hill 1973; Chao et al. 2014), and the three most widely used orders (i. e. q = 0, q = 1 and q = 2) were assessed here. 0D corresponds to species richness, which is an indication of the number of species present in the community without considering their abundances and thus gives disproportionate weight to rare species. 1D indicates the measure of Shannon diversity (i. e. exponential of Shannon entropy), which weighs each species according to its abundance in the community and can be interpreted as the number of ‘common’ species present in the assemblage. Finally, 2D represents Simpson diversity (i. e. inverse of Simpson concentration index), which discounts all but the dominant species and thus can be interpreted as the number of highly ‘abundant’ (or dominant) species in the community (Chao et al. 2020). To allow for comparisons among the investigated benthic communities, the steps proposed by Chao et al. (2020), were followed as listed below. Firstly, sample completeness of each survey was estimated via a bootstrap method (n = 500) for q = 0, 1, 2 to compare the diversity detected, or sample completeness, between each depth zone and site (Table S3).

Output: pass


Input: Claim: The majority of organic farming avoids the use of synthetic chemicals like pesticides and fertilizers. [SEP] Text: 4. 1 Section 2: Literature surveyOrganic farming started along with the human civilization but it was totally dependent on nature. Due to increase in the population, there is higher demand for the agricultural products also increased. In-order to cope with this demand farmers started using fertilizers. Indiscriminate use of pesticides and chemicals polluted soil, water and whole ecosystem. Off late our farmers from different parts of India are switching over to sustainable agriculture practice in fields by adopting smart farming techniques as part of campaign to promote Digital Agriculture. Exhaustive literature survey is carried out to determine the soil primary nutrients and pH value of the irrigation water of the agriculture field, the technologies employed for the agricultural activities, the mobile applications etc. The research work is enlisted in the Table 2. Sustainability dimensionCriteriaSocial sustainabilityCreating self-reliance in organic productionEmpowering rural communities through partnerships with other farmers to form groups (providing participatory guarantee system and the strengthening of social organization)Increasing farm employmentImproving employment opportunities, particularly in rural areasEconomic sustainabilityImproving efficiency in areas with low inputs (pesticides, herbicides, etc. )Reducing crop damage and Reducing risk of productionSatisfying farmers from an economic perspectiveAdded value of organic products through marketing activities and processingEfficient usage of resources more efficiently (to minimize the use of non-renewable resources)Affordable than traditional agriculture (due to lower variable costs of inputs, identical Fixed costs and higher prices of organic products)Greater economic profitability due to the use of domestic inputsEnhancing the overall performance of the farm in unit areaEnvironmental sustainabilityMaintaining soil fertility in the long-termImproving soil structureIncreasing soil water retention potentialProducing food without chemicalsUsing environmentally friendly production methods (such as the use of animal products, intercropping, use of mulch, using natural pesticides, crop residue returned, green manure, compost, crop rotation)Reducing environmental pollution (pollution reduction of water, soil, and air)Table 1. Important sustainability criteria in organic farming. ReferenceProposed workSensor used[1]IoT sensor for monitoring the soil moisture content with automatic irrigation to save excess water usage.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Traditionally, indirect methods such as locating den sites, and scat or spoor (tracks, pugmarks) surveys have been utilised as a highly cost effective method (Gusset and Burgener 2005) to determine the presence or absence, abundance or population density for species such as the leopard Panthera pardus, snow leopard Uncia uncia, cougar Felis concolor, lion Panthera leo, caracal Caracal caracal, tiger Panthera tigris and pine marten Martes martes (Beier and Cunningham 1996, Zalewski 1999, Hussain 2003, Melville and Bothma 2006, Sharma et al. 2005, Houser et al. 2009, Sanei et al. 2011, Sheehy et al. 2014). It has been suggested that the sex of tracked Panthera spp. and cougar Felis concolor can be judged from size differ enees in spoor (Bothma 1984, Stander et al. 1997, Stander 1998, Sharma et al. 2003, Sanei et al. 2011, Gu et al. 2014); indeed, if enough features are recorded, leopard, snow leopard, tiger, white rhino Ceratotherium simum and black rhino Diceros bicornis individuals may be identified from spoor alone (Stander et al. 1997, Riordan 1998, Jewell et al. 2001, Sharma et al. 2005, Alibhai et al. 2008, Balme et al. 2009). However, using spoor alone to estimate tiger densities in the field has been criticised for giving unreliable results, as simple measurements or analyses fail to provide the discrimination required (Karanth et al. 2003). Established methods were to take plaster casts or tracings of spoor on acetate from which measurements were taken (Lewison et al. 2001). Sharma et al. (2005) highlighted that data gathered in this way were of poor quality and inconsistently collected. Most feline spoor studies have been investigated in snow (Hayward et al. 2002), tropical (Sanei et al. 2011) or clay substrates (Garcia et al. 2010), sandy loam (Lewison et al. 2001), or thick soil (Sharma et al. 2005), which are often more than 5 mm in depth. Lewison et al.

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Secondly, the observed and extrapolated diversities at each site and depth zone were estimated by computing individual-based and sample-size based R/E curves for Hill numbers of q = 0, 1, 2; with extrapolation to a sampling effort equal to double the number of individuals (extrapolation beyond this is unreliable due to prediction bias; (Chao et al. 2014)). Thirdly, the observed and extrapolated Hill numbers were compared to assess undetected diversity and compare diversity between the depth zones and sites (Table S4). However, these results must be taken with caution as they were extracted prior to sample completeness standardisation. Finally, diversity estimates were calculated at a standardised coverage Cmax (e. g. equally complete samples) to statistically compare the benthic diversities along the depth gradient and between sites, which corresponds to the sample coverage of the depth zone exhibiting the lowest coverage when samples are extrapolated to double the number of samples per depth zone at a site (Table S5), as used in El-Khaled et al. (2022). Here, the Cmax value was 96. 4% (Fig. S1; Table S5), corresponding to the lowest sample completeness for q = 0, 1, 2. Beta diversity analysesBeta diversity was measured as the variation in morphospecies with depth and between sites. Jaccard’s dissimilarity was calculated, including its two components, turnover (i. e. species replacement) and nestedness (i. e. subset of a richer community), using the entire benthic community presence-absence data for each depth zone and site. Jaccard dissimilarity varies between 0 and 1, with values close to 1 revealing a high difference in the composition of benthic communities between two depth zones, while a low dissimilarity (close to 0) denotes similar benthic communities. These indices were calculated using the betapart package and plotted in a heatmap, as well as a barplot with ggplot2 package in R (Baselga and Orme 2012; Core 2020). ResultsAlpha diversityA total of 582 distinct morphospecies (OTUs) were identified from the benthic communities at Egmont Atoll, between 15 and 160 m, covering 17 higher taxa (ranging from phylum to genus; Table S2).

Output: pass


Input: Claim: The majority of organic farming avoids the use of synthetic chemicals like pesticides and fertilizers. [SEP] Text: 3248 T/H, since this is the limit of the optimal range for plant nutrients. But when nutrient levels in the soil are below that range, yield naturally begins to decline. Figure 10. (a) The reduction of organic matters first enhances yield largely due to the use of chemical fertilizers, but yield falls over time along with the decline of organic matters; (b) Yield improves with a relatively small uptick in pest population, but when pest population grows significantly, yield drops precipitously; (c) Plant nutrients increase when organic matters fall as a result of the effects of chemical fertilizers, but the plant nutrients actually decline in the end; (d) Increased plant nutrients improve yield within the standard value of plant nutrients; otherwise, production tends to declineFigure 11. State variables converge with different initial valuesState variables, such as yield, organic matters, and plant nutrients, converge over a wide range of initial values, as exemplified by Figure 11. These factors instill confidence in the proposed model's boundedness and the reliability of the study's analysis. As a logical consequence, it may be concluded that the newly proposed model is biologically valid. 4. 12 Results and DiscussionBangladesh is predominantly an agriculture-based country with rice as the major food crop. Furthermore, farming heavily depends on healthy soil. To put it simply, poor soil quality results in subpar yields. It took just ten kilograms of fertilizers to grow five mounds of paddy two years ago, but now it takes between 14 and 15 kilograms to get the same result. Soil fertility has been declining due to the widespread use of chemical fertilizers (Muhammad, 2022). Meanwhile, production is declining. Research in the field of agriculture warns that if the current trend continues, the agricultural system might eventually collapse, causing a serious food shortage in the near future. By analyzing the proposed model for this food security problem, some results can be observed virtually in the form of graphs. Generally, yield is affected by plant nutrients and soil organic matters. Figure 5 suggests that if farmers use more organic fertilizers, it leads to a slow increase in yield and plant nutrients. Commonly used organic fertilizers include composted animal manure, compost, sewage sludge, food processing wastes, and municipal biosolids. They improve soil health and release nutrients to soils gradually.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: They can have a significant advantage over indirect means of recording individuals, often allowing the population size of cryptic species (notably the large cats) to be estimated using capturerecapture models (Karanth 1995, Karanth and Nichols 1998, Trolle and Kery 2005, Jackson et al. 2006, Balme et al. 2009, Royle et al. 2009, Chapman and Balme 2010, Negroes et al. 2012, Maputla et al. 2013, Tobler and Powell 2013). Although abundance can be estimated relatively accurately when using camera traps (Chapman and Balme 2010) providing that the probability of detection is high, cameras are positioned appropriately and camera avoidance is low (Maputla et al. 2013), there remains a lack of a standardised method of camera trap-based mark—recapture (Kelly 2008) and issues such as camera performance and efficacy have still not been satisfactorily addressed (Maputla et al. 2013, Urlus et al. 2014). However, it is detectability which presents the greatest challenge in effectively sampling the species abundance in a surveyed area (Royle and Nichols 2003) as little is known about how species vary in their likelihood of being recorded by camera traps (Balme et al. 2009, Ballard et al. 2014). In most studies the proportion of individuals which enter the camera trap range and fail to trigger the unit is unknown, resulting in under-estimation of distribution and/ or abundance. One approach is to compare camera trap results with spoor. Lyra-Jorge et al. (2008) found that compared to spoor, film cameras with a trigger delay of one second collectively under recorded by 1. 65 times puma, Puma concolor; maned wolf, Chrysocyon brachyurus; mazama, Mazama sp; striped hog-nosed skunk, Conepatus semistriatus; armadillo, Dasypus sp. and forest rabbit, Sylvilagus brasiliensis. In such studies, spoor is usually recorded in sand traps, which maximises the likelihood of successfully recording a passing individual. In field work such perfect recording opportunities are rare and conditions often affect ability to record spoor (Lyra-Jorge et al. 2008, Balme et al. 2009).

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: All morphospecies identified can be consulted in the freely accessible catalogue created for the region (Diaz et al. , 2023b). Species richness (q = 0) was observed to decrease with depth at both sites, with lower diversity observed at 110–120 m and 150–160 m compared to the shallower depth zones (Fig. 2a). Although only conservative minimum estimates could be obtained for species richness (as individual-based R/E curves did not reach an asymptote; Fig. 2a), at the fixed sample coverage of Cmax = 96. 4%, benthic morphospecies richness was significantly lower for the two deepest zones compared to the shallower depth zones (Fig. S1c, Table S5; non-overlapping 95% confidence intervals (CIs) for the two deepest depth zones, overlapping CIs for 15 m to 90 m depth zones). Fig. 2Overview of alpha diversity analysis based on Hill numbers. a) individual-based Rarefaction/Extrapolation (R/E) curves for q = 0. b) individual-based R/E curves for q = 1. c) individual-based R/E curves for q = 2. Shaded areas represent the 95% confidence intervals for each curve. Shades of blue represent the different surveyed depth zones at Rats (IDR), and the shades of orange are the depth zones at (MA). Solid lines are the rarefaction curves while the dashed lines represent the extrapolation curves up to double the respective sample sizeFull size imageEstimates of Shannon and Simpson diversity reached asymptotes for all depths at both sites and overall showed significant decreases in diversity over the depth gradient, from 15 to 160 m (Fig. 2b, c; Fig. S1b, c; non-overlapping CIs). The exception was at IDR, where both Shannon and Simpson diversity were significantly higher at 30–40 m compared to 15–20 m (Fig. 2b, c). At both sites, Shannon diversity between 60–70 m and 150–160 m was significantly lower than observed at shallower depths; however, there was no significant difference in Shannon diversity between 80–90 m and 110–120 m (Fig. 2b). Simpson diversity differed between the two sites (Fig.

Output: pass


Input: Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: Abstract Experience-dependent modifications of synaptic connections are thought to change patterns of network activities and stimulus tuning with learning. However, only a few studies explored how synaptic plasticity shapes the response dynamics of cortical circuits. Here, we investigated the mechanism underlying sharpening of both stimulus selectivity and response dynamics with familiarity observed in monkey inferotemporal cortex. Broadening the distribution of activities and stronger oscillations in the response dynamics after learning provide evidence for synaptic plasticity in recurrent connections modifying the strength of positive feedback. Its interplay with slow negative feedback via firing rate adaptation is critical in sharpening response dynamics. Analysis of changes in temporal patterns also enables us to disentangle recurrent and feedforward synaptic plasticity and provides a measure for the strengths of recurrent synaptic plasticity. Overall, this work highlights the importance of analyzing changes in dynamics as well as network patterns to further reveal the mechanisms of visual learning. Introduction Experience-dependent changes in neural responses have been suggested to underlie the more efficient and rapid processing of stimuli with learning. Human and monkeys have been reported to process familiar stimuli with shorter response times and with less effort (Greene and Rayner, 2001; Logothetis et al. , 1995; Mruczek and Sheinberg, 2007). The possible neural correlate for such behavior enhancement is the sharpening of stimulus selectivity that is achieved by broadening the distribution of activities as the stimulus becomes familiar (Freedman et al. , 2006; Kobatake et al. , 1998; Lim et al. , 2015; McKee et al. , 2013; Woloszyn and Sheinberg, 2012). Also, temporal sharpening of neural responses with experience has been observed, which can increase the resolution of discriminating stimuli in time with learning (Meyer et al. , 2014; Recanzone et al. , 1992). Modifications of synaptic connections have been thought to be one of the basic mechanisms for learning. A repeated encounter of a stimulus would elicit a particular activity pattern in the network, which in turn modifies synaptic connections depending on pre- and post-synaptic activities. Such modifications of synaptic connections lead to changes in neural responses that can be a substrate to differentiate learned and unlearned stimuli.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Camera traps work was part of a wider study of the ecology of leopards, utilising a network of over 30 camera traps across an area of 5400 ha. Camera traps were sited along unpaved dirt roads, which tend to be used as highways by many animals (Rowcliffe and Carbone 2008). Four sites (Fig. 1) were chosen among these to 1) maximise the likelihood of recording high numbers of predatory mammals, 2) have the correct natural substrate for spoor recording and 3) were within walking distance of base camp in order to collect data before the movement of vehicles occurred. The cameras 5210A camera traps were utilised in this study. Each camera unit had a trigger time of one second and was set to photo mode, at normal sensitivity level. The cameras have three sensors, with two requiring activation before an image is taken. The sensors detect motion and heat and can be triggered up to 15 m away. Images were taken in bursts of three and the interval between captures was set at 30 s to reduce battery depletion resulting from moving vegetation or large groups of animals passing by the site. A single camera was placed at one side of a ‘T’ junction at four study sites, to increase the probability of capturing an animal. The camera unit was attached to a metal stake which was pushed into the ground resulting in the lens being positioned approximately 45 cm off the ground with the sensors at 40 cm. This height allowed for a range of species including meso-carnivores to trigger the sensors. The camera was angled 45° to the road to allow an animal to be in range of the lens during the one second delay between the sensors being triggered and the animal being photographed. Any vegetation which could trigger the camera or hinder the view of an animal in the image was removed prior to the survey and during monitoring. Camera sites were checked every morning, with cards and batteries being exchanged once a week. As the sites were part of a longer running study in a commercially utilised part of the reserve we felt that our activity would not impact greatly on the normal movement of animals living in the area. However to check for this we compared spoor located inside and outside of the trap zone as discussed under spoor surveys. Additionally, we compared image capture rate in weeks preceding and following the study, and found no difference in image collection rates (data not shown).

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2c): at MA, Simpson diversity was significantly different between 15–20 m and 30–40 m and the deeper depths; however, the values did not differ significantly between the depth zones from 60–70 m to 110–120 m; while at IDR, the 80–90 m depth zone showed a significantly lower Simpson diversity, compared to both 60–70 and 110–120 m (Table S5). Finally, the values were significantly lower at 150–160 m compared to shallower depth zones for both sites (Fig. 2c; Fig. S1b, c; Table S5). In this study, the diversity curves did not all reach an asymptote, indicating that total diversity was not captured (Fig. 2). This undetected diversity for each depth zone at each site is detailed in Table S4. Nevertheless, coverage-based sample completeness profiles revealed that between 93. 8% and 97. 3% of benthic morphospecies diversity was detected, depending on the depth zones and sites (Table S3), showing that while a few species were undetected, an acceptable standardized value for comparing species richness between communities was sampled (Moreno and Halffter 2000). Beta diversityValues of beta diversity between depth zones for the entire benthic community were similar between both study sites (Fig. 3a and d). Total beta diversity values were high between the depth zones, throughout the entire depth gradient for both sites (from 15 to 160 m; Jaccard 0. 61 to > 0. 96; Fig. 3, Table S6, Fig. S1), indicating a limited number of shared morphospecies between depth zones. Adjacent depth zones displayed less dissimilarity compared to depths located further apart (Jaccard 0. 65 for 15–20 m/30–40 m, to 0. 81 for 110–120 m/150–160 m; 0. 85 for 80–90 m/150–160 m to 0. 95 for 15–20 m/150–160 m, respectively; Fig. 3a and d, Table S7, Fig. S1). Fig. 3Changes in beta diversity and its two components–turnover and nestedness – with depth for the entire benthic community at both study sites Rats (IDR, a to c) and (MA, d to f), using Jaccard’s dissimilarity index from 0 to 1.

Output: pass


Input: Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: The previous modeling works investigated the relationship between synaptic plasticity and changes in network activity to find a synaptic plasticity rule that can account for sharpening of stimulus selectivity observed with learning (Dayan and Abbott, 2005; Gerstner and Kistler, 2002). However, whether such rules can also explain temporal changes in neural responses is in question. In this work, we investigate the mechanism underlying changes of response dynamics with learning. To this end, we consider neural activities recorded in inferior temporal cortex (ITC) known to be important for visual object recognition (Miyashita, 1993; Tanaka, 1996). In ITC, changes in the response properties with learning have been reported in several experiments (Freedman et al. , 2006; Li et al. , 1993; Lim et al. , 2015; Logothetis et al. , 1995; McKee et al. , 2013; Woloszyn and Sheinberg, 2012; Xiang and Brown, 1998). The average over different visual stimuli of time-averaged responses decreases with familiarity, while the distribution of responses across visual stimuli broadens with learning. The dynamics of visual responses were also found to change with familiarity – in particular, rapid successive presentation of familiar images, but not novel images, elicits strong periodic responses (Meyer et al. , 2014). Previously, we investigated synaptic plasticity in recurrently connected circuits that reproduces changes in the distribution of time-averaged visual responses observed experimentally (Lim et al. , 2015). As the distribution of time-averaged visual responses of a single cell to multiple stimuli can be a surrogate for a spatial pattern of the network to one stimulus in a homogeneous network, the previous work mainly focused on how recurrent synaptic plasticity shapes the network pattern and stimulus tuning. Here, we extend our previous framework to understand mechanisms underlying changes of temporal patterns with learning. First, we demonstrate that the synaptic plasticity rule inferred from the time-averaged responses is not sufficient to reproduce changes in response dynamics. Next, we show that the interaction between synaptic plasticity and negative feedback mechanisms is critical for generating stronger oscillation after learning. Using a mean-field analysis, we identify the conditions on synaptic plasticity and negative feedback to reproduce changes in response dynamics consistently observed in different experimental settings. Finally, we validate these conditions through network simulations and infer the post-synaptic dependence of synaptic plasticity from the experimental data.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: The thickness of substrate was recorded as being below 2 mm or over 2 mm (rarely more than 5 mm). Only complete and clear spoor were recorded. Partial prints were not recorded to avoid misidentification. Species identification from spoor recorded in the field were checked by observers using Liebenberg (2005) and re-checked from the image by an experienced tracker. For both camera trap and spoor surveys, presence of a given species was logged once per night per location, irrespective of the number of spoor and/or images recorded. Figure 2Open in figure viewerPowerimage of carnivore spoor taken for confirmation of species identification with measurement zeroed at the back of the metatarsal or metacarpal. Inter-observer variation in spoor measurements A plaster cast of a clear leopard print was made from a spoor found in fine mudstone sand over 2 mm thick. Hairspray was used to fix the track to avoid the p13. 3, p = 0. 02; Table 1) and that there was no camera avoidance. Table 1. Number of spoor recorded inside and outside of the camera trap area for each species during the study. Spoor found inside the trap area Spoor found outside the trap area Hyaena brunnea 15 10 Canis s2. Number of recordings per species during the study period as camera trap images alone, spoor alone, or both images and spoor. images recorded Only spoor recorded Both spoor and image Hyaena brunnea 4 8 3 Canis s0. 114 total recordings of species camera-1 h-1. For spoor in the trap area, a mean of 0. 25 species trap area-1 h-1 was recorded, suggesting that overall spoor recorded the presence of a species more than twice as frequently as the camera traps. There was no significant difference between the number of nights species were recorded using the camera traps or using spoor located within the trapping area when leopard was included (t5 = 2. 57, p = 0. 23; Table 2). However when leopard was omitted from the analysis the result became highly significant (t4 = 2. 78, p < 0. 01), indicating camera traps significantly under-recorded compared to spoor. Leopard was recorded on three occasions by camera trap during the study, but not by spoor inside the trap area.

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Values close to 0 indicate a high level of similarity between the two communities being compared and values close to 1 indicate a low level of similarityFull size imageTurnover was the largest component of beta diversity, thus it can be considered as the main driver of differentiation between benthic communities, indicating that the change in morphospecies diversity along the depth gradient, from 15 to 160 m, was mostly due to species replacement (Jaccard > 0. 5 to > 0. 9; Fig. 3b and e; Table S7, Fig. S1). On the contrary, nestedness had only a limited influence on the overall dissimilarity with depth (Jaccard 0. 01 to < 0. 2; Fig. 3c and f; Table S7, Fig. S1); showing that, while species diversity broadly decreased with depth (Fig. 2), deeper communities were not just a subset of richer communities observed at shallower depths. The differences between the turnover and nestedness components, compared to total dissimilarity, are detailed in Fig. S1. Between sites, for the same depth zone, beta diversity did not show a strong pattern (Fig. 4). Overall, dissimilarity was less pronounced between sites in shallow-water communities (15–20 m) compared to communities at mesophotic depths (30–160 m), with 60–70 m and 150–160 m zones showing the highest benthic dissimilarity at a local scale. Similarly to the beta diversity calculated along the depth gradient, turnover was the main component of beta dissimilarity between sites, indicating benthic dissimilarity is mainly driven by species replacement at a local scale. Fig. 4Jaccard’s dissimilarity and its two components – turnover and nestedness – between the two study sites (Ile des Rats and Manta Alley) for a depth zone. Jaccard’s dissimilarity index ranges from 0 to 1, with values close to 0 indicating a high level of similarity between the two communities and values close to 1 indicating a low level of similarityFull size imageDiscussionThis study provides novel, baseline data on the diversity of benthic communities within MCEs in the central (Egmont Atoll, Chagos Archipelago).

Output: pass


Input: Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: In sum, the prominent effects of visual learning on responses of excitatory neurons are (i) reduction in average response, (ii) increase in maximum response, and (iii) stronger oscillations after learning. In the following, we show how such changes guide us to reveal a mechanism underlying visual learning that sharpens stimulus selectivity and temporal resolution of stimuli. Note that we focus on excitatory neurons only assuming that the dynamics of inhibitory neurons follow that of mean excitatory neurons, and do not contribute qualitative changes of response dynamics after learning. Such a simplification is based on the experimental observation that input changes and the magnitude of rebound activity depend weakly on the post-synaptic firing rates in inhibitory neurons (for further justification). Recurrent synaptic plasticity alone cannot reproduce the response dynamics Activity-dependent modifications of synaptic connections can be one of the key elements to explain changes in network patterns and response dynamics with learning. Previously, we introduced a procedure to infer synaptic plasticity rules from experimental data so that networks implementing the derived learning rules can quantitatively reproduce changes in the distribution of time-averaged visual responses observed experimentally (Lim et al. , 2015). We now extend this framework and explore whether synaptic plasticity alone would be sufficient to explain stronger oscillatory responses after learning. To investigate the effect of learning on response dynamics, we considered a firing rate model with a plasticity rule that modifies the strength of recurrent synapses as a function of the firing rates of pre- and postsynaptic neurons. Activities of neurons are described by their firing rates ri for i = 1,…, N, where N denotes the number of neurons in the network. Their dynamics are described by the following equations (1) τrdridt=−ri+Φ(∑WijRrj+∑WijFIjX) where Φ is a static transfer function (f-I curve), and the total input current is the sum of the recurrent input ∑WijRrj and the feedforward input ∑WijFIjX. Wijk denotes the strength of synaptic connection from neuron j to neuron i with k = R or F representing recurrent and feedforward connections, respectively. The superscript X denotes an external input, and IiX is the external input to neuron i before learning with WijF=δij.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: These were identified as study individuals MS24, MS24 and FS7. There is a significant correlation between carnivore species recorded by both methods (Rs = 0. 81, n = 6, p < 0. 05;Fig. 4) indicating that an increase in activity is detected by both methods. Each species' mass and length (including tail where this is visible) were taken as the mean values for the male and female of each species (from Kingdon 1997 and Skinner and Chimimba 2005). The percentage difference in recording between camera traps and spoor was found to be nonsignificant, but there was a borderline trend with length (one-tailed Pearson r = -0. 724, n = 6, p = 0. 052; Fig. 5), suggesting that relatively short species such as the genet or Meilers mongoose may be more likely to be under-recorded than longer species like hyena and leopard. Inter-observer variation Of the 20 prints made, 19 were useable and in one print, measurement D was not possible due to damage during recordings. There was a highly significant difference between observers for each measurement (Table 3). The experienced researcher had a greatly reduced variation between measurements compared to the three novices. Intra-observer variation Hind right measurements were omitted from the analysis due to only one animal registering in substrate < 2 mm and two animals only registering in substrate > 2 mm. With the exception of hind pad width, there was a highly significant difference in all measurements between the animals and a significant difference was found between the two substrates for all measurements (Table 4) suggesting that substrate depth affects spoor size. The full spoor length was found to be the most distinct between each animal, supporting results of Sanei et al. (2011), followed by hind spoor length, with full spoor width only showing a difference in substrate > 2 mm (Table 5). ability to document the presence (and potentially abundance) of species of conservation concern is of great interest to field ecologists (Karanth and Nichols 1998, Trolle and Kery 2005, Jackson et al. 2006). Understanding the limitations of the main means of recording these species is therefore of considerable importance. In this study of mammalian carnivores inhabiting savannah habitat in South Africa, we found that camera traps significantly under-recorded the number of animals passing a trapping area when compared with those identified using spoor.

Output: contrast


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2023a), gorgonian and antipatharian (Bridge et al. 2012; Bo 2019; Rowley et al. 2019; Diaz et al. 2023a)) diversity in MCEs were shown to increase with depth. Similar trends in specific phylum change are presented in Table S2. Benthic diversity and community composition change along the depth gradient may be explained by several environmental factors, as observed in a parallel study by (Diaz, et al. 2023a), which identified fifteen environmental variables contributing significantly to the distribution and community composition of MCEs over the depth gradient. Decreasing photosynthetically active radiation (PAR) and temperature were identified as the two main drivers of variation in community composition with depth at (Diaz et al, 2023a). Similarly, a global study across a variety of taxa identified PAR and nutrient availability (both dissolved and particulate organic matter) to be correlated with a community break observed at 60 m (Lesser et al. 2019). In the Caribbean (i. e. Bermuda and Little Cayman), temperature and light were the main factors correlated with changes in overall benthic morphotype richness and scleractinian corals with depth, respectively (Stefanoudis et al. 2019; Carpenter et al. 2022). A decrease in benthic diversity with depth can therefore be explained by several factors, but mostly by the decrease in PAR, temperature, and nutrient availability. These factors are important drivers of primary production, reflected here in the distribution of photosynthetic corals and algae. Nevertheless, while morphospecies richness decreased with depth overall for the two study sites; for a standardised sample coverage (i. e. Cmax) and within the four shallowest depth zones (from 15 to 90 m), this decrease was not significant (Fig. 2d). However, Shannon and Simpson indices (i. e. , accounting for abundant and highly abundant morphospecies), revealed significant differences between depth zones with an overall decrease in diversity indices with depth (except between 80–90 m and 110–120 m for q = 1, Fig. 2e; and between 60–70 m and 110–120 m at IDR, 80–90 m and 110–120 m at MA for q = 2, Fig. 2f).

Output: pass


Input: Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: We assumed that the recurrent synapses are plastic, changing their strengths according to WijR→WijR+ΔWijR, which depends on the activities of both pre- and postsynaptic neurons during the stimulus presentation. We further assumed that the learning rule is a separable function of pre- and postsynaptic activity as (2) ΔWijR=1NfR(ξi)gR(ξj) where f and g are post- and pre-synaptic dependence of the learning rules, respectively, and ξi is the activity of neuron i averaged during the stimulus presentation before learning. Previously, we found that synaptic plasticity in recurrent excitatory connections is sufficient to reproduce changes in the distribution of time-averaged visual responses observed experimentally (Lim et al. , 2015). Hebbian-type synaptic plasticity with a potentiation in high firing rates leads to an increase of the maximal response of excitatory neurons, while overall depression leads to a decrease of the average network response of both excitatory and inhibitory neurons (Figure 2A). With such synaptic plasticity derived from the time-averaged activities, response dynamics in Equation (1) shows similar changes to the time-averaged responses (Figure 2B,C). However, the temporal profile is similar before and after learning and does not show oscillations after learning. Thus, synaptic plasticity alone is not sufficient for reproducing changes in response dynamics observed experimentally, which will be shown analytically in the next section. Figure 2 Download asset Open asset Networks with synaptic plasticity in recurrent connections without slow negative feedback. (A) Example post-synaptic dependence of recurrent synaptic plasticity inferred from changes of time-averaged responses. Dependence of synaptic plasticity on the post-synaptic rate, f in Equation (2), shows depression for low rates and potentiation at high rates. (B, C) Average (B) and maximal (C) response before (red) and after (blue) learning for the network with synaptic plasticity only in recurrent connections. Interactions between recurrent synaptic plasticity and slow negative feedback Another key ingredient to explain changes in response dynamics with learning can be slow negative feedback. In a dynamical system, resonance-like behavior emerges from the interaction between strong positive feedback and relatively slow negative feedback.

Output: corroborate


Input: Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: This under-recording ranged from 22% for brown hyena to 64% for civet and there is a suggestion that animal size affected the likelihood of being recorded. This study illustrates that spoor can provide us with an opportunity to calibrate camera traps. However, the ability to detect and/or identify spoor is affected by the tracker's expertise as well as ground characteristics (Alibhai et al. 2008, Lyra-Jorge et al. 2008) which do need to be taken into consideration, but could be controlled where necessary. We found that inter-observer variation in the simple measurements recorded in our study meant that spoor size data collected by amateur volunteers are unlikely to provide useful data beyond the presence/absence of leopards. However, experienced researchers were more consistent in measuring spoor, although spoor size was influenced by substrate, as well as other factors such as terrain and speed of the animal's movement (Riordan 1998, Liebenberg 2005, Sanei et al. 2011). We found that mean leopard spoor size was significantly larger in substrate with a depth of > 2 mm when compared with those recorded in < 2 mm, which may result from the greater displacement of the toes from the metatarsal pad as the foot pushes further into the substrate. Full spoor length was significantly different between animals, which was more apparent in substrates > 2 mm depth, although we advise that measurements taken directly in field are best done in < 2 mm sandy substrate as this reduces variance. This is in contrast to Lewison et al. (2001) and Sharma et al. (2005), who found thicker soil to be more beneficial for tracings and photographs, especially for digital measurements. Therefore, while simple spoor measurements collected directly in the field in sandy soil may not reliably allow the identification of individuals, simple spoor measurements of the leopard of interest can be taken, so that where the trail is broken or crossed by another individual, size measurements provide evidence that the focal leopard's trail has been relocated. Figure 4Open in figure viewerPowerof images of each study species recorded inside the camera trap area by spoor or camera trap images. Figure 5Open in figure viewerPowerbetween study species body length (cm) and likelihood of being under-recorded by camera trap (considering the percentage difference between spoor and image records) in this study.

Output: contrast