Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF‐predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation. For example, in order for assisted gene flow to contribute to robust populations of harvested forests   analyses  remain unanswered, including how it is affected by neutral demography. For example, changes in allele frequencies could reflect simple genetic drift rather than adaptive signals  in allele frequencies across a gradient, GF Offset will always be positive. The underlying assumption being that a population already occupies its adaptive optimum when sampled, and therefore any subsequent change in allele frequency composition will reduce its fitness. However, because fitness could Variation in population size (N). What effect do neutral processes, operating on a cline in population size across an environmental gradient, have on GF Offset when only neutral loci are considered? We tested the hypothesis that a decrease in population size would result in an increased GF Offset at neutral loci due to increased genetic drift operating in small populations.(Q1) Relationship between GF Offset and fitness offset. Given equal deme sizes in a metapopulation, how well does GF Offset predict changes in fitness when populations experience an immediate environmental change ? We tested the prediction that GF Offset is inversely related to fitness by conducting in silico common garden experiments under monogenic and polygenic architectures.(Q2) GF Offset versus other measures of offset. Given equal deme sizes in a metapopulation, how does GF Offset perform relative to environmental distance or STF? We tested the hypothesis that GF Offset outperforms both environmental distance and genetic distance because GF appropriately weights and scales the environmental gradients to reflect their genetic importance.(Q3) To evaluate GF Offset as a potential measure of fitness offset, we asked the following questions:We tested the performance of GF Offset and other offset measures in their ability to predict fitness of genotypes when transplanted to common gardens  across the species range in silico. Using SLiM  different sampling schemes of a steep allele frequency cline along one or multiple environmental variables, (2) different slopes of allele frequency clines along an environmental variable, (3) different nonmonotonic relationships between allele frequency and an environmental variable, (4) a comparison of linear and nonmonotonic allele frequency relationships with an environmental variable, and (5) a comparison of a linear allele frequency relationship with an environmental variable and the same relationship with noise added by sampling from a normal distribution with 0 mean and variance of 0.1.2.2N) were split across a metapopulation consisting of 100 demes arranged in a 10×10 connectivity matrix  was assigned at least one environmental value that could vary over generational time . When two environmental variables were considered, they are referred to as Environment 1 and Environment 2 . Symmetric migration was simulated between adjacent demes at a per‐generation migration rate (m), and each deme contained equal proportions of males and females, with bi‐parental mating producing the subsequent generation.We generated simulations using SLiM v3.4. Ten‐thousand individuals  was 10−7 , and a base recombination rate (r) of 10−5 (T * rN = 0.1) was used to approximate a distance of 50 cM per linkage group. The population‐scaled mutation and recombination rates were chosen to approximate the resolution that would be observed from sampling SNPs from a larger genome, that is, allowing SNPs 50K bases away to be unlinked, while allowing for signatures of linkage to arise within each linkage group , and neutral variants were then overlaid on that recapitated file ; increasing dN; and decreasing Nd . Each scenario was replicated 10 times.To test the hypothesis that GF Offset at neutral loci could be influenced by variation in genetic drift, we simulated a linear environmental gradient with values from −1 to 1, in three neutral scenarios: equal deme size , then the correlation between dN and GF Offset will be negative.GF analysis, implemented in R . These simulations evolved a simple two‐dimensional isolation‐by‐distance population structure. We evaluated the relationship between GF Offset and mean deme fitness, after individuals from that deme were transplanted into another environment in the simulation, for both monogenic and polygenic scenarios. We evaluated offset measures under two genetic architectures: a single‐locus population genetic model, and a multilocus two‐trait quantitative genetic model. In both models, a migration rate of ωS) and mean allopatric fitness (ωA)  considered to be antagonistically pleiotropic . See Appendix In our “population genetic” model of environmental adaptation, a single allele had a linear relationship with fitness across an environmental cline. To avoid a scenario where maladapted individuals persist at the range edge, we modeled individual fitness for each genotype as a function of the environment, with the ancestral allele  with no dominance. When an ancestral allele mutated, the bivariate effect size of the derived allele was drawn from a multivariate normal distribution with a standard deviation of σQTN = 0.1 (without covariance) for both traits, which gives flexibility for mutations to evolve with effects on one or both traits . For each deme, the phenotypic optimum simply equaled the environmental value.In our “quantitative genetic” two‐trait, two‐environment model of environmental adaptation, QTNs were allowed to arise at a rate of i in deme j (ijω) was based on how far their P1,ij and P2,ij phenotypes fell from the bivariate optimum in that patch (Θ1jt and Θ2jt) using a multivariate normal distribution with standard deviation kσ to represent the strength of stabilizing selection in each deme:The relative fitness of individual ωmax, where ijP = Θjt. We ran a cumulative burn‐in period of 3000 generations, consisting of a 1000 generation homogeneous initial burn‐in, followed by a 1000 generation transition burn‐in  in a transplant “common garden” location j  was calculated for each deme across all contemporary environments in the simulation, resulting in a 100 × 100 matrix of pairwise relative fitness comparisons. For each common garden j, we could then assess how well an offset measure between i and j predicted the relative fitness of individuals from i when transplanted to j. We refer to this relative fitness measure as CG Fitness (common garden fitness).In order to represent the relative fitness effects of an instantaneous environmental change, we implemented a reciprocal transplant fitness assessment in our single‐ and multilocus simulations. The mean relative fitness of individuals from a home location For each common garden, the performance of each offset measure was evaluated as the correlation between the offset measure and CG fitness, with a higher correlation inferring that the given offset measure was better able to predict the relative fitness of a genotype when transplanted to the common garden environment. To capture limitations affecting most empirical studies, we used a subset of demes in the reciprocal transplant that was used to calculate the correlation for each common garden  environmental distance.In order to assess the performance of GF Offset in predicting CG Fitness relative to other distance metrics, we also calculated offset measures based on pairwise (i) ST offset(i) F. FST offset for a set of individuals from a deme was based on the pairwise FST between the source deme and transplant deme. Pairwise Weir–Cockerham FST values were calculated for every combination of demes at the end of each simulation after filtering loci for MAF > 0.01: one set of such values using all loci (FST Genome), another set using only the QTN loci .(ii) Environmental offset. Environmental offset for a set of individuals from a deme was based on the pairwise environmental distance between the source location and the transplant location. Environmental distances were calculated using both n‐dimensional Euclidean , and using just the causal environments . Cauchy,  and Maha(iii) GF offset. GF outputs an individual cumulative importance curve for each locus considered to have an environmental association and a weighted aggregate function across all such loci, for each important environmental variable. GF Offset is defined as the Euclidean distance between two locations A and B in the rescaled environmental space obtained by applying the fitted GF model to the environmental predictors. Note that the rescaling and calculation of GF Offset can be performed using the individual cumulative importance curves  we calculated GF Offset based on (i) all loci and all environments , (ii) all loci and causal environments , (iii) QTN loci and all environments , and (iv) QTN loci and causal environments .r et al. ), but mo33.1To explore the behavior of GF, we created different relationships between genetic variation and an environmental cline and used these to evaluate the relationships between the rate of allele frequency turnover and total amount of cumulative importance, as well as the shape of the cumulative importance curve. In our study, GF produced similar nonlinear cumulative importance curves for linear clines, in which the rate of turnover was highest near the middle of the cline and low elsewhere, regardless of the slope between allele frequency and the environment , where ijα was the effect of locus i on trait j, and ip was the frequency of locus i in the metapopulation postsampling), indicating that a large number of QTNs were rare alleles that individually contributed little to the total additive genetic variance.All simulated cases produced high degrees of local adaptation Figure , with CaWe evaluated if GF could identify the causal environments when all the simulated environments were input. In the cases with two linear causal environmental clines , GF was able to identify the causal environment driving adaptation Figure . HoweverFST Genome, and FST Causal  than any other method. While FST Causal was generally a better predictor of CG Fitness overall for the Edge demes, FST Genome performed similarly in the Core demes.Measures of 4Potential applications for GF Offset range from small‐scale conservation efforts in genetic rescue , GF Offset, which is based on weighting of environmental gradients given the strength of their association with adaptive variation, outperformed predictions of changes in relative fitness from the unweighted distance metrics. However, when environmental drivers of adaptation were known and only those gradients were included in the offset calculation, environmental distance performed as well as, or better, than GF Offset. Note that the demography in those simulations was simple in the sense of equal deme sizes and migration rates. We also found that neutral demography can confound GF Offset, and that GF Offset can be sensitive to sampling schemes. Additional research is needed to inform how to account for population structure and apply appropriate filtration thresholds prior to calculations of offset measures, especially if they are to be applied to real‐world applications.Whether the intent is to predict how a population will perform when moved to another location or when faced with environmental changes in its current location, GF Offset is useful only to the extent that it approximates associated reductions in fitness . Across all simulated cases with selection, GF Offset performed well at predicting CG Fitness, regardless of whether or not nonadapted loci and noncausal environments were included in the analysis. However, it is important to note that we used GF offset to predict relative fitness in our simulations, as absolute fitness was not simulated. GF Offset was representative of changes in 4.1FST, regardless of whether FST was calculated using only adapted loci or not. This has significant implications for the use of FST as a decision metric for prioritizing conservation efforts, which other evaluations have also shown has limitations  result in decreases in fitness. Furthermore, GF Offset assumes that the molecular signatures of local adaptation when multiple demes occupy the same environment at different sites are the same. These assumptions are likely met when there is high stability of both the adaptive landscape and the genomic architecture maintaining fitness. On the other hand, these assumptions are likely to be violated with shifts in the adaptive landscape potentially driven by fluctuations in climate and ecology . This distinction between absolute and relative fitness is critical when using models to inform conservation management decisions, since changes in allele frequencies (due to genetic drift or differences in relative fitness) do not necessarily impact demography as absolute fitness does. In fact, allele frequency changes can FST values , with corresponding data which are unlikely to be reflected in empirical work. All calculations using causal alleles were not dependent on those alleles being identified as outliers – it was simply assumed that they were known. In reality, even though many causal alleles showed relatively elevated s Figure , most wo5While most emergent complexities involved in applying GF Offset to realistic scenarios are still poorly understood, there is still promise in the application of this method to identifying key environmental drivers of local adaptation and for estimating fitness declines in response to rapid environmental change. This may be especially applicable to translocation assessments of at‐risk species or cultivars, and genetic rescue efforts. Key considerations of demography, genomic architecture, and the nature of environmental gradients have been highlighted here, as in earlier work (Capblancq et al., The authors have no conflict of interests to declare.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


It has become known as an imaging marker of “neuroinflammation” indicating active disease, and is best interpreted as a nondiagnostic biomarker and disease staging tool that refers to histopathology rather than disease etiology. The therapeutic potential of TSPO as a drug target is mostly based on the understanding that it is an outer mitochondrial membrane protein required for the translocation of cholesterol, which thus regulates the rate of steroid synthesis. This pivotal role together with the evolutionary conservation of TSPO has underpinned the belief that any loss or mutation of TSPO should be associated with significant physiological deficits or be outright incompatible with life. However, against prediction, full Tspo knockout mice are viable and across their lifespan do not show the phenotype expected if cholesterol transport and steroid synthesis were significantly impaired. Thus, the “translocation” function of TSPO remains to be better substantiated. Here, we discuss the literature before and after the introduction of the new nomenclature for TSPO and review some of the newer findings. In light of the controversy surrounding the function of TSPO, we emphasize the continued importance of identifying compounds with confirmed selectivity and suggest that TSPO expression is analyzed within specific disease contexts rather than merely equated with the reified concept of “neuroinflammation.”The 18  This is in direct contrast to neuroreceptors which are usually downregulated, their loss often correlating with neurological or cognitive deficits. However, increased regional TSPO expression in the brain typically covaries with disease state and activity rather than clinical deficit and, therefore, does not map easily onto established disease classifications. In this sense, TSPO is a nondiagnostic biomarker that is best interpreted in the context of histopathology and only secondary to disease etiology. A substantial number of clinical trials are now being conducted, the majority of which are using The lexical change Table  from theTspo gene does not result in the phenotype expected if steroid synthesis was indeed altered Tspo knockouts, to how far it is possible to extrapolate to the protein's function in the context of a normal organism, it suggests that the “translocation” concept needs to be rethought. Like the “neuroinflammation” concept, the concept of “translocation” is critically dependent on the context within which it is applied.This review, however, is written at a time when new data have become available which show that knocking out the Here, we provide an overview of the publication trends before and after the introduction of the new nomenclature for TSPO, the efforts to develop new TSPO ligands as well as some of the newer findings. This review emphasizes that the continued development of new or the reassessment of existing molecules, with confirmed selectivity for TSPO, will be crucial to the further development of the field at large.in vivo. These observations have led to a surge in the development of TSPO‐specific ligands appropriate for imaging and with potential for clinical applications. So far, none of these are in routine use in the clinic, but the number of clinical trials recently initiated (Table S1 in Supporting Information) indicates this may change in the years to come.The 18 kDa TSPO has been implicated in many important physiological functions, including cholesterol transport and steroidogenesis Tspo are, counter to expectation and prior indication in the literature, neither embryonic lethal nor obviously impaired in their production of steroids Although much has been learned about this protein's molecular, biochemical and pharmacological properties, its exact role under physiological and pathological conditions rests on a number of assumptions which have come to be doubted in the wake of the recent discovery that mice with tissue‐specific or global knockout of Given these recent developments in the field and the abundance of reviews on the protein, it is not our intention to discuss all of these proposed functions in detail; the reader is directed to other articles Literature relating to TSPO encompasses many research fields, highlighting a diverse range of reported functions and pathological involvement, and the inherent complexity this entails. A timeline of the early observations about TSPO prior to 2006 Ro5‐4864 and [11C]PK11195 have been used to measure, quantify and image increased TSPO receptor overexpression in neurodegenerative disorders in vitro affinity in human brain tissue in vivo pharmacokinetics as a PET radiotracer compared with [11C]Ro5‐4864, however, it too displayed poor signal to noise ratio, low blood–brain barrier permeability and high nonspecific binding However, initial PET imaging with [11C]Ro5‐4864 in patients with brain tumors did not show great promise because of large amounts of nonspecific binding and low in vivo imaging, accelerating the progression to in vivo human studies.A considerable number of diverse chemical entities with high specificity and selectivity for TSPO have been developed in the last 30 years in vivo using PET or SPECT. Furthermore, many of these radiotracers have demonstrated superior imaging capability in imaging inflammatory processes in both animal models of disease and in humans in comparison with Ro5‐4864 and PK11195. However, despite the considerable improvements exhibited by these TSPO radiotracers, most developed to date are less than ideal, primarily because of a significant component of nonspecific binding stemming from an unfavorable metabolism and nonspecific uptake of the radiolabeled fragments in regions of interest.The currently leading TSPO radiotracers include the N‐benzyl‐N‐(2‐phenoxyaryl)‐acetamide derivative [11C]‐DAA1106 In vivo studies of radiolabeled [123I]iodozolpidem, in contrast to [11C]zolpidem, showed that the modification resulted in a ligand with characteristics of a TSPO ligand Table S4 of Supporting Information).Modeling studies have suggested three common hydrophobic interactions with the tryptophan residues  and a polar hydrogen bond interaction as a requirement for affinity to TSPO in vivo animal studies in vitro ligand binding, autoradiography, RT‐qPCR  and immunohistochemical staining with in vivo small animal SPECT and PET imaging using our novel radiotracers [123I]CLINDE in vitro and in vivo ligand binding to TSPO as a measure of lesion activity in autoimmune CNS demyelinating diseases. Most importantly, TSPO imaging using these radiotracers enabled us to measure and quantify TSPO expression in activated microglia with excellent correlation to in vitro immunohistological staining and RT‐qPCR results in vivo properties In the last two decades, our group has developed several TSPO‐selective radioligands that could be used for PET or SPECT imaging; an extensive structure‐activity study of molecular entities spanning more than 10 different chemical families resulted in the synthesis and evaluation of over 250 novel molecules Radioligands are often metabolized by experimental subjects during imaging. The metabolism of the ligands is thus a parameter that must be considered for imaging quantification. Resulting radiometabolites that also have affinity to the receptor being imaged will distort the analysis, whereas high levels of radiometabolites that enter the region of interest will decrease the potential sensitivity of the radioligand. Strategic positioning of the radiolabel can moderate these troublesome radiometabolites; however, knowing the site of metabolic cleavage on the molecule is not always predictable and is further complicated by species differences In vitro metabolic studies of the TSPO ligands CLINDE, PBR111, PBR102 and the bioisosterically similar compound DPA‐714, using rat and human microsomes, have identified that the common main metabolic pathway in this group is via N‐dealkylation, O‐dealkylation and hydroxylation of aliphatics As previously mentioned, many of the TSPO ligands were derived from [11C]methylation of a nitrogen or an oxygen. Dealkylation of these methyl groups results in the formation of [11C]formaldehyde and [11C]carbon dioxide, which for brain studies, does not interfere as these radiometabolites do not cross the blood–brain barrier. In contrast, several of the [18F]fluorinated TSPO radioligands are [18F]‐O‐fluoroalkylated derivatives . O‐dealkylation can result in an [18F]fluorinated aldehydes which can subsequently be oxidized to their [18F]fluorinated acids. Further metabolism of these acids can result in [18F]fluoride which can accumulate in bone potentially complicating the imaging analysis and decreasing the ability of the radioligand to identify subtle changes to the receptor. However, the extent of metabolism is dependent on species To further refine our established PET radioligands and considering the metabolic data, our group synthesized a new [11C]imidazopyridine ligand, [11C]PBR170  Preliminary results show that [11C]PBR170 shows saturable, displaceable and specific binding in TSPO‐rich tissues. The kinetics and low nonspecific cerebral binding make this radiotracer suitable for imaging TSPO‐associated brain abnormalities Another complexity in assessing radioligand potential in humans is due to a genetic polymorphism of TSPO Imaging the subtle changes in TSPO expression by PET or SPECT requires the design of radioligands with exquisite sensitivity and specificity, free of deleterious signal contributed by spurious metabolites. Accordingly, attempts to image diseases using radiotracers that compromise this fine signal would be futile. With so many applications and a plethora of novel TSPO radiotracers available, it is an exciting time to extract the clinical value that TSPO has to offer. Of the radiotracers that have already progressed to human studies for neuroinflammation and neurodegenerative diseases A substantial part of the research relating to TSPO is focused on its diagnostic utility in neuroimaging. It is, therefore, possible to interpret the development of scientific knowledge around “TSPO and neuroimaging” so far as the appropriation of concepts from different fields. Starting with the rediscovery of microglia in the 1960s, the wider availability of immunohistochemical staining techniques for microglia in the early 1990s led to the gradual recognition that the brain was not an “immune‐privileged” organ and catalyzed the evolution of the “neuroinflammation” concept. Since then, the “neuroinflammation” concept has found steadily broadening applications, including to illnesses that traditionally have been accompanied by disagreement in regard to construct validity and nosology, such as schizophrenia Tspo knockout mouse should either be embryonic lethal, as in fact had been reported Tspo is an ancient, nonessential gene could be interpreted as support that its function is in fact an emergent property that necessarily requires the context of other pathways and protein–protein interactions.In the discussion around “neuroinflammation” and the “translocator protein,” one witnesses the role and impact of lexical changes in new scientific developments Tspo knockout animals with normal physiology across their lifespan Drug discovery marked the beginning of research into TSPO and its role in physiology. With the availability of well‐characterized Figure S1. (A) Publication of articles within search terms; refined by name used for TSPO. Evidence indicates Ro5‐4864 has been replaced by newer ligands targeting TSPO; however, PK11195 is still widely used as the prototypical TSPO ligand. In spite of the change in nomenclature, TSPO is still very commonly referred to in terms of its pharmacological properties . (B) Citation of articles within search terms each year; sorted by name used for TSPO. Articles using the term “peripheral benzodiazepine,” “PK11195/PK 11195” and “peripheral‐type benzodiazepine” receive considerably more citations than articles exclusively using the other search terms.Table S1. Clinical trials relating to neuroinflammation using TSPO ligands .Table S2. Terms for literature search.Table S3. TSPO‐associated proteins.Table S4. TSPO ligands.Click here for additional data file.


High human‐caused mortality due to wildlife‐vehicle‐collisions and illegal killing leads to frequent cases of orphaned Eurasian lynx juveniles. Under natural conditions, this would result in starvation of the young. To avoid this, wildlife managers conventionally rear animals in captivity and release them later. However, this measure is an undesirable outcome for species conservation, managers, and animals alike. Increased awareness of Eurasian lynx orphaned by human‐caused mortality means managers must often intervene in endangered populations. In this study, we report for the first time a successful case of in situ feeding designed to avoid captivity of two orphaned Eurasian lynx. We exposed 13 roe deer and 7 red deer carcasses in the field to successfully support two orphans to the age of independence and confirm dispersal from the natal range. We present this management approach as a feasible and complimentary tool that can be considered in small or isolated large carnivore populations where every individual counts toward population viability. The topic of orphaned juvenile Eurasian lynx is one of growing importance. Given the high rates of human‐caused mortality of Eurasian lynx, especially in Central Europe's anthropogenic landscapes, large carnivore managers must frequently deal with orphans. It is necessary to intervene in order to conserve individuals but the options available to managers besides conventional captive rearing have been relatively unexplored. We describe how two juvenile Eurasian lynx became orphaned and could be supported for 5 months to independence age via in situ provisioning of deer carcasses, thus avoiding human contact and captivity. We also confirmed dispersal from the natal range and the success of this method. Under pressure from growing traffic volumes, flushed out of their den refuges during wood extraction and due to human persecution, mothers are frequently taken from their litters. In the last decade at least 80 orphaned or displaced lynx have been reported, with a peak of 14 orphans occurring in 2016 .In recent years, the occurrence of orphaned Eurasian lynx Most lynx populations in Central Europe are the result of a political shift induced by the shifting attitudes of citizens ; however, there are unfenced major roads in the park's vicinity.The in situ feeding of two orphaned juveniles was conducted opportunistically following the death of their mother “B286.” B286 was a resident adult born in 2016 and part of the Bohemian‐Bavarian‐Austrian lynx population (BBA). The BBA follows the Czech‐German border from the Erzgebirge/Krušné hory in the north, down to the Waldviertel, Austria in the south. Following its reintroduction in the 1980s, the BBA expanded to fill its current range increasing from 17 released individuals to approximately 80 in 2014 , and 7 red deer (Cervus elaphus), for the orphans to feed upon  between 15/10/2018 and 13/02/2019, so‐called “targeted” carcasses (TC). The last “targeted” carcass was exposed 121 days after B286’s death. In addition to this opportunistic feeding, throughout the period an existing carrion ecology research project in the national park was underway , the first evidence of the two orphans was obtained on 18 October by a wildlife camera of another research project. The individuals could not be sexed but were individually identifiable as B300 (large‐spotted) and B301 (marbled) , wild boar (Sus scrofa), and common raven (Corvus corax). Over the respective monitoring periods, a maximum of 9 scavenger species were observed at one site, with a total of 11 species among all sites  or white‐tailed eagle , were only observed at sites where red deer carcasses were exposed. The specific consumption rates of scavengers were not measured, and carcass attenuation was only monitored insofar as to maintain carrion availability.Consumption was directly observed via camera trapping and/or assumed following inspection of the carcasses. The orphans typically remained at a carcass for several hours per visit and tended to feed in series. Taking evidence from the CES (114 photographs), the site with the most appropriate camera, the activity of the orphans was crepuscular with 76% of photographs around dusk/dawn and 24% at night. On all but 3 days camera traps could capture both individuals at the same site Figure  and A2. s Figure . Less frEvidence of B300 was collected by the Bavarian Environmental Agency (Bayerisches Landesamt für Umwelt) on 07/05/2019 in the region west of the national park Figure . On 06/042 area of their activity, dispersal from the natal range was apparent. We subsequently obtained evidence of the two orphans which was used to confirm survival to independence and success of the management action.Lynx in the reintroduced populations of Central Europe suffer high mortality due to traffic and illegal killing  translocated from the autochthonous Carpathian population. At this early stage, it appears the orphaned individuals are faring well having successfully hunted and reproduced  no risk of inadvertent habituation, (b) feeding with carrion simulates natural scavenging which is a normal dietary component ; Formal analysis (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Martin Gahbauer: Conceptualization (supporting); Investigation (lead); Writing‐review & editing . Franz Leibl: Supervision (supporting); Writing‐review & editing (supporting). Marco Heurich: Conceptualization (lead); Supervision (lead); Writing‐original draft (supporting); Writing‐review & editing .


Schoenoplectus americanus, an ecosystem engineer in coastal salt marshes in the Chesapeake Bay. We hypothesized that these two model structures align with an ecological understanding of dormancy and revival: zero‐inflation could arise due to failed germinations resulting from inviability or failed attempts to break dormancy, and overdispersion could arise by failing to measure important seed traits. A model that accounted for overdispersion, but not zero‐inflation, was the best fit to our data. Tetrazolium viability tests corroborated this result: most seeds that failed to germinate did so because they were inviable, not because experimental methods failed to break their dormancy. Seed viability declined exponentially with seed age and was mediated by seed provenance and experimental conditions. Our results provide a framework for accounting for and explaining variability when estimating propagule viability from soil‐stored natural archives which is a key aspect of using dormant propagules in eco‐evolutionary studies.There has been a steady rise in the use of dormant propagules to study biotic responses to environmental change over time. This is particularly important for organisms that strongly mediate ecosystem processes, as changes in their traits over time can provide a unique snapshot into the structure and function of ecosystems from decades to millennia in the past. Understanding sources of bias and variation is a challenge in the field of resurrection ecology, including those that arise because often‐used measurements like seed germination success are imperfect indicators of propagule viability. Using a Bayesian statistical framework, we evaluated sources of variability and tested for zero‐inflation and overdispersion in data from 13 germination trials of soil‐stored seeds of  Despite these findings, there remain outstanding questions about the fraction of seeds recovered from the S. americanus seed bank that failed to germinate. For example, it is unknown if the seeds that fail to germinate are inviable, if methods to break dormancy fail, and how seed age mediates these processes. Further, the extent to which traits related to seed dormancy are correlated with those of adult plants is unknown, and this is critical to the understanding of whether cohorts of resurrected propagules are representative of historical populations  of variability, and bias due to experimental conditions. Linking understanding of seed banking ecology with the design of statistical models can provide even greater insight. For example, bias in seed germination data can result because seeds can fail to germinate  as a result of inviability or because attempts at revival failed to break dormancy. Zero‐inflated statistical models, which are often used to account for zeros that arise from separate processes  has been the focus of foundational research on coastal marsh responses to global environmental change including elevated atmospheric CO2, nutrient loading, warming, and biological invasions  and sexual reproduction. S. americanus produces semispherical seeds with durable coats  in subsequent germination trials.We stratigraphically isolated seeds from the soil monoliths and cores. We first cut each core and monolith perpendicular to the vertical axis in 2 cm‐thick segments and then washed the segments over a 1 mm sieve. We visually identified and counted all 2.3To verify the age and sedimentary stratification of seeds, we dated three sediment cores collected from Kirkpatrick Marsh according to radioisotope activity . We reli2.4S. americanus seeds sieved from the soil monoliths and cores  and soil depth Table . For som2.5We fit four hierarchical Bayesian regression models, each with a different statistical likelihood function, to data on germination success for all experiments combined. Germination success was defined as the number of successful germinants out of the number of seeds planted and was predicted by the fixed effects of seed age, experimental temperature, experimental medium, experimental photoperiod, and whether or not seeds were pretreated before the germination trial. We also included a random intercept as a grouping variable for seed provenance (11 locations). Thus, we quantified the proportion of seeds that germinated within each unique combination of experimental conditions, mean seed age , and seed provenance. The proportion of seeds that germinate within a group of seeds of the same covariate values is synonymous with the probability that one seed within that group would germinate. We originally included a second random intercept to group observations by experimental assay, but this did not explain appreciable variation in germination success, so we removed the term from all subsequent models.210Pb activity and bulk density  are regression coefficients, and nϵ is residual error. Thus, the fixed effect of seed age for a group of seeds collected from the same depth from the marsh surface in the regression models is a random variable with a distribution  for groups of seeds that shared the same experimental treatment combination, seed provenance, and seed depth . We assessed whether the proportion of viable seeds that failed to germinate varied according to seed age using a binomial regression with seed depth as a fixed effect.We used data from two tetrazolium seed viability assays  and Model 4  Figure . AssessiR2 = 0.87) and Model 4 , while Models 1 and 2 overconfidently predicted germination probabilities  with our understanding of seed banking ecology. We proposed that variability in our germination trial data could be explained by two separate processes that result in seed germination failure and/or our failure to measure important characteristics of the seeds. We then proposed statistical models that would best capture these potential sources of variability in the data. While our germination trial data did exhibit a large number of zeros Figure —with 59.4.2S. americanus seeds can remain viable for more than a century . Germination probability declined from an average of 22% in recent sediments to 3% in century‐old sediments  of storage to 100% after 158 years (Cassia multijuga) of storage  to 0.9% (Oenothera biennis) and 0.18% (Verbascum blattaria) with ≥120‐year longevity (Kilivaan & Bandurski, S. americanus seeds is comparable or greater than declines estimated for other soil‐stored dormant propagules. For example, some Daphnia ephippia exhibit up to 75% revival over century‐long sedimentary records (Burge et al., Though s Figure . The estS. americanus (Summers et al., S. americanus seeds given the data from our germination trials.Testing the fit of a zero‐inflated model to our data allowed us to assess the hypothesis that decline in seed germination success with seed age could arise from two processes: increases in the likelihood of seeds being inviable with age or declines in our ability to break the dormancy of older seeds that have been buried in sediment for longer. Both processes could result from progressive deterioration of seeds due to microbial degradation or more prolonged exposure to unfavorable environmental conditions. It is also possible that a decline in germination might reflect temporal shifts in genetic variation, as has been observed in Empirical assays of seed viability support our model‐based inferences. Tetrazolium tests supported the inference that seed viability declines with seed age: there was no evidence to suggest that the decline in germination with seed age found in our trials was due to an increasing inability to break the dormancy of viable seeds Figure . This isWhile our results indicate that statistical separation of zero‐generating ecological processes was not imperative to understanding how seed germination success declines with seed age for our experiments, it is nonetheless important to recognize that different phenomena can influence seed germination success and viability and that there were indeed likely seeds that failed to germinate because they were inviable and those that failed to germinate because of our inability to break their dormancy. A zero‐inflated modeling framework might still prove useful for estimating viability for particular age cohorts or other hatching and germination experiments, which tend to generate data with zeros that can reflect different underlying processes. Within the statistical literature, zero values are referred to as true and false zeros (Hooten & Hefley, 4.3S. americanus (Marty & Kettenring, The results from our best fit model indicate that germination success varied by experimental conditions Figure , with thDaphnia ephippia vary according to provenance (Radzikowski et al., Daphnia ephippia can be depressed by long‐term exposure to heavy metals in sediments (Rogalski, We found that seed provenance accounted for a considerable amount of the observed variation in germination success Figure . This isS. americanus exhibits genetic differentiation within and among marshes (Blum et al., S. americanus. This hypothesis is supported by evidence that seed persistence can vary among populations (Kochanek et al., Daphnia ephippia vary by familial descent (De Meester & De Jager, Interestingly, the most extreme differences in germination success across locations were between Kirkpatrick Marsh and Corn Island, two geographically proximate sites at the Smithsonian Environmental Research Center in the Chesapeake Bay (Figure It is important to note that our inferences are constrained by the number of dated sediment cores used to inform our estimates of seed age, with all three dated cores collected from Kirkpatrick Marsh Figure , future 4.4Here, we identified and accounted for some biases and sources of variation that arise when using germination data of soil‐stored seeds to serve as a proxy for seed viability, including experimental conditions and distinguishing between viable and inviable seeds as a function of seed age. While we do not address all significant biases of using resurrected propagules in eco‐evolutionary studies (e.g., “the invisible fraction”; Weis, 5S. americanus can serve as a model for studying persistent soil‐stored seed banks and for using dormant propagules to infer evolutionary change of an ecosystem engineer over ecologically‐relevant timescales. Evidence that in situ viability of S. americanus seeds extends for a century or more helps lay the foundation for further inquiry about the ecophysiology, environmental conditions, and evolutionary drivers of aging, decay, and dormancy of soil‐stored seeds (Long et al., S. americanus plants revived from seeds does not decline with time since burial (Summers et al., Our findings build on prior work (Blum et al., S. americanus for time‐shift experiments (Blanquart & Gandon, ex situ seed archives (Etterson et al., Our work also offers some guidance for breaking dormancy to assemble depth/age cohorts of The authors declare no conflict of interest.Supplemental MaterialsClick here for additional data file.


Demonstrators spend significant time with students on a weekly basis in instructional laboratories and are well poised to offer students meaningful learning. Most often, effective demonstrator training is neglected due to time and resource restraints and it is clear more attention is needed. We hypothesized that students’ learning experience in laboratories would improve if demonstrators were well trained particularly across three overlapping learning domains: subject‐specific knowledge (cognitive and psychomotor), problem solving (cognitive) and group management including personalized student learning strategies (affective). We assessed both students and demonstrators on the impact of this extensive demonstrator training in 1st‐ and 2nd‐year bioscience practical courses over two years. The results show that all students rated the demonstrators’ performance higher after the extensive training. Students from both years valued the provision of problem‐solving skills; however, 1st‐year students placed greater value on the demonstrator’s ability to address student inclusivity, whereas 2nd‐year students preferred the provision of strong subject knowledge. Interestingly, demonstrators’ own perception of their teaching ability was different from student feedback on their performance, which may be due to lack of reflective practice. We propose a multimodal training framework that includes inclusivity/approachability and reflection as an integral part of training. This study further suggests that demonstrator training needs to be tailored to the changing needs of students as they progress through the different levels of their degree. Our proposed framework is particularly relevant to the current pandemic which has affected young people’s mental health, confidence and openness to new experiences. We ask, if we focus equally on active training of demonstrators with respect to the affective, cognitive and psychomotor learning domains, how will students and demonstrators respond? We found that this training approach was extremely effective and helped us develop an active demonstrator training framework that underscores the importance of personalized student learning and continued demonstrator self‐reflection. COVID‐19Coronavirus Disease 2019GTAGraduate Teaching Assistants GSETStudent Evaluation of TeachingSEEQStudent Evaluation of Educational QualityTeaching laboratories for practical‐based courses are a unique place for students to actively apply the concepts and methods covered in theory courses and are therefore essential learning outcomes in Bioscience at the University of Liverpool , the UK First‐year students entering into the higher education are considered to be in the critical transition stage . They coSome previous studies on demonstrator training provide a context for our study, which we have grouped in the following five areas.It is clear there are challenges to provide effective demonstrator training with the resources available in any given academic department. Demonstrators’ attitude to their work has been shown to be negatively affected by the amount of time they are actually paid for and their general working conditions . DemonstSeveral studies have provided rich insight into best practices for developing demonstrators’ competencies. Many training approaches were initially stand‐alone, covering general pedagogy approaches which involved introducing various learning theories  and general instructional methods (ways to effectively communicate with students and explain concepts clearly) as well as teaching evaluation and feedback, etc. , 33, 34.et al found that demonstrators were unaware of the students’ needs across the cognitive, psychomotor and emotional domains and identified this as a barrier to developing their teaching effectiveness [Experimentation in the laboratory evokes not only Bloom’s taxonomy cognitive learning domain (inquiry‐based learning) but also the overlapping psychomotor and affective domains. However, a survey of studies over the past 4 years shows that the emphasis for training demonstrators is often placed principally on the cognitive and psychomotor domains Table . For exativeness . It is cAn increasing number of studies have emphasized that the evaluation of demonstrators’ training should be an integral part of the training strategy. Most of the studies have focussed on surveying the demonstrators on their opinions of the training that took place , 39, 40 There are examples of demonstrators’ training development programmes across different nonbiological disciplines including chemistry, physics, maths, electrical engineering, geosciences, etc. , 40, 41;Demonstrators’ understanding and application of pedagogy impacts their development as a teacher . SeveralThis study continues the process of evidencing barriers, aligning expectations and developing demonstrators’ teaching capability that promises to provide wider implications to the field. We recognize that for demonstrators to successfully enhance the laboratory learning experience of students across cognitive, psychomotor and affective domains, it is essential that demonstrators’ training has learning objectives guided by these domains. Moreover, when learning tends to be more active and inquiry based in laboratories, the training should also be delivered in an active way. We also recognize that the findings from existing demonstrators’ training programmes cannot be easily extrapolated to the field of bioscience as the students joining this discipline come from a much wider range of academic backgrounds (with or without maths/chemistry/physics). In addition, students joining 1st‐year bioscience have a socioeconomic diversity and therefore demand personalized learning strategies, where the student’s individual needs are carefully considered. This is particularly important as many previous studies on training demonstrators have recommended student‐focussed approaches but have briefly touched on the important topics of student diversity and inclusivity, in many cases due to the limitations of time and resources .To improve two bioscience laboratory courses, we hypothesized that instead of solely passive, basic, content‐based training, demonstrators should be actively trained across three domains for student learning: subject‐specific knowledge (theory and practice for each laboratory), inquiry‐based learning (focused on problem‐solving skills) and group management . To address this hypothesis, we revamped the demonstrators’ training on first‐ and second‐year practical modules. Both the students and the demonstrators were surveyed to evaluate the changes in the teaching effectiveness of demonstrators pre‐ and post‐training. Open comments from students and demonstrators were used to further explore students’ and demonstrators’ views. We then used this information to make recommendations in the form of a multimodal training framework that will prove useful for other laboratory courses with large sizes and diverse cohorts.If we focus equally on active training of demonstrators with respect to the three Bloom’s learning domains, how will students and demonstrators respond to this in the laboratory? Will this response change from a general year 1 to a more specialized year 2 laboratory course?How do demonstrators perceive their teaching abilities before and after receiving this training and how does this correspond to the student perceptions?What does our data suggest for best practices in training demonstrators?First‐ and second‐year bioscience students enrolled in the School of Life Sciences at the University of Liverpool were recruited in the study. All first‐year students (400‐420 students) take the core experimental skill practical module which runs over two semesters. This module covers introductory practical elements for the range of bioscience programmes in the school . The learning objectives are to level the playing field for basic experimental skills such as using basic scientific equipment, planning and executing experiments, data analysis and evaluation of hypotheses. The module is assessed by a range of online tests, abstract‐writing and in‐laboratory competency tests of practical skills, whereas in second year, only a subset of students (140‐160 students) choose the 6‐week molecular biology practical module and were therefore recruited in this study. This module is more specialized, focusing on key molecular biology topics such as microbiology techniques and cloning. The learning objectives are to prepare students for doing molecular biology in future practicals and their research project, and there is more of an emphasis on problem‐solving and writing as well. The module is assessed by a range of online tests, abstract‐writing and a problem‐solving written tests. For both year 1 and year 2 practicals, although the laboratory manuals do provide traditional step‐by‐step instructions, the emphasis for each week is on inquiry‐based learning. This involves extensive problem‐solving exercises related to their work to explore, actively apply and extend their understanding. Year 2, however, was much more advanced and focused on multiweek investigations. Students from 2018/2019 cohort represent the group who received teaching from the demonstrators with minimal training while 2019/2020 cohort received teaching from demonstrators who were provided extensive training  and 6  demonstrators who delivered teaching for two consecutive years and received minimal as well as extensive training (2018 and 2019) were included to allow comparison between minimal versus extensive training. The number of student respondents was lower in 2018 compared with 2019 as the surveys were distributed online in 2018 while they were distributed in person for 2019 which increased participation significantly.The demonstrators recruited for this study were in second or third year of their PhD programmes in different disciplines across the Faculty of Health and Life Sciences at the University of Liverpool. 30 and 12 demonstrators helped teach the 1Training workshops for demonstrators were provided by academic staff who have had a considerable teaching experience in the instructional laboratories. The training took place weekly, a day before each practical day and was categorized as minimal or extensive. Minimal training consisted of an hour‐long training where demonstrators were given theoretical overview of the practical and demonstration of the equipment which were to be used in practical. In the extensive training, demonstrators were provided with 2‐hour face‐to‐face time that was interactive with additional work to do outside of the laboratory in their own time as shown in Table For the extensive training, weekly monitoring of demonstrators was undertaken to ensure the training was applied during the practical. Demonstrators had to fill in a checklist that reports on the individual needs for each student their group each week which helped academic staff develop support plans. For example, academic staff were able to allocate ‘floating’ demonstrators to these students to give them 1:1 support and get them back on track. Also, opportunities for changing student groups were explored so better peer‐assisted learning could take place with their new partners. This approach made students feel that a diversity of learning needs was being accommodated and that the practical was inclusive.Students and demonstrators were surveyed at the end of semester 1 using online or printed questionnaires. Teaching quality can be evaluated using the nine identifiable dimensions of Student Evaluation of Educational Quality (SEEQ) . SEEQ waP < 0.05 was considered as statistically significant results.The responses from students were collected in the form of online and paper questionnaires, and EvaSys software was used to convert Likert scale responses to the numbers from 1 to 5 with 1 being the most negative response (e.g. strongly disagree) and 5 representing the most positive (e.g. strongly agree). For each question, each Likert value was converted to a percentage, and these were plotted as stacked bar graphs. A Mann–Whitney test was performed to test for significance between the two years. Ethics for this study was granted by the University of Liverpool Research Ethics Committee . No information on gender, age or demographic factors was requested. Participants were given information sheet, and they signed the consent form prior to their participation. Participants were informed that their participation is voluntary, and their responses would be kept anonymous.Students’ evaluation scores are a rich source of data to help improve teaching and learning, and they frequently mirror students’ perceived learning across the cognitive, psychomotor and affective domains , 51, 56.P ≤ 0.0001 (year 1), P ≤ 0.017 (year 2)) compared to demonstrators with minimal training from 2018/2019  was observed after they received extensive training  . Also, tOur studies have explored demonstrators’ training for bioscience students and revealed some key findings that further this field and are particularly applicable to laboratories with large cohort sizes.Our training places an equal importance on developing demonstrators’ ability to understand students’ individual needs and to customize their teaching strategies to promote personalized student learning. This is particularly relevant during and after the COVID‐19 pandemic, which has taken its toll on school leaver’s mental health and confidence and who would find transitioning to university especially challenging and calls for training demonstrators to effectively deal with the academic unreadiness of 1st‐year students.st years require more inclusive teaching approaches (affective domain) to give them confidence to become independent learners. Second‐year students, however, need more attention with their independent academic performance, such as problem‐solving skills (cognitive domain).In our study, we have not only designed thorough strategies but also conducted student as well as demonstrators’ evaluation within a single study. This allowed quality assurance of our training programme and ensured productive learning on both sides as indicated by increase in teaching evaluation scores and/or comments. Furthermore, most SET‐based research studies are not multilevel as the comparisons are simply made between different disciplines. These uncontrollable variables due to different subjects pose limitations to these studies. Our study ran across two years and at two levels of bioscience in the same academic department. This direct and continuous comparison gave valuable insight about changing needs of students and how demonstrators’ training needs to be tailored. While both years require demonstrators to provide the essential psychomotor/cognitive instruction, 1Simultaneous student and demonstrator student evaluations also allowed us to evidence the misalignment of demonstrators’ perceptions and students’ expectations. As such, ongoing reflection should be a critical component of training.Is there sufficient departmental budget available for demonstrators?Is there sufficient staff expertise to actively engage with demonstrators?Can face‐to‐face training be combined with asynchronous training resources to provide more flexibility?Can demonstrators be informed about the academic preparedness of their students (which will depend on year group) and is there a mechanism each week (such as a checklist/cheat sheet) to ensure they provide personalized student learning in their group?Can regular demonstrator self‐reflection be monitored, and staff feedback be provided?Based on our findings, we present a multimodal training framework Fig.  to guideThis study and framework will allow us to better articulate to future demonstrators how they can positively impact laboratory‐based student learning and development over their first two years. With better understanding of active and student‐focussed training, we hope to better motivate demonstrators to create and sustain a learning environment that encourages and supports students’ efforts to learn.The authors declare no conflict of interest.st‐year data and wrote the paper. ES designed the project, acquired and analysed 2nd‐year data and wrote the paper.RA conceived and designed the project, acquired 1Fig. S1. In depth analysis of the students’ responses in open comments section of questionnaire. The open text responses from 1st year (n = 70) and 2nd year (n = 102) were coded to three main themes‐subject knowledge, problem solving and approachability & inclusivity (A&I). Within each category, the themes were split into positive or negative comments. The size of each bubble and the number within each bubble represent the percentage responses  in that category.Click here for additional data file.


Octodon degus) colony in central Chile. We ask whether native and non‐native forbs respond differently to degu bioturbation on runways versus herbivory on grazing lawns. We ask whether this can explain the increase in non‐native plants on degu colonies. We found that biopedturbation did not explain the locations of non‐native plants. We did not find direct evidence of grazing increasing non‐native herbs either, but a grazing effect appears to be mediated by grass, which is the dominant cover. Further, we provide supplementary evidence to support our interpretation that a key mechanism of non‐native spread is the formation of dry soil conditions on grazing lawns. Thus, ecosystem engineering  may be an outcome of disturbances, in which each interacts with specific plant traits, to create the observed pattern of non‐native spread in the colony. Based on these results, we propose to extend Jentsch and White  concept of combined pulse/ disturbance events to the long‐term process duality of ecosystem engineering/ disturbance.Different conceptions of disturbance differ in the degree to which they appeal to mechanisms that are general and equivalent, or species‐, functional group‐, or interaction‐specific. Some concepts of disturbance, for example, predict that soil disturbances and herbivory have identical impacts on species richness via identical mechanisms (reduction in biomass and in competition). An alternative hypothesis is that the specific traits of disturbance agents  and plants differentially affect the richness or abundance of different plant groups. We tested these hypotheses on a degu ( Octodon degus) colonies. We show that this is the result of the interaction and accumulation of two kinds of disturbances (biopedturbation and herbivory) with different effects on plant species over time. Although disturbance is usually represented as different from, or indeed opposite to, ecosystem engineering, we show that ecosystem engineering best explains the outcome of these two disturbances over time.Non‐native plant species accumulate on the centers of native degu ( The degu a priori we are unsure whether biopedturbation level is always correlated with herbivory level or only in specific spaces. We expect accumulated disturbance/ pulse outcomes from both herbivory and biopedturbation to be jointly highest in the older colony center. Specifically, we predict that the ruderal non‐native herbs should be more common where bioturbation on runways is the highest. We also predict that non‐native herbs adapted to herbivory should be more common at sites with higher grazing pressure  to different combinations of disturbances forming different disturbance/ pulse conditions, represented in the degu colony as areas with differing accumulation of herbivory or biopedturbation, or both, over time. We expect that herbivory may vary in intensity/ accumulated impacts both across the colony structure and at the level of structures within the colony (on and away from runways). This pattern is not necessarily the same as the distribution of the highest biopedturbation, which we expect to be diffused across colony center runways and grazing lawns, and intensively concentrated along runways in more peripheral areas. These patterns may interact, although 22.1Acacia caven savanna) subject to occasional fires (1–2 events per decade) and grazing by cattle and sheep , open grasslands dominated primarily by native perennial grasses, and denser matorral (evergreen shrubland), dominated by sclerophyllous shrubs and perennial herbs. The study site includes extensive degu colonies with lawns of herbaceous species, found essentially in the valleys of a small mountain range  forming one side of the field station.The study took place in September and October 2011 at the Estación Experimental Rinconada de Maipú , a field station of Universidad de Chile, Santiago, Chile. The field station consists of espinal  mounds. We considered the presence of fresh soil from excavations or the presence of fresh droppings to determine that a burrow was occupied  colonies, since this species has a similar life history, ecological niche, and use of habitat, compared to degus. Prairie dog colonies expand outwards so that central areas are older than peripheral areas  (runways are about 8 cm wide) and 25 cm from the runway edge (“off runway”). Each quadrat was a square cardboard frame of 10 cm2.5Plant species rarely completely overlapped each other due to an absolute low abundance in all plots, and it was possible to identify each species from the photographs of the quadrats by the shape of cotyledons, leaves, and flowers. Names and distributions are according to Hoffmann , except Plants were identified and counted using SamplePoint (in R), which records the classification into user‐defined categories for pixels at crosshairs arranged in a regular grid over the digital photograph. We set our grid to 25 crosshairs per photograph. Dead plants were not counted. When plants overlapped, we counted the top (visible) species only. Overlap occurred only rarely . Cover was calculated as count number.2.6We assessed herbivory by measuring the amount of rolled oats eaten from dishes. Oats are a favorite food of degus and are used to trap them . We summed the total number of footmarks for the two tracking cards for each site to yield a measure of bioturbation along runways. We observed only one footprint from another species, not identified. We did not collect bioturbation data for the site omitted from the foraging data collection.We measured bioturbation on runways as the amount of degu traffic along the runway‐transect in each plot. We recorded degu traffic using tracking cards Meserve, . We cut 2.8t test and correlations to examine grass distribution, the relationship between the measures of herbivory and biopedturbation with grass cover and moss cover, and the distribution of native and non‐native plants.Native and non‐native plant abundances or cover was approximated by total count number from the virtual grids in SamplePoint, summed across the quadrats of each plot; we summed across different plant categories and/or quadrat positions to construct plot‐level measures. Grass cover was calculated separately in the same way. Biopedturbation was quantified as the sum of footmarks per plot. Herbivory was quantified as mean grams of oats eaten per plot across the three sampling periods. We compared these across central and peripheral plots and along the gradient of grass cover. Because space‐for‐time substitutions involve interpretive assumptions that are not present in long‐term manipulative experiments, we chose to use the most conservative (least powerful) statistical approaches available to decrease the likelihood of detecting ecologically weak effects .We observed 10 native or endemic forb species or morphospecies, two taxonomic groups dominated by native species (mosses and grasses) and 3 non‐native species along all of our transects. There was no significant difference in cover per taxa between native and non‐native forbs . At a finer scale, natives and non‐natives were also distributed differently on or away from runways: Native cover was higher off runways than on, and higher than non‐natives; non‐natives were also higher off runways . We found an interaction between colony location (center/ periphery) and runway location (on/ off) on the total cover of non‐native species: The cover of non‐natives is higher in the colony center than in peripheries, and higher on runways than off runways, a distribution significantly different from expected .Natives and non‐natives were distributed differently across the plot locations, with non‐native cover higher in central plots and native cover higher in peripheral plots, which was significantly different from the expected cover distribution . The ratio of non‐native to native herb abundance per plot was negatively correlated with the grass proxy (mean grass cover off runway) per plot . Focusing on moss as a proxy of colony age, we find that the mean percent cover of moss differed significantly across on–off and center–periphery and was highest on peripheral runways .Mean grass cover on off‐runway quadrats, the proxy for colony age, was not lower in central plots, differing from our expectation . Although grass tended to have relatively greater cover on runways, and other herbs tended to have relatively greater cover off runways, these differences were not correlated at the plot level.The ratio of grass cover on to off runways per plot was not correlated to the general distribution of other herbs on and off runways per plot . The number of footmarks registered on runways also did not differ between peripheral and central plots . Footmarks and total amount of oats (on and off runways) eaten in each plot were highly correlated . Footmarks increased as the percent cover of grass on off‐runway quadrats increased . However, there was only a weak, nonsignificant relation between percent grass cover on runways and footmarks . Biopedturbation was not correlated with the ratio of non‐native to native herbs .The average amount of oats eaten per day did not differ between peripheral and central plots (Student's 4N = 11) was almost four times that of non‐native species (N = 3) on degu colonies. The number of native or endemic species may have been underestimated as we were not able to identify mosses or grasses to species level. As expected, the cover of non‐native plants was higher in the center of the colony than in the periphery. At a finer scale, the runways also influenced the distribution of native and non‐native plants. Non‐native plants had higher cover on runways in central plots than in peripheral plots.We found that the richness of native nonwoody herbaceous taxa , but reduces in extent as the microhabitat becomes drier (see below).The distribution of high and low biopedturbation and herbivory was not explained by colony position, but was explained by grass cover off runways. As grass cover in the off‐runway quadrats increased, foot traffic on runways and amount of herbivory on oats increased. Grass avoidance  may thus mediate degu biopedturbation on runways. We suggest that avoiding grass will occur whenever it is long enough that it needs to be physically pushed aside to make a runway or impedes degu movement . If an important impact of degus is via soil aridity in grazing areas, this might also account for Madrigal et al.  and trophic (herbivory) forms of disturbance/ pulse appear to have different impacts that interact with one another and with specific plant traits to create ecosystem engineering that favors non‐native herbs on degu colonies ; Investigation (lead); Methodology (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Cesar Muñoz: Investigation (supporting). Juan J. Armesto: Supervision (lead).


Partial lung resection by video‐assisted thoracoscopic surgery (VATS) was performed, and the intraoperative frozen section of the tumor tissue resulted in a diagnosis of carcinoid tumor. As a result, right middle lobectomy by VATS was performed. The final histological diagnosis of the permanent specimen was intrapulmonary type A thymoma. VATS thymectomy was performed three months later. The histological diagnosis was type A thymoma with intrapulmonary metastasis (Masaoka stage IVb). Additional therapy was not performed because complete resection was achieved. Follow‐up CT was performed once every six months after the operation. The patient has been followed up for one year without any further recurrence.A 79‐year‐old woman was referred to our facility because of an abnormal chest shadow. Chest computed tomography (CT) showed a solitary right middle lung nodule with a maximum diameter of 3 mm and anterior mediastinal nodule with a maximum diameter of 21 mm. The lung nodule was suspected of being a primary lung cancer rather than a metastatic tumor because there were no primary malignant tumors, apart from an anterior mediastinal tumor visible on diagnostic imaging, including F It is rare for type A thymoma to have solitary and tiny pulmonary metastasis. Intrapulmonary metastasis of thymomas should be kept in mind in the differential diagnosis of small pulmonary nodules, even when they are solitary. Type A thymoma is a relatively rare histotype that progresses very slowly. The distant metastasis of type A thymomas is rare. We herein report a case of type A thymoma with solitary pulmonary metastasis.18 fluorodeoxyglucose‐positron emission tomography (F18 FDG‐PET) showed no uptake of FDG in either the lung or anterior mediastinal nodule . She had never smoked and had no family history of cancer. CT scan showed a small nodule (maximum diameter: 3 mm) in the right middle lobe of the lung and an anterior mediastinal nodule (major axis: 21 mm) with ring‐shaped calcification Figure . These nThe lung nodule was suspected of being a primary lung cancer because it was solitary and there were no other suspected primary malignant tumors. The anterior mediastinal nodule was suspected of being a vascular malformation because the nodule showed ring‐shaped calcification and no uptake of FDG on positron emission tomography‐computed tomography (PET‐CT).Partial lung resection by video‐assisted thoracoscopic surgery (VATS) was performed, and a carcinoid tumor was diagnosed by using an intraoperative frozen section. Therefore, additional resection (right middle lobectomy) was performed. However, the final pathological findings revealed a well‐defined tumor, and the small tumor cells grew like alveolar cells on hematoxylin and eosin staining Figure . FurtherThymomas are anterior mediastinal tumors with low to moderate malignancy. In the database from the Latin‐American Consortium for the Investigation of Lung Cancer (CLICaP), the five‐year OS rates were 73.4% for stage I–II, 63.8% for III–IVa and 51% for IVb.The most common site for metastasis or recurrence in thymomas is pleural dissemination. It is quite rare for thymomas to cause a single pulmonary metastasis as in this case.Some type A thymomas are extremely progressive, and atypical type A thymoma was added to thymomas subtype in the 2015 WHO classification.In the present case, the thymoma and intrapulmonary metastasis of the thymoma were found at the same time. The simultaneous solitary intrapulmonary metastasis of thymoma, not recurrence, is quite rare. It was difficult to diagnose the lesion as a thymoma and solitary lung metastasis of thymoma by diagnostic imaging. The pulmonary nodule was not suspected of being a metastatic lung tumor because it was solitary and tiny, and there were no lesions, including the anterior mediastinal tumor that showed the uptake of FDG on PET. The anterior mediastinal tumor was not considered a malignant tumor based on the PET finding until the pathological results of the pulmonary nodule were obtained. The thymoma was conventional type A because it did not have pathological characteristics of atypical type A.In conclusion, it is necessary to keep lung metastasis of thymoma in mind as a differential diagnosis of a solitary lung nodule.The authors have no conflicts of interest to declare.


Research and continuous quality improvement in pediatric rehabilitation settings require standardized data and a systematic approach to use these data.We systematically examined pediatric data concepts from a pediatric learning network to determine capacity for capturing gross motor function (GMF) for children with Cerebral Palsy (CP) as a demonstration for enabling infrastructure for research and quality improvement activities of an LHS. We used an iterative approach to construct phenotype models of GMF from standardized data element concepts based on case definitions from the Gross Motor Function Classification System (GMFCS). Data concepts were selected using a theory and expert‐informed process and resulted in the construction of four phenotype models of GMF: an overall model and three classes corresponding to deviations in GMF for CP populations.Sixty five data element concepts were identified for the overall GMF phenotype model. The 65 data elements correspond to 20 variables and logic statements that instantiate membership into one of three clinically meaningful classes of GMF. Data element concepts and variables are organized into five domains relevant to modeling GMF: Neurologic Function, Mobility Performance, Activity Performance, Motor Performance, and Device Use.Our experience provides an approach for organizations to leverage existing data for care improvement and research in other conditions. This is the first consensus‐based and theory‐driven specification of data elements and logic to support identification and labeling of GMF in patients for measuring improvements in care or the impact of new treatments. More research is needed to validate this phenotype model and the extent that these data differentiate between classes of GMF to support various LHS activities. Analytical strategies, including phenotyping algorithms, to improve the identification of cohorts related to physical function would be a great benefit to research, quality improvement, and clinical practice in pediatric rehabilitation settings. Therefore, our work in developing a phenotype model of gross motor function (GMF) built on EHR data standards and architectures is more exploratory and conceptual and serves as a foundation for future phenotyping algorithms to define functional classes broadly from existing data sources.1.1Many clinicians , physical therapists (PT), and nurse practitioners) use the Gross Motor Function Classification System (GMFCS) level to classify physical function during routine care for patients with CP. The GMFCS is a five‐level ordinal classification structure  and a standard screening tool used to classify deviations in the performance of GMF activities for children with CP.GMFCS levels describe performance and participation rather than CP‐related physical impairment and body region involved .1.2In the past decade, federal funding and non‐profit organizations supported establishing LHS in pediatrics by developing several national clinical data research networks.2The overall aim of this study was to develop a methodology to build conceptual classification models of functional performance phenotypes from EHR data concepts in pediatric learning networks. Objective 1 of this aim was to construct a phenotype model of GMF using a theory and expert‐informed approach based on SHOnet CDM discrete data element concepts and using existing case definitions for each GMFCS level as gold‐standard phenotype definitions. Objective 2 of this aim was to define three clinically meaningful classes of GMF that were derived from an expert‐panel review of a set of data element concepts and corresponding value sets available in a pediatric EHR.33.1The use of functional performance data elements to build patient cohorts for research or quality improvement is challenging because different clinicians observe and record physical functioning differently and this is not captured discretely or consistently by providers. Functional status can also represent a challenge because of the range of states (high functioning to low functioning), and it manifests differently in different patients. For this research, we designed a stepped and iterative process based on consensus expert reviewGiven this initial effort in what may be a more difficult classification, function across multiple subgroups rather than presence or absence of a condition on binary terms, for this study, the GMFCS was collapsed from five levels into three distinct classes of GMF. The three classes are largely consistent with major functioning levels: GMF Phenotype Class 1 includes GMFCS I and II, those who ambulate without assistive devices; GMF Phenotype Class 2 corresponds to GMFCS III, those individuals who use assistive devices including wheelchairs; GMF Phenotype Class 3 includes GMFCS IV and V, those individuals who have significant ambulatory limitations. Furthermore, all phenotype models in this study corresponded to patients aged 6‐18 years old because this is the largest age‐band of the GMFCS that overlaps with the school setting and clinical practice  guidelines and 6‐18‐year‐olds are expected to have stable GMFCS levels. The construction of the GMFPM and classes proceeded through four phases. As this model is foundational and is not a computable phenotype using real patient data, the validation of such a model is beyond the scope of this paper.3.2In Phase 1, two SHOnet clinical domain experts, an OT and PT with experience treating patients with motor dysfunction, systematically selected data element concepts and value sets for panel review. These domain experts reviewed and scrutinized 10 000+ observational discrete data element concepts in the SHOnet CDM to be included for panel review. The initial set of data concepts were selected based on existing knowledge of routine care, evaluation, and treatment of patients with CP by SHC OTs, PTs, and nurses and if the concepts were thought to align with and deviate across GMFCS levels. For example, the data concept “Ambulation Level” had six possible values of mobility performance that demonstrate significant visible deviations between GMFCS levels and is known to be collected by OT and PT. Muscle tone and motor control dysfunction are common problems in patients with CP; therefore, data concepts for “Drooling” relates to oral motor control and concepts for “Elbow Tone” and “Ankle Tone” are known to deviate between GMFCS levels. Those not selected included data concepts for manual muscle testing of specific muscles and concepts on lab values that may be indeterminate of GMFCS levels. The initial set comprised 540 data element concepts. Domain experts then convened to review the initial selection of data concepts and removed those that were redundant or extraneous to functional performance. This resulted in a final selection of 89 data element concepts.In Phase 2, the final set of data element concepts were consolidated into 31 unique and derived variables for use in the expert‐panel review exercise to assign and rate variables. A unique variable corresponds to one data element concept. A derived variable corresponds to many data element concepts that could be collapsed into one variable due to similarity in concept and value set. This also simplified the expert‐review process. For example, multiple data element concepts correspond to different types of assistive devices a patient may use with the same yes/no value set, so the concepts were combined to form the derived variable “Assistive Devices Used.” This contrasts the “Ambulation Level” variable, which is a single data element concept that maintains a standard 6‐level value set spanning “Independent” to “Dependent” performance. The 31 variables included 14 unique variables, 17 derived variables, and corresponding value sets. Variables and GMF classes formed a two‐step grid‐like exercise for expert‐panel review and completion.In Phase 3, we convened a panel of four new domain experts to support the design of the overall phenotype model and classes. The panel included four licensed clinicians and mobility researchers (three PTs and one OT) from three different SHCs with extensive knowledge of CP and an average of over 20 years of clinical experience. Panelists independently completed an evaluation exercise for the 31 variables by each GMF class. Figures In Phase 4, we stratified the overall phenotype model by the three GMF classes based on the deviations in how panelists allocated performance values for all variables in EX1. The deviations in assigned values informed the construction of logic statements and rules for variable value sets to instantiate membership to one of the three GMF classes. Each statement contains human‐readable text and includes a stem, rule, and qualifier that is stratified by value sets for each variable. Two PhD trained, licensed PTs, on the SHOnet team with informatics and clinical domain expertise reviewed the structured rules, value assignments, and logical operators for variables in each GMF class for content validity.3.3The expert panel responses to the two‐step evaluation exercise were analyzed in three‐phases. First, we reviewed the values in EX1 that individual panelists assigned to the 31 variables across each GMF class and assigned an overall value per GMF class based on one of three results: (a) One variable value received panel consensus. (b) If panelists assigned multiple values to a variable or there was a tie in values, then both values comprised the final value for a GMF class . (c) If the result in (a) or (b) did not occur, then the respective GMF classes included all values assigned by panelists.Next, we analyzed the panel ratings in EX2 regarding the extent to which variables differentiated between the three GMF classes. The variables were analyzed for one of two criteria to be included in all GMF classes: (a) if a consensus of panelists rated the variable ≥3, or (b) if the panel responses for a variable were split, for example, if the variable received two ratings of ≤2 and two ratings of ≥3. Furthermore, the models did not include variables that received a consensus rating of ≤2.In the final phase, we conducted a quality control check by analyzing the conformance between how panelists assigned values for each GMF class and how panelists rated the differentiation between GMF classes. Instances of conformance occurred when assigned variable values deviated across all GMF classes in EX1 and panelists rated the variable differentiation ≥3 in EX2. If the responses did not conform, then the variable was not included in the model. Microsoft Excel was used to complete all analyses.4The overall GMFPM and three GMF classes included 20 variables that correspond to 65 performance‐related data elements that were identified by the expert panel. Table Variables were grouped into five performance‐related domains Table  to organEach GMF class comprised human readable logic statements and rules for each variable to instantiate membership to that respective GMF class. As an exemplar, Table 4.1The analysis of conformance revealed inconsistencies for two variables. The first variable was “General Lower Extremity Muscle Tone”. Although panelists perceived that this variable moderately differentiated (≥3) between GMF classes, the panelists all assigned the same performance value to each GMF class. Due to the inconsistency, this variable was not included in the overall model. This may have occurred due to the decreased granularity in the value set for this variable. Fortunately, the panel also selected two other lower extremity derived variables that were joint‐specific and had more granular value sets: “Knee Tone” and “Ankle Tone.” These variables included both flexor and extensor tone data elements and are scored using the Modified Ashworth Scale (MAS). The MAS is a standardized 6‐level ordinal scale  of muscle tone and better deviates between GMF classes compared to variables for presence of general tone with yes/no value sets.The second inconsistency occurred for the variable “Elbow Tone.” On the 5‐level rating of differentiation between GMF classes, two panelists rated “Elbow Tone” ≤2, one panelist declined to rate the variable, and another rated the variable a 3. The overall performance values applied to “Elbow Tone” using the MAS followed a clear gradation across each GMF class . At the discretion of SHOnet domain experts, “Elbow Tone” was included in all models.5In this study, we developed a standards‐based, expert‐informed GMFPM that also offers flexibility across three clinically meaningful classes aligned with the GMFCS. This is the first instance where CDM data concepts are organized into a phenotype model of functional performance specifically for pediatric rehabilitation. The findings illustrate that GMF for initial cohort identification activities in pediatric rehabilitation can be represented through phenotypes of discrete data elements within standardized database models used widely in CDMs for learning networks, data registries, and data warehouses. This study also underscores the complexity of modeling functional performance using standardized data elements and the rigor necessary to develop similar typologies of functional performance in the future; therefore, our methods should be informative, nonetheless.The work supports future infrastructure by exploring what data are available that fit into a conceptual model (GMFPM) that the CP community has already decided are important. These data element concepts can later be leveraged for different applications, including the development of a phenotype algorithm that can be deployed and validated on existing data for a given purpose. Our phenotype model will undergo additional study and validation to determine its performance in differentiating between GMF classes. In addition, this phenotype model serves as a measurement instrument to determine the documentation and value of these EHR data elements.The rigorous approach to construct the phenotype model and classes was contextualized by the desiderata for computable phenotyping and information modeling work by Westra et al.are collected. Instead, the typologies may be interoperable with other standards‐based pediatric data resources and foregrounds what data should be collected by clinicians and systems to classify clinically sensible classes of GMF. Moreover, the iterative approach we used demonstrates the utility of stretching existing methodologies into developing “functional” phenotypes for pediatric rehabilitation.The methods described in this paper support the theory‐based selection of data elements and corresponding interface terminologies to design the logic statements and rules capable of classifying cohorts of patients by deviations in GMF. The underlying rationales for provider documentation are unknown, but discrete data are likely documented for billing and administrative purposes, while diagnostic reasoning, including GMFCS levels and other information used to form these clinical phenotypes, is included in narrative documentation. Nevertheless, our findings demonstrate the inherent value of designing function‐based mechanisms around discrete data elements readily available in an EHR and learning network and emphasizes the need for improved capture of meaningful and usable clinical data. The benefit of using a theory‐driven, expert‐informed approach is that the typologies are not constrained by what data This expert‐informed GMFPM may support future predictive analytics of GMFCS for research; however, this study has strengths and limitations. The primary limitation of this study is its generalizability. Since SHC is a specialty pediatric healthcare system, the documentation of many of these data elements and their inclusion in SHOnet may be different from other systems. Other pediatric healthcare systems that manage general pediatric disorders may not prioritize, document, or have fields in the EHR for many of the data elements in the phenotype model.In terms of strengths, our study devised and applied a foundational methodological approach to phenotyping that could very easily be adapted to any other use cases, particularly in the field of medical rehabilitation. Evidence demonstrates that the re‐use of EHR data improves patient cohort identification and may be essential to support pragmatic prospective cohort studies with the economy of scale.The GMFPM, although not operational in an EHR, builds infrastructure from a CDM to identify pediatric patient cohorts by distinct categories of GMF for research and quality improvement. Our findings can also inform other multi‐site research and learning networks that support pediatric populations  of the opportunities afforded by building out their data elements for measurement infrastructure to conduct critical LHS research in rehabilitation. Future work should analyze data quality dimensions of the phenotype model, the extent that the typologies can validly differentiate between GMF classes, and its utility in applications such as CP hip surveillance efforts. More use‐cases of phenotypes for characterizing functional performance and care processes are needed to build a computable measurement library with economy of scale and scope for pediatric rehabilitation.The authors have no conflict of interest to disclose.Data S1. Supporting Information.Click here for additional data file.