Genetic methods for the estimation of population size can be powerful alternatives to conventional methods. Close‐kin mark–recapture (CKMR) is based on the principles of conventional mark–recapture, but instead of being physically marked, individuals are marked through their close kin. The aim of this study was to evaluate the potential of CKMR for the estimation of spawner abundance in Atlantic salmon and how age, sex, spatial, and temporal sampling bias may affect CKMR estimates. Spawner abundance in a wild population was estimated from genetic samples of adults returning in 2018 and of their potential offspring collected in 2019. Adult samples were obtained in two ways. First, adults were sampled and released alive in the breeding habitat during spawning surveys. Second, genetic samples were collected from out‐migrating smolts PIT‐tagged in 2017 and registered when returning as adults in 2018. CKMR estimates based on adult samples collected during spawning surveys were somewhat higher than conventional counts. Uncertainty was small (CV < 0.15), due to the detection of a high number of parent–offspring pairs. Sampling of adults was age‐ and size‐biased and correction for those biases resulted in moderate changes in the CKMR estimate. Juvenile dispersal was limited, but spatially balanced sampling of adults rendered CKMR estimates robust to spatially biased sampling of juveniles. CKMR estimates based on returning PIT‐tagged adults were approximately twice as high as estimates based on samples collected during spawning surveys. We suggest that estimates based on PIT‐tagged fish reflect the total abundance of adults entering the river, while estimates based on samples collected during spawning surveys reflect the abundance of adults present in the breeding habitat at the time of spawning. Our study showed that CKMR can be used to estimate spawner abundance in Atlantic salmon, with a moderate sampling effort, but a carefully designed sampling regime is required. The aim of this study was to evaluate the potential of close‐kin mark–recapture (CKMR) for the estimation of spawner abundance in Atlantic salmon and how age, sex, spatial, and temporal sampling bias may affect CKMR estimates. Our study showed that CKMR can be used to estimate spawner abundance in Atlantic salmon, with a moderate sampling effort, but a carefully designed sampling regime is required. Instead, genotyped individuals “mark” their close‐kin, which may then be genetically identified (“recaptured”) in a second sample  are under severe pressure worldwide and reliable estimates of population size are crucial for their management. Anadromous Atlantic salmon migrate to the sea after a juvenile life‐stage in freshwater. After growth and maturation at sea, adults return to their natal river for spawning. This homing behavior leads to genetically distinct populations within and among rivers  in the 2018 spawning season by nonlethal sampling of adults and their potential offspring emerging the following year. The genetic data comprise (a) 54 smolt PIT‐tagged in 2017 and returning as adults in 2018, (b) 67 adults sampled during surveys of spawners in 2018 and (c) 278 juveniles sampled with electrofishing in 2019 and being potential offspring from the 2018 spawning. Samples are genotyped at a high number of neutral molecular markers (SNPs), and the number of parent–offspring pairs is inferred from parentage analysis. The frequency of parent–offspring pairs is used to estimate adult abundance. The Atlantic salmon population in the River Vigda is well studied and therefore suited for the validation of CKMR. Each year, spawner abundance is estimated by conventional surveys, which also provide life‐history data that are highly valuable for the application and validation of CKMR. This study explores how the precision of CKMR estimates is affected by the sampling design and by sampling biases for spatial distribution, size, age, and sex of the sampled fish. This is an important step in the development and validation of CKMR for Atlantic salmon, but also in developing similar methodology for other fish species in both river and lake ecosystems.2Survey samples hereafter). Second, genetic samples were collected during PIT‐tagging of smolt in 2017 and tagged individuals returning as adults in 2018 were registered with PIT antennas at the river entrance (PIT samples hereafter). Genetic samples of potential offspring were obtained by electrofishing of 0 + juveniles in 2019. CKMR estimates were compared with conventional counts of spawners.CKMR estimates of 2018 spawner abundance required genetic samples of adults returning to the river in autumn 2018 and of their potential offspring. Adult samples were obtained in two ways. First, scale samples were collected from adults caught during conventional spawning surveys  or Atlantic salmon and based on their size, Atlantic salmon were categorized as small (<3 kg), medium (3–7 kg), and large (>7 kg). When possible, also the sex was determined. Spawning surveys started approximately 500 m upstream of the river mouth and proceeded upstream to the end of the anadromous section . A team of three to five persons waded systematically through the riverbed at night‐time, using strong light to detect adult Atlantic salmon. Encountered fish were determined as either anadromous brown trout  while that was the case for only about a third of all fish encountered Table . Our dat kg while2.3Another sample of adult Atlantic salmon was obtained from a PIT‐tagging program in the River Vigda. Permanently installed antenna at the river entrance Figure  registerA total of 54 salmon PIT‐tagged as smolt in 2017 were registered at the entrance of the River Vigda in autumn 2018 and successfully genotyped. Genetic sexing revealed that this sample consisted of 20 males and 34 females. Many of the PIT‐tagged fish made repeated passages across the antennae in upstream and downstream direction, potentially moving between freshwater and sea (data not shown). Figure 2.4Juvenile samples were collected in autumn 2019 (10–11 October) by electrofishing . Samples were genotyped at a total of 192 SNPs using the EP1TM 96.96 Dynamic array IFCs platform (Fluidigm). The 192 SNPs included 15 mitochondrial and 177 nuclear SNPs, and out of those, 164 neutral loci were used for parentage analysis  was kept for parentage analysis, but the individuals were included in CKMR estimates for both survey samples and PIT samples.2.7mJ and mA are the number of juveniles and adults genotyped and P is the number of parent–offspring pairs detected. In this approach, each juvenile tags its two parents and the number of juveniles is therefore multiplied by two. The number of parent–offspring pairs represents the recaptures. Multiple juveniles may share parents, and the number of parent–offspring pairs may therefore exceed the number of adults genotyped. CKMR estimates may alternatively be calculated based on the number of unique parents identified in sibship analysis. That approach is presented in the Appendix CKMR estimates for spawner abundance were calculated with an analogue of the Lincoln‐Petersen estimator, as described by Bravington, Grewe, et al. :(1)N^aduConfidence intervals for CKMR abundance estimates were calculated from the variance estimator for mark–recapture with sampling with replacement Ricker, :(2)V^=mAmfemale is the number of females genotyped and Pfemale is the number of dam‐offspring pairs detected. Because each juvenile tags a single dam, the number of juveniles is here not multiplied by two. Male spawner abundance was calculated analogously. The variance estimator for female and male spawner abundance was accordingly calculated as:We also estimated spawner abundance for males and females separately. While total spawner abundance may be affected by sex‐biased sampling of adults, male and female abundance is unaffected by sex‐biased sampling. Female spawner abundance was calculated as:In this CKMR approach, variation in the number of offspring among adults does not affect the abundance estimate  and time (river entry vs. time of breeding). Abundance estimates based on PIT samples reflect the total number of adults entering the river, while estimates based on survey samples reflect the number of adults alive and present in the breeding habitat at the time survey samples were carried out. A difference between the two estimates may for example result from mortality between river entry and the time of spawning.2.8Sampling of adults was biased with regard to size and age and did therefore violate the requirement of sampling adults independently from their reproductive success . We therefore pooled medium and large fish for this analysis. For adults registered by PIT‐tags, body size was unknown and therefore inferred from sea age. Among individuals sampled in spawning surveys, all 1SW fish were of the smallest size class and all adults registered by PIT‐tags were therefore assigned to the smallest size class.nS is the number of small fish in the adult sample and nML is the number of medium and large fish in the adult sample. RS and RML are the average number of offspring for adults of the respective size classes and R¯ is the average number of offspring in the population:nS,population and nML,population are the numbers of small (S) and medium and large fish (ML) fish in the population. Numbers of small and medium and large fish in the population  were obtained in spawning surveys, during which the size of all encountered fish  was recorded.Adult abundance was estimated by CKMR adjusted for size‐biased sampling as:2.9Juvenile Atlantic salmon have a limited dispersal during the first year after hatching and are not expected to be randomly distributed in the river with regard to the place of hatching at the age of 0+  of mature male parr was estimated as:33.1A total of 278 out of 292 juvenile salmon (95%) and all adult samples were successfully genotyped at a minimum of 80% of the 164 loci and were included in further analysis. The average number of successfully genotyped loci among included samples was 162.N = 31,339).In total, 75 out of 278 juveniles were assigned to sampled parents and 37 out of 113 adults were detected as parents  than when calculated for survey samples (adults caught during spawning surveys) Figure . For surz‐test: χ2 = 7.3, p =.007). For adults assigned parentage, there was no difference in the number of offspring assigned to each parent among the two types of samples (Table The difference in CKMR abundance estimates resulted from a twice as large proportion of adults assigned parentage for survey samples 43%) than for PIT samples (19%) . This generated an underestimation of parent–offspring pairs, because 1 SW fish had on average fewer offspring assigned than fish that had spent two and more winters at sea Table . We usedThe corrected abundance estimate was 57% higher than the abundance estimate based on survey samples after correction for size‐biased sampling Figure , suggest3.4In sampling of adults during spawning surveys, there was a bias toward larger fish. Larger salmon ≥3 kg) made up about half of the sampled individuals, but only about a third of all individuals encountered during spawning surveys Table . At the  kg made 3.5Our data did not allow to correct for a potential effect of sex‐biased sampling on CKMR estimates, because we did not have reliable estimates for the population sex ratio or the size distribution within sexes (see Section 2.2). Abundance estimates by CKMR are however unaffected by sex‐biased sampling when calculated for males and females separately. For males, 24 parent–offspring pairs translated into an abundance estimate of 278 males . For females, 36 parent–offspring pairs translated into an abundance estimate of 324 females . The sum of abundance estimates for males and females  was similar (approximately 3% lower) to the total CKMR abundance estimate , suggesting that a potential sex bias in survey samples did not strongly affect abundance estimates.3.6The collected data were used to estimate the total reproductive contribution of anadromous males and mature male parr respectively. There was a pronounced sex bias among the anadromous salmon sampled during spawning surveys Table . Under tAssuming that 33% of the juveniles were fathered by mature male parr, the number of juveniles marking sires can be corrected to estimate the number of anadromous males only. Out of 278 juveniles genotyped, 185 (67%) would have been sired by and thus marked anadromous males. With 24 anadromous males genotyped and 24 parent–offspring pairs detected  resulted in considerably higher abundance estimates than sampling in the breeding habitat (survey samples), and this may correctly reflect different adult abundances at the respective time and place. Sampling biases were evident in both types of adult samples, and their correction resulted in moderate changes in CKMR abundance estimates.4.1Abundance estimates from CKMR included mature male parr, which explains why the abundance estimate from CKMR was considerably higher than that from conventional spawning surveys. In addition, conditions for spawning surveys were relatively poor in 2018 and spawner abundance estimated was lower than in previous years, when estimates ranged from 500 individuals in 2016 . The low level of uncertainty was achieved by a sampling regime that resulted in the detection of 60 parent–offspring pairs. The number of parent–offspring pairs determines to a large degree the uncertainty associated with CKMR abundance estimates  salmon entering the river in the course of the entire breeding season. CKMR estimates based on PIT‐tagged salmon are therefore expected to estimate escapement, that is, the total number of adults returning to the river. Samples collected in spawning surveys were in contrast taken in the spawning habitat and in mid‐October, when spawning was ongoing. We have no indication that sampling during the spawning season negatively affected spawning success. Notably, any negative effect of sampling during spawning surveys would have reduced the difference in abundance estimates observed between the two adult sampling methods. CKMR estimates based on those samples are therefore expected to estimate the number of adults present in the breeding habitat during spawning and were also closer to the conventional estimates. A reduction from the first to the second abundance estimate may be explained by mortality taking place in the river and before spawning. The 2018 spawning season was exceptional in most Norwegian salmon rivers, with very little precipitation and with low water levels until late in the season. Very few salmon  were caught by angling in the River Vigda in 2018  and of total population abundance. CKMR may be used to estimate the proportion of offspring fathered by mature male parr rather than anadromous males. In our study, we estimated this proportion to be 33%, but the estimate may have been strongly affected by sex‐biased sampling. If this proportion is estimated with greater certainty, it can be used to correct the CKMR abundance estimate for anadromous males and for the total abundance of anadromous adults. If in addition mature male parr were sampled, their abundance may be estimated with CKMR.4.5Our study showed that CKMR can be used to estimate spawner abundance in Atlantic salmon. A moderate sampling regime involving approximately 350 genotypes was sufficient to estimate abundance in a population of about 700 spawners. CKMR has earlier also successfully been applied to coho salmon  method is a useful tool for estimating the abundance of Atlantic salmon. It is important to avoid, or to correct for sampling bias and most importantly to design the sampling of potential parents in accordance with what they represent; escapement or number of spawners.None declared.Sebastian Wacker: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Writing‐original draft (lead). Hans J. Skaug: Conceptualization ; Methodology ; Writing‐review & editing . Torbjørn Forseth: Conceptualization ; Funding acquisition ; Writing‐review & editing . Øyvind Solem: Data curation ; Investigation ; Writing‐review & editing (supporting). Eva M. Ulvan: Data curation ; Investigation ; Writing‐review & editing . Peder Fiske: Data curation ; Writing‐review & editing . Sten Karlsson: Conceptualization ; Data curation ; Funding acquisition ; Investigation ; Methodology ; Writing‐review & editing .


More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision‐makers keep pace with a rapidly changing world.As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks  for determining when learning is occurring through the cycle of monitoring, modeling, and decision‐making steps in ARM. Many applications of ARM use multiple model‐based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed “ecological null models,” which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence ( Ecological null models based on sound logic can be used as benchmarks for learning in ARM that trigger the need for new model parameterizations when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help guide decision making in a rapidly changing world. Though commonly used statistical models provide predicted fits to data , these within‐sample fits primarily serve to explain mechanisms that may have given rise to the observed data. Explanatory inference provides a means for scientific learning, but the accumulation of evidence for hypotheses and theories across stand‐alone studies can be rather slow  in the North American midcontinent.In Section 2A fundamental principle of ARM involves learning about the responses of ecological systems to management actions, and then using that knowledge to inform future decisions. As a consequence, the practice of ARM should reduce uncertainty over time, often in the form of iterated, structured decision making that is connected to model‐based representations of ecological processes  and are designed to evaluate alternative mechanisms of interest while controlling for some variables that are known to affect the system  serve as pattern‐based null models for gauging the improved accuracy of near‐term mechanistic weather forecasts Silver, . Mechani44.1Anas platyrhynchos; Johnson et al., Arising out of decades of contentious stakeholder disagreements about the impact of harvest mortality on waterfowl populations, adaptive harvest management (hereafter AHM) was adopted in 1995 to scientifically guide the process of setting harvest regulations for mallards in the North American midcontinent (Sa) versus compensatory (Sc) mortality hypotheses  versus weak (Rw) density dependence, wherein the functions are based on analyses of historical data collected prior to 1995 . As such, the AHM models should be expected to provide a range of under‐ and overprediction in any given year, with the goal of basing decisions more heavily on those models that consistently predict better than others over time. In 1995, initial weights of 0.25 were assigned to each model . As new monitoring data become available, combinations of the survival and reproduction submodels are implemented into a balance equation to forecast population abundance for the following spring (see Table SaRw model that has performed best at 1‐year‐ahead forecasting (72% of weight), ScRw still receives some support (28% of weight), and both SaRs and ScRs received near zero support in 2019 . This would effectively cancel out the vital rate mechanisms in the AHM balance equation . Though system persistence is rarely considered as a benchmark for evidence and learning in ARM, an interim strategy for the AHM of mourning doves did consider its utility . This simple null model of population persistence captures the intuition of a naturalist that waterfowl populations do not radically change from one year to the next . Alternatively, one might consider an ecological null model that adds an effect of wetland abundance at time t (tW) on the population growth rate from t to t + 1, sans the vital rate mechanisms, since it has long been known that duck populations ebb and flow with wetland availability , εt ~ Norm, and the parameters  are estimated recursively with data up to time t.It is always healthy to question whether a model is adequately specified Box, . A simply Lynch, :Nt+1=λtt + 1 associated with each of the four AHM models to eventually observed abundances at t + 1 for midcontinent mallards between 1996 and 2019. We performed the same out‐of‐sample predictions for a weighted average of the four AHM models, and for our two ecological null models  and learning by doing. Given that the AHM models consistently underpredict observed abundance and cannot surpass the NMSDs of ecological null models, however, there seems to be ample room for improvement as a means to guide science‐based decision making in a changing world. Though systematic biases in the AHM models have been acknowledged  consider agreed‐upon ecological null models as benchmarks for evaluation of learning in their applications of ARM, (b) iteratively track improvements in the predictive skill of ARM models over time ; Formal analysis (lead); Investigation ; Methodology ; Validation ; Visualization ; Writing – original draft (lead); Writing – review & editing . Thomas Riecke: Conceptualization ; Formal analysis ; Investigation ; Methodology ; Validation ; Visualization ; Writing – review & editing . G. Scott Boomer: Conceptualization ; Data curation (lead); Formal analysis ; Investigation ; Methodology ; Validation ; Writing – review & editing . Ben S Sedinger: Conceptualization (supporting); Investigation (supporting); Writing – review & editing . James S. Sedinger: Conceptualization (supporting); Investigation (supporting); Writing – review & editing . Perry J. Williams: Conceptualization (supporting); Investigation (supporting); Writing – review & editing . Todd W Arnold: Conceptualization (lead); Formal analysis ; Investigation ; Methodology ; Supervision (lead); Validation ; Visualization ; Writing – original draft .Supplementary MaterialClick here for additional data file.Appendix S1Click here for additional data file.


Developing odontoma is a rare mixed odontogenic tumor that can arise with other odontogenic lesions. The association of COC with ameloblastic fibro‐odontoma is extremely rare. We report an extremely rare case of developing odontoma arising from a calcifying odontogenic cyst in a 17‐year‐old girl. The importance of accurate diagnosis that influence the treatment plan. In this case with accurate diagnosis, conservative treatment was substituted with marginal resection. It has a highly diverse histopathologic characteristic.The combination of COC and ameloblastic fibro‐odontoma has been reported before. However, this combination is extremely rare. This paper reports the first case of developing odontoma as a new entity arising from calcifying odontogenic cyst.2A 17‐year‐old female patient was referred to the Oral and Maxillofacial Pathology Department, Tehran University of Medical Sciences, Tehran, Iran, for consultation and diagnosis of a lesion on the right side of her mandible. The previous histopathologic diagnosis of the lesion was ameloblastoma. Before the incisional biopsy, the patient's chief complaint was a swelling in the right posterior mandible for 2 weeks. There was no facial asymmetry. In the intraoral examination, a slight swelling was observed in the right retromolar area. The swelling was non‐tender and was firm in consistency. Panoramic and CBCT radiographs showed a well‐defined radiolucent lesion that contained areas of radiopacity around the crown of an unerupted third molar. The margins of the lesion were sclerotic  occur concurrently with other odontogenic lesions and the most common concurrent lesion seen in this association is odontoma.Developing odontoma was formerly called AFO until 2017 when WHO changed its name.Developing odontoma is a rare odontogenic tumor. It shows proliferation of odontogenic epithelium and primitive ectomesenchymal tissue in association with tooth structure.Calcifying odontogenic cyst is a rare odontogenic lesion presented as a painless slow‐growing lesion with predilection for the anterior region of the jaws. It affects both maxilla and mandible equally.Calcifying odontogenic cyst may occur in association with an impacted tooth. The distinguishing histopathologic feature of COC is the presence of ghost cells, which may calcify, in an ameloblast‐like epithelium.The first case of calcifying odontogenic cyst with ameloblastic fibro‐odontoma was reported by Farman, et al in 1978.Matsuzaka, et al reported a case of ameloblastic fibro‐odontoma arising from a calcifying odontogenic cyst in 2001. The patient was a 23‐year‐old male with the chief complaint of painful swelling on the left mandibular molar region. It was a multilocular mixed lesion in the panoramic radiograph. Tooth impaction was also evident.Lee et al., in 2014 reported calcifying odontogenic cyst associated with ameloblastic fibro‐odontoma of the anterior mandible in a 4‐year‐old girl. The chief complaint of the patient was swelling swollen jaw. It had caused tooth displacement. It was a unilocular mixed lesion that caused root resorption and cortical perforation. The lesion was also around an impacted tooth.Apart from these two combinations of COC with ameloblastic fibro‐odontoma, there were also two other case reports of COC with two other lesions forming a combination of three odontogenic lesions.On the contrary, Phillips also reported a hybrid odontogenic tumor in a 7‐year‐old boy which consisted of ameloblastic fibro‐odontoma, COC, and adenomatoid odontogenic tumor. The lesion was discovered on panoramic examination by his dentist as a radiolucent lesion around unerupted succedaneous teeth extending from the deciduous left second molar to the left lateral incisor.The mechanism that causes the occurrence of two odontogenic lesions together is not well known. Nevertheless, various theories have been proposed to explain the phenomenon including a transformation of one lesion into another, a collision of two separate lesions, and an inductive effect of one lesion on another.4We report an extremely rare case of developing odontoma arising from calcifying odontogenic cyst. This report highlights the importance of accurate diagnosis that influences the treatment plan.The authors declare no conflict of interest.AA gathered the data from the literature and wrote the first draft of the manuscript. MMKh supervised during the whole process and prepared the images for the manuscript. MMKh and NM performed pathologic diagnosis. NM approved the final manuscript.Written informed consent was obtained from the patient and her parents.Published with written consent of the patient.


Soil biota contribute to diverse soil ecosystem services such as greenhouse gas mitigation, carbon sequestration, pollutant degradation, plant disease suppression and nutrient acquisition for plant growth. Here, we provide detailed insight into different perturbation approaches to disentangle soil microbiome functions and to reveal the underlying mechanisms. By applying perturbation, one can generate compositional and functional shifts of complex microbial communities in a controlled way. Perturbations can reduce microbial diversity, diminish the abundance of specific microbial taxa and thereby disturb the interactions within the microbial consortia and with their eukaryotic hosts. Four different microbiome perturbation approaches, namely selective heat, specific biocides, dilution‐to‐extinction and genome editing are the focus of this mini‐review. We also discuss the potential of perturbation approaches to reveal the tipping point at which specific soil functions are lost and to link this change to key microbial taxa involved in specific microbiome‐associated phenotypes. According to the BQH, some microorganisms called beneficiaries ‘avoid’ having a function in order to optimize their adaptation to the environment. This loss of function is possible because other ‘helper’ microorganisms in their direct vicinity provide for this function, thereby offering a stable environment. Hence, soil bacteria can be metabolically dependent on other microbes in their direct surroundings. This may also explain, at least in part, that a considerable fraction of the soil microbial community is not (yet) culturable. Considering that the majority of soil microbial species cannot be cultured under laboratory conditions, our knowledge of the taxonomic and functional diversity of soil and plant‐associated microbial communities is primarily based on next generation sequencing, in particular taxonomy‐based amplicon  analyses and function‐based marker gene or shotgun metagenome analyses . Perturbation methods are typically culture‐independent and therefore the preferred choice for soil microbiome analyses where the majority of taxa is yet unculturable. Here, we present an overview of three different microbiome perturbation approaches, namely selective heat, selective biocides and dilution‐to‐extinction see Fig. .th century Louis Pasteur discovered that heating wine and milk to a temperature between 60° and 100°C prevents it from spoiling. Pasteur used the heat treatment, later termed pasteurization, to prove the germ theory of disease  is a tipping point for microorganisms better adapted to higher temperatures and with fast growth rates organisms in human and animal health as well as agriculture are collectively referred to as biocides and include pesticides  and antibiotics. The vast majority of studies on biocides concerns the impact of antibiotics on the gut microbiome and human health  in diluted and undiluted treatments was minimal . 'Omics analyses around the tipping point between the suppressive and conducive soil status is ongoing to obtain more insight into shifts in microbiome composition and functions associated with this microbiome‐associated plant phenotype.To study microbiome‐mediated protection of plants from infection, Hol and colleagues  diluted et al., Pseudomonas, Paenibacillus) were required for effective degradation of the plant polymers. Kang and colleagues  can help to understand the forces that allow microorganisms to coexist and perform certain functions. Different perturbations allow to design experiments explicitly based on soil ecology and reveal which taxa co‐occur and interact and are responsible for certain soil functions. Furthermore, perturbation experiments are highly instrumental in revealing the tipping point see Fig.  at whichThe authors have no conflicts of interest to declare.


The complication of hepatocellular carcinoma (HCC) in Wilson's disease is rare. Wilson's disease treatment using D‐penicillamine (DPA) is useful to prevent HCC occurrence; however, it also causes iron accumulation and synergistic radical formation in the liver, which may enhance carcinogenesis. Reported herein is a case of HCC in Wilson's disease treated with DPA for 36 years. The tumor was surgically resected and histologically diagnosed with moderately differentiated HCC surrounded by cirrhotic tissue with fatty infiltration. Rhodanine staining revealed a slight positively stained area in both tumor and surrounding tissues. Information obtained from this case and literature review highlight the feature of HCC in Wilson's disease. A complication of the hepatocellular carcinoma (HCC) in Wilson's disease is rare. The treatment for Wilson's disease using D‐penicillamine is useful to prevent HCC occurrence; however, it also causes iron accumulation and synergistic radical formation in the liver which may enhance the carcinogenesis. We report a case of HCC in Wilson's disease treated with D‐penicillamine for 36 years. ATP7B, the responsible gene that is located on chromosome 13, has mutated, resulting in functional failure of ATP7B protein as copper‐transporting protein. This results in impaired copper transportation from the cytosol to the Golgi apparatus and excretion into bile.Wilson's disease is an autosomal recessive genetic disorder, and A 62‐year‐old female who was diagnosed with Wilson's disease with Kayser–Fleischer ring Fig.  and neur