Unintentionally, deer herbivory also occurred at night of Day 2. Distance‐based statistics demonstrated that PVOC compositions were plant species specific, but neither the earliest colonizer nor the subsequent colonizer community composition could be explained by plant species identity. Rather, Procrustes analysis showed that both the PVOC composition and that of the earliest colonizer community can be used to explain the subsequent colonizer community. In addition, the linkage between PVOCs and the subsequent colonizer community was stronger on individuals with deer herbivory. These findings indicate that PVOCs have widespread effects on initial community assembly, as well as priority effects brought on by stochastic immigration, and that plant species identity only has weak and indirect effects on the actual composition of the community.Plant traits, which are often species specific, can serve as environmental filtering for community assembly on plants. At the same time, the species identity of the initially colonizing arthropods would vary between plant individuals, which would subsequently influence colonizing arthropods and community development in the later stages. However, it remains unclear whether interindividual divergence due to priority effects is equally important as plant trait‐specific environmental filtering in the initial stages. In this study, we propose that plant volatile organic compounds (PVOCs) may play a crucial role as an environmental filter in the initial stages of community assembly, which can prevent the community assembly process from being purely stochastic. To test this hypothesis, we conducted short term but highly frequent monitoring (19 observations over 9 days) of arthropod community assembly on intact individuals of six willow species in a common garden. PVOC compositions were analyzed before starting the experiment and compared with arthropod compositions occurring on Days 1–2 of the experiment ( We elucidated the roles of plant volatile organic compounds (PVOCs) and priority effects in initial stages of the arthropod community assembly on six willow species. Despite the clear species‐specificity of PVOC composition, arthropod community assembly was better explained by interindividual variations of PVOC composition and the stochastically determined earliest colonizer community composition (structured in 2 days). Deer herbivory occurred unintentionally during the second night of the community assembly experiment; therefore, we investigated its influence on the colonizer communities.The overarching objective of this research is to contribute to a better understanding of the mechanisms underlying the initial stages of community assembly by investigating the proposed role of PVOCs as the environmental filter. We conducted short term (9 days from the establishment) but highly frequent (two–three times per day) monitoring of arthropod community assembly on four intact potted individuals  from each of the six willow species in a common garden. We sampled the headspace around the potted plant to analyze PVOC compositions from all individuals  before starting the community assembly experiment and compared them with arthropod compositions occurring on Days 1–2 of the experiment  and raw‐based (Inukori yanagi), Salix eriocarpa (Erio)(Ja yanagi), Salix gilgiana (Gilg)[or Salix miyabeana (Kawa yanagi)], Salix serissaefolia (Jess)[or S. jessoensis Seemen subsp. serissaefolia (Kimura), Ohashi, Kogome yanagi), Salix chaenomeloides (Chae)(Maruba yanagi), and Salix triandra (Tria)(Tachi yanagi). The abbreviations in the first brackets after scientific names are used in the following text and figures. The words in the second brackets are the corresponding Japanese names of the willow species, which would help avoid confusion related to the synonyms in willow taxonomy, since these are more stable and have fewer synonyms , occur sympatrically in a floodplain as the predominant woody plants along the Yasu River  in Siga Prefecture, central Japan. Willow stems from multiple clones of each species were collected from these populations and cultivated in an experimental willow garden at the Center for Ecological Research (CER) of Kyoto University  since 2002.Six willow species were used:  Ohashi, . The natWe cut and collected 1–2‐year‐old branches of willows (two branches per clone × two clones × six species) from the willow garden in the CER on May 13, 2016. After keeping their basal section in water for 10 days until root emergence, we potted them individually in soil  in pots  and supplied them with fertilizer (Hyponex (N–P–K = 6–10–5); HYPONeX) every 2 weeks. Potted plants were maintained in a greenhouse under a long‐day photoperiod (18 L: 6 D) at 25 ± 2°C and 50%–70% RH until the newly emerged shoots were approximately 15–20 cm in height with approximately 20 leaves. We used four potted plants for each willow species (two clones × two replicates per clone). These potted plants were used to collect plant volatiles and then located randomly in three lines of eight pots at 2‐m interval in the experimental garden next to the willow garden in CER on June 21, 2016. It appears that replicates within each clone are pseudo‐replicates. However, the high vegetative reproduction ability of floodplain species of willows  was used as the internal standard. The potted willow plant was covered with a TPS bag , and closed at the base of its stem with a clip. The TPS bag was fitted using two nozzles. One nozzle was connected to an air cylinder and the other was connected to a glass tube packed with an adsorbent . The glass was connected to an air pump . Purified air from the cylinder was directed into the TPS bag, and volatile compounds from the headspace of the plant were collected in the adsorbent by generating an air flow (100 mL/min) using an air pump collected on the adsorbent for 40 min.The volatiles were collected from each potted plant in a climate‐controlled room. A hexane solution of tridecane (0.5 mg/mL) impregnated into a piece of filter paper (1 cmhttp://riodb01.ibase.aist.go.jp/sdbs/cgi‐bin/direct_frame_top.cgi). The retention times of the volatiles were further compared with those of standard compounds . Compounds for which no standards were available were regarded as tentatively identified when >90% of spectra matched those in the databases.The collected volatile compounds were analyzed using a gas chromatograph mass spectrometer , Agilent Technologies, Santa Clara, CA, USA) equipped with an HP‐5MS capillary column . The system was equipped with a thermal desorption system (TDS), cooled injection system, and cold trap system . Headspace volatiles collected on the adsorbent were released by heating the TDS at 280°C for 4 min. Flash heating of the cold trap unit induced sharp injection of the compounds into the capillary column of the GC. The GC oven temperature was programmed to increase from 40°C (9‐min hold) to 280°C at a rate of 5°C/min. The headspace volatiles were tentatively identified by comparing their mass spectra with those from Wiley databases (Wiley7N and Wiley275) and the database of the National Institute of Advanced Industrial Science and Technology ).2.4Cervus nippon) early in the morning before the survey on June 23 (Day 3 of the experiment). The potted willow damaged with deer herbivory was recorded ), for a repeated measures ANOVA to examine the relationship between explanatory variables . The second method involved comparing the communities at each day with those on the previous day to calculate the dissimilarities between neighboring day communities. Since raw temporal series of compositional data were converted into temporal differences, we did not consider AR(1) structure and simply applied linear model to these datasets when investigating the influences of plant species identity and deer herbivory. More specifically, we first prepared the full model  and then selected the best model by stepAIC function.When testing whether each of these compositions  differed between categorical parameters , PERMANOVA (vegan::adonis) was employed after checking for the absence of heterogeneity of divergence (dispersion) within a level through PERMDISP (vegan::betadisper), with 4999 permutations. This is the same when checking if the community composition differed between days only when homogeneous dispersions between days were confirmed. Note that we also investigate the relationship between deer herbivory (occurring at the night of day 2), PVOC composition , and earliest colonizer community (observed in day 1–2) in order to confirm whether deer herbivory occurred randomly or was related to plant traits or conditions. The Mantel test (vegan::mantel) was also used to test for the presence of an overall linkage between two distance matrices.Plagiodera versicolora). The dissimilarity between PVOCs was calculated by the Hellinger distance as the same as our main analyses and the spatial median of replicates was obtained by using betadisper function in vegan package of R. Distance between the spatial median of the intact individuals and that of the infested individuals represents the inducibiliy of PVOCs for each willow species. Note that the source plant individuals for this PVOCs analysis were not identical to those in our study but originated from the same population , while the explanatory variables consisted of categorical variables  and continuous univariates (the first two axes of PCoA of PVOC composition and time). PERMDISP and PERMANOVA utilized multivariate continuous variables (dissimilarity matrices from PVOC composition and arthropod community composition) as response variables, and the explanatory variables are categorical only . Time (day) specifically acted as a categorical explanatory variable for analyzing time‐dependent arthropod community compositions (PERMDISP) but not used for assessing the snapshot of composition . When examining temporal changes in community compositions, the linear model was employed with univariate continuous variables (dissimilarity of community composition over time) as response variables and categorical variables  and continuous variable (time) as explanatory variables. Both Mantel test and Procrustes test utilized multivariate dissimilarity matrices from PVOC composition and arthropod community composition as both response and explanatory variables. In ANOVA for Procrustes residuals, the response variables were continuous univariates , while the explanatory variables were categorical . Notably, as this ANOVA was unrelated to time, the explanatory variables did not include time. In all analyses except for PERMDISP, plant clones were treated as a nested factor within plant species identity.2.5.6Both the base plot functions and ggplot2 packages were used. More specifically, PCoA  and cluster dendrogram (vegan:hclust) for two‐dimensional nonhierarchical clustering and hierarchical clustering were used, respectively.33.1Z)‐3‐Hexenyl acetate, (E)‐4,8‐dimethyl‐1,3,7‐nonatriene, (E)‐beta‐ocimene); the list of VOCs is available online (https://dx.doi.org/10.6084/m9.figshare.23583426). Moreover, 19 arthropod species/group  and one snail species  were identified , thrips (thrips sp.), aphid1 , aphid2 (Aphis farinosa), and spider (spider). Differences in their population dynamics between plant individuals were explained by plant species identity for two aphid species , by plant clones for two aphid species and spider, and by deer herbivory for A. farinosa , plant clones , and deer herbivory  while those in taxonomic richness were explained by plant species identity , plant clones , and deer herbivory  was greater than on S. chaenomeloides .Differences in the total abundance of colonizers in the communities were not explained by plant species identity  but became smaller than plant individuals without herbivory after deer arrival at the 2nd day night  but could not be evaluated for plant clones due to heterogeneous dispersion .Variations in PVOC composition between individual plants were explained by plant species identity , plant species , and plant clones , but not on deer herbivory . Due to these heterogeneous dispersions, it was not possible to evaluate the differences of community composition along time, between plant species, and between plant clones by PERMANOVA. Although PERMANOVA demonstrated the statistically significant differences of community composition with or without deer herbivory , these were not simply captured by PCoA plots , and its dependence on plant species identity changed with day . However, on each plant individual, the taxonomic composition did change , plant clone , or deer arrival . Similarly, the subsequent colonizer community  or plant clones . There was also no influence of deer herbivory . The overall correlation evaluated using the Mantel test was significant  for the earliest colonizer community versus the subsequent colonizer community but was not significant for PVOCs versus the earliest colonizer community , and PVOCs versus the subsequent colonizer community , respectively.The difference between the centroid positions of earliest colonizer community and subsequent colonizer community was not clear Figure  because e Figure . The eare Figure  was not y Figure  was not 3.4r = .2767, p = .3308; Figure F = 1. 9116, p = .16596) but clustered by plant clones  or plant clones . On the contrary, the linkage strength between the subsequent and earliest colonizer communities was not explained by plant species identity  and deer herbivory  but clustered by plant clones . The relationship between the two communities was not explained by plant species identify  and plant clones . Additionally, the relationship appeared to be stronger on plant individuals that experienced deer herbivory .After partialling out the influence of PVOCs on the earliest and subsequent colonizer communities, a significant correlation between these two communities still remained , Figure PVOCs were an effective predictor of overall arthropod community assembly, as suggested by Procrustes analysis. Although the Mantel test did not detect the linkage between PVOCs and communities, we assume that Procrusted analysis have a greater statistical power . However, PVOCs would influence the colonizer's choice of whether to stay on the plant individual, and the gradual increase in taxonomic richness and abundance on an individual plant made the link between PVOCs and community composition more apparent (within 9 days). This could explain why the link between PVOCs and community assembly was statistically significant for the subsequent colonizer community, but not for the earliest colonizer community.p = .178, adjusted R2 = 0.039) and with interindividual divergence . Although accumulating evidence suggests that induced PVOCs act as attractants and repellants for predatory and parasitoid arthropods as well as herbivorous arthropods , herbivore specificity should be evaluated using the differences in the induced traits between those induced by different herbivore species. The linkage strength between the earliest and subsequent colonizer communities was not associated with the inducibility of PVOCs .A priority effect is stronger with a stronger “herbivore specificity” of the herbivore‐induced plant traits, that is, when the dependence of herbivore‐induced plant traits on herbivore species identity is stronger. One may hypothesize that the inducibility of PVOCs shown in Figure 4.4Unexpected deer herbivory also had a substantial effect on arthropod community assembly. The counterintuitive finding that plant individuals with deer herbivory had a stronger link between PVOC and the subsequent colonizer community on Days 8 and 9 Figure  could beF = 1.6856, p = .0686). Since the earliest colonizer communities were not grouped by the presence/absence of deer herbivory, it is unlikely that plant traits were modified by the earliest colonizers and then attracted deer. Notably, we observed a higher taxonomic richness on plants at the 1st day that would later be subjected to deer herbivory at night on the 2nd day  as well as arthropods use PVOCs as foraging cues  studies suggest that deer herbivory can induce changes in plant physical and chemical traits Bryant, , but it 4.5In conclusion, our study found that both PVOCs and priority effects played a significant role in the short‐term, initial stages of community assembly of arthropods on willow trees. Although our monitoring was limited to the initial stages, we predict that the effects of community divergence observed in our study was not transient but could persist in a longer timescale, at least for several months, based on previous studies ; formal analysis (supporting); funding acquisition (lead); investigation (lead); methodology (lead); resources (lead); writing – original draft . Takeshi Miki: Formal analysis (lead); writing – original draft . Noboru Katayama: Investigation (supporting); writing – review and editing (supporting).The authors declare no competing interests.


Genetic diversity is the raw material of evolution, yet the reasons why it varies among species remain poorly understood. While studies at deeper phylogenetic scales point to the influence of life history traits on genetic diversity, it appears to be more affected by population size but less predictable at shallower scales. We used proxies for population size, mutation rate, direct selection, and linked selection to test factors affecting genetic diversity within a diverse assemblage of Neotropical salamanders, which vary widely for these traits. We estimated genetic diversity of noncoding loci using ddRADseq and coding loci using RNAseq for an assemblage of Neotropical salamanders distributed from northern Mexico to Costa Rica. Using ddRADseq loci, we found no significant association with genetic diversity, while for RNAseq data we found that environmental heterogeneity and proxies of population size predict a substantial portion of the variance in genetic diversity across species. Our results indicate that diversity of coding loci may be more predictable than that of noncoding loci, which appears to be mostly unpredictable at shallower phylogenetic scales. Our results suggest that coding loci may be more appropriate for genetic diversity estimates used in conservation planning because of the lack of any association between the variables we used and genetic diversity of noncoding loci. Genetic diversity is the raw material of evolution but varies between species in ways that are not yet fully understood. We used two genomic datasets for Neotropical salamanders to show that while diversity of coding loci is well predicted by population size and environmental heterogeneity, diversity of noncoding loci may be largely unpredictable. Understanding how intraspecific genetic variation differs across species is essential to predict the ability of populations to adapt to novel environmental conditions; genetic divergence plays a key role in most models of speciation and is increasingly being incorporated into conservation planning , which can vary as a result of both species‐specific parameters (variance in reproductive output and sex ratios) and historical demography  and the nearly neutral theory predicts additional diversity in smaller populations due to the balance of selection and drift maintaining slightly deleterious mutations  and genetic diversity for plants  predicted a large proportion of variance in genetic diversity while attributes of geographic range did not  were significant predictors of genetic diversity of transcriptomic loci, while life history attributes and an estimate of current population size were not  and genetic diversity, possibly as a result of a lower density of targets of selection on larger chromosomes  to understand which processes most strongly affect genetic diversity among species. We use an assemblage of Neotropical salamander species  as the reference for mapping with bwa‐mem . Because of differences in median read depth between datasets, we called SNPs at sites with a read depth of at least six for ddRAD and 60 for transcriptomes using a probability threshold of 1e‐06 for both types of loci.For the ddRAD data, we used the Stacks2  for both ddRAD and transcriptome loci for each species by dividing the number of variable sites between two alleles at each locus by the length of the locus. We then calculated median nucleotide diversity for each species across all loci as our estimator of genetic diversity. Finally, we examined correlations of the number of polymorphic loci, genome size, and the number of species in the pool with mean coverage for each species in our ddRAD dataset in order to understand possible technical artifacts related to coverage differences between species that may have affected our results.Because of the large size of the salamander genomes in our dataset and resulting reduction in sequence depth per locus, it is possible that we failed to obtain sequence data for both alleles for some ddRAD loci. Considering that low depth in ddRAD libraries can cause only one of the two alleles to be sequenced, we decided to estimate genetic diversity only from loci with at least one heterozygous site. To generate comparable estimates with transcriptomic data, we estimated genetic diversity from only heterozygous RNASeq loci. We estimated nucleotide diversity (2.3www.gbif.org), using only points with voucher specimens. We checked all points against range maps to eliminate misgeoreferenced specimens or incorrect taxonomic identifications. After quality control, we retained over 30,000 records for the species in our dataset, some of which included more than one specimen. We used the total number of vouchered specimens for each species after quality control as a proxy for population size. We estimated geographic range size by constructing a 10 km buffer around each museum record and merging overlapping buffers for a species. We then summed the total area of all buffered points as an estimate of geographic range size. Values for proxies of population size are given in Appendix We included three proxies of census population size that have been used in prior studies of genetic diversity : Bio4 , Bio16 (precipitation of the wettest quarter), and Bio17 (precipitation of the driest quarter). We calculated the range of the two precipitation variables for each species by subtracting the minimum and maximum values recorded for the species. For microendemic species known from a single site, the range of climatic variables was equal to zero; in these cases, we changed zeros to a very small number (0.001) to avoid undefined values after log transformation of the data. For temperature seasonality, we used the value extracted from the locality used in our molecular dataset.Although our ecological knowledge of most species of bolitoglossine salamanders is limited, a study of temperature selection and thermal variation in their environments showed that while most species select specific temperature ranges, they are often limited by the diversity of thermal environments with sufficient moisture Feder, . The abiBecause we have no estimate of mutation rate for the species in our study, we used mean annual temperature as a proxy for mutation rate following Wright et al. , who shoLissotriton vulgaris  was markedly shorter than the long maps found in Ambystoma mexicanum (Ambystomatidae)  and Notophthalmus viridescens . Plethodontid salamanders, which show the widest variation in genome size within the salamanders, may have as many as three chiasmata per chromosome arm , with homologous chromosomes essentially being scaled versions of each other across species depending on genome size , while we expected to see a positive relationship between these variables and genetic diversity for transcriptomic loci because environmental heterogeneity leads to selection for different alleles across a species range. Finally, we expected to see a negative relationship between genome size and genetic diversity, as larger genomes should have a lower recombination rate per bp and thus stronger linked selection, reducing diversity at linked sites. We summarize the hypothesized relationships between our predictor variables and genetic diversity in Figure Bolitoglossa alberchi, B. nympha, B. odonnelli, B. xibalba, Bradytriton silus, Chiropterotriton mosaueri, Cryptotriton monzoni, and Oedipina uniformis) do not have available genome size estimates and were excluded from initial analyses. Because we did not find a significant effect of genome size on genetic diversity for any subset (see Section We performed model testing separately for three genetic loci/species subsets: (A) heterozygous ddRAD loci, (B) heterozygous transcriptomic loci, and (C) ddRAD and transcriptomic loci for species in common between the two data types (hereafter referred to as the combined dataset). Because we expect most ddRAD loci to be noncoding, and thus less likely to be under direct selection, we compared the results of ddRAD and transcriptomic loci to understand the effects of direct selection on genetic diversity for the two locus types. Possible bias resulting from different species between the two full datasets was tested using the combined dataset. Eight of the species in the ddRAD dataset .Estimates of genetic diversity of bolitoglossine salamander species ranged from pi = .0012–.015 for ddRAD loci and pi = .0020–.0072 for heterozygous transcriptomic loci Figure . For ddRC‐value estimates.For ddRAD loci, no predictor variable was significantly associated with genetic diversity using a simple linear model or a phylogenetic stepwise model in either the full or the combined dataset. This remained true when the analyses were rerun excluding genome size to include the eight species without R2 = .49; Table R2 = .60). Results from ordinary least squares regression were broadly similar to those from phylostep although fewer variables were significant for the combined dataset  and one proxy of effective population size (SVL) are significant predictors of genetic diversity  is the predictor with the most weight for genetic diversity of coding loci, with the range of precipitation in the driest quarter having the second highest summed AIC weight Table . For ddR4We found contrasting results for predictors of genetic diversity for presumably noncoding (ddRAD) and coding loci. For ddRAD loci, no variable was significantly associated with genetic diversity for the full dataset or the set of species in both locus types. For transcriptomic loci, measures of environmental heterogeneity and proxies of population size explained a substantial portion of the variance in genetic diversity across both subsets. These results indicate that genetic diversity of noncoding regions of the genome may be unpredictable at shallower phylogenetic scales, in accordance with the results of Singhal et al. , while dNecturus maculosus  but higher than for Ambystoma opacum and A. talpoideum   vary by about one order of magnitude and fall within the range of those from previous studies that examined genetic diversity of eukaryotes using intergenic regions, introns and fourfold degenerate sites of pi = 0.0001–0.25  rather than to effective population size (Ne), a measure of the strength of genetic drift in a population. Although Nc and Ne should be correlated  under demographic equilibrium conditions, Singhal et al.  or because of differences in methodology and data quality. Likely as a result of the large genome size of all salamanders in this study, our depth of coverage for all ddRAD loci (mean coverage 5×–20× per locus across libraries) was low compared to mean depth of coverage for transcriptomic loci (23×–62×). This, combined with our use of only one individual per species, means that we were unlikely to uncover low‐frequency variants within species. The significant correlation between mean coverage and genome size  was not a significant predictor of genetic diversity estimated for either data type. This could either mean that linked selection does not affect genetic diversity strongly in this group, contrary to the results of Corbett‐Detig et al. , or thatIn contrast to the results for ddRAD loci, the best model for genetic diversity of heterozygous transcriptomic loci shows significant associations of proxies of environmental heterogeneity and population size with genetic diversity. Population size is typically negatively associated with body size, as larger organisms tend to have lower average population density . Our transcriptomic data have substantially higher coverage depth compared to the ddRAD loci, and thus estimates of genetic diversity are likely more accurate for transcriptomic data than for ddRAD data.The large difference in explanatory power and predictors between ddRAD and transcriptomic loci cannot be due to species composition alone, as the combined dataset had no predictor significantly associated with ddRAD genetic diversity while the model for transcriptomic genetic diversity had five predictors and much higher explanatory power  is often used as a proxy for adaptive genetic diversity, which should determine the ability of species to adapt to environmental changes such as climatic warming. The discrepancy between results from our two datasets may mean that, while genetic diversity estimates from noncoding loci are increasingly incorporated in conservation and management strategies, it may be more appropriate to use diversity estimates from coding loci, upon which selection is more likely to act directly. As an alternative strategy, conservation efforts could attempt to protect populations that are likely to maximize genetic variation based on the associations we found. For salamanders, our results suggest that larger species in environments with less variation in dry season precipitation are likely to have the lowest genetic diversity, and thus might struggle to adapt to environmental changes brought by climate change and anthropogenic disturbance. High‐quality whole‐genome data for closely related species, together with linkage maps or recombination estimates across the genome, will provide a stronger test of the impact of mutation, selection, and drift on genetic diversity of wild populations.María Guadalupe Segovia‐Ramírez: Conceptualization ; data curation (lead); formal analysis ; investigation (lead); methodology (lead); writing – original draft . Obed Ramírez‐Sánchez: Formal analysis ; methodology (supporting); writing – review and editing (supporting). Louis Paul Decena Segarra: Data curation (supporting); investigation (supporting); writing – review and editing (supporting). Hairo Rios‐Carlos: Data curation (supporting); investigation (supporting); writing – review and editing (supporting). Sean M. Rovito: Conceptualization ; funding acquisition (lead); investigation (supporting); project administration (lead); writing – original draft .The authors have no conflict of interest to declare.


Epidemiological research on late effects of therapy shows the necessity to aggregate chemotherapy agents to substance classes. This requires using conversion factors by substance classes.The aim of this study was to identify previously used conversion factors from the literature, to present a novel approach for additional factors, and to compare these approaches.A literature review was performed, which identified two main principles of deriving conversion factors: effect‐equivalence and equimolar. Thirty‐five articles presenting effect equivalence‐based factors in the widest sense were found in the literature. Ten articles presented the equimolar approach which can be applied to almost all chemotherapy substances. Based on a comprehensive list of treatment protocols used in German pediatric oncology, we derived alternative conversion factors from typical doses. We compared the conversion factors using Pearson correlation coefficients and linear regression. At least two types of conversion factor were available for each of the 49 substances included. The equivalent effect‐based and the typical dose‐based factors were highly correlated with a regression coefficient close to 1. The equimolar factors are independent.For substances for which no conversion factor based on some type of effect equivalence has been published so far, a factor based on a typical doses‐approach may be used in epidemiological late effects research. Doses aggregated based on the equimolar approach may not be compatible with doses aggregated based on equivalent effects. For this we obtained retrospective cumulative chemotherapy dose data for the former patients. It became clear that the number of different substances is too large for joint statistical analysis and some substances are applied rarely and therefore allow no statistical analysis. Other groups working on late effects of chemotherapy had been using the solution of grouping substances by pharmacologic principles,However, a comprehensive list of substances used in pediatric oncology and conversion factors for them turned out not to be available in the pertinent literature. Therefore, we initiated a very broad literature search aiming to collect factors having been used before in this field of late effects research, with a special focus on childhood cancer survivors. We are presenting the results of this search here.In addition, we developed an algorithm to fill in conversion factors for which conversion factors cannot be found in our literature search. This approach is based on typical doses determined from a comprehensive list of treatment protocols of the German Society for Pediatric Oncology and Hematology (GPOH) from the years 1970 to 2018.22.12 (except for asparaginase (L01XX), where International Units (IU)/m2 are generally used). The substances were classified into 12 substance groups according to the ATC.We included all substances with reported conversion factors in the pertinent literature and which have been used in treatment protocols for pediatric oncology in Germany since the 1970s.2.2The literature review was performed as a scoping review according to the PRISMA‐ScR  checklist.The search strategy with criteria for inclusion and exclusion was defined a priori .If available in the literature, we recorded the basis for the equivalence  and the evidence behind it. The term equivalence usually refers to the treatment effect or to different kinds of toxicity.2 is equivalent to a prednisone dose of 0.25 mg/m2. For anthracyclines, daunorubicin‐equivalents were considered equal to doxorubicin‐equivalents if only daunorubicin‐equivalents were available.For each substance group, the reference drug was chosen based on what was usually used in the literature. If a publication used a different reference drug, we recalculated the respective conversion factors accordingly. For glucocorticoids, prednisone was used as reference drug. Hydrocortisone‐equivalents were recalculated into prednisone‐equivalents by using a conversion factor of 0.25 according to the ‘Arzneimittelkommission der Deutschen Apotheker’,defined a priori) in this order: (1) most recent publication year and (2) articles which developed their own conversion factor based on their own literature review of equivalence. We present all factors found, indicating the one we selected /m2 were considered). The list contains cumulative doses per therapy block for each individual substance, each therapy arm and each protocol. This mainly excludes doses given in mg/kg. As a typical dose for a substance, we consider the mode, that is, the most frequently used dosage, for all doses across all therapy blocks and therapy arms of all protocols where that substance had been used. The substance Methotrexate, that is, has been used in 1515 therapy blocks over all treatment protocols and treatment arms with cumulative dosages from 12.5 to 24 000 mg/m2. Three hundred and seventy‐six out of these 1515 cumulative doses, and thus the most frequently used dosage (=mode), were 1000 mg/m2. The median dose was also 1000 mg/m2.For deriving the typical doses per substance we used a comprehensive list of all treatment protocols used in German pediatric oncology from the years 1970 to 2018,Dividing the typical doses of a reference substance and a substance yields the alternative conversion factor.The literature search did not provide conversion factors for all substances needed in our project. In order to decide whether we can justify filling the gaps with the typical dose‐approach, we compared them using Pearson correlation coefficients and linear regressions on a log scale for all substances where factors from different approaches were available. We provide regression coefficients with confidence limits for the individual (CLI) values and the mean predicted values (CLM). The statistical analyses were performed with SAS 9.4 (proc corr and proc reg).33.1Figure The articles were rather diverse regarding the study designs and the study populations. Most of them included children or adolescents with cancer or childhood/adolescent cancer survivors and referred to study populations in Europe or North America. Except for three articles,2 of chemotherapeutics to moles/m2 to quantify the total dose of a drug in each drug class (equimolar approach).In 10 out of the 151 articles screened which met all inclusion criteria, the authors suggested converting mg/mAs molecular weights are easily available for almost all chemotherapeutic substances, we were able to calculate additional factors using this approach ourselves. The factors were calculated using the molecular weights, independently from any article. The higher the molecular weight, the fewer active molecules are included per weight of a substance. Under this assumption, we calculated factors derived from the molecular weights for each substance as described above for the other factors.Twenty‐two further studies not (only) using the equimolar approach met all inclusion criteria. Additionally, we identified seven more articles of this type, which had been cited by the articles identified in the original search,Two3.2n = 6), hematotoxicity (n = 8), or hematological toxicity, non‐hematological toxicity and cardiotoxicity (n = 1). An isotoxicity factor of, for example, four for a substance means that one unit of the substance was considered four times more toxic than one unit of the reference substance.The basis of assessment of the different principles for effect equivalence other than the equimolar approach was usually not entirely clearly stated and rather diverse. For chemotherapeutics except glucocorticoids (24 articles), the conversion factors in the literature were mostly (15 out of 24) based on a principle which can be summarized by the term isotoxic. Toxicity referred to cardiotoxicity (n = 2) or potency (n = 1), respectively. These can be summarized by the term equipotency. One additional article justified a factor with both cardiotoxicity (isotoxicity) and potency (equipotency).Three articles (3 out of 24) referred to the intended effects of the chemotherapeutics, using the terms antitumor efficacy . In these articles, the following principles were used: potency  (n = 4), conventional glucocorticoid replacement therapy (n = 1), hydrocortisone‐equivalent dose (n = 2) or prednisone equivalent (n = 1). In three articles, the basis of the conversion factor was not stated explicitly. We conclude an underlying assumption of equipotency from the context and usage of the factors in the respective study.For glucocorticoids (11 articles), all factors in the literature were based on the concept of equipotency  as effect equivalence below. These factors are listed in Table 3.4The rationale behind the equimolar principle is that ‘a molecule of a given drug generally has one active site, whatever its weight. Even if a particular drug may have more than one active site per molecule, the error introduced by this hypothesis is probably lower than that introduced when summing the weights’.This permitted directly calculating equimolar conversion factors for all substances , presented in column 9 in Table For asparaginase (ATC‐code L01XX02) and pegylated asparaginase (L01XX24), we could not present factors derived from molecular weights because this chemical approach is not applicable to enzymes.3.5Typical doses were available for 41 (of the 49) substances, including the 11 substances for which no conversion factor based on effect equivalence had been found in the literature. The remaining eight (49 minus 41) substances have not been used in treatment protocols in German pediatric oncology since the 1970s. The resulting conversion factors are presented in Table 3.6Table The conversion factors based on effect equivalence derived from the literature ranged from 0.15 to 100, those derived from typical doses ranged from 0.05 to 43.8. More than 80% or 90%, respectively, of these conversion factors were between 0.1 and 10. The range of the factors based on molecular weights, 0.42–3.70, was much narrower.r = 0.83. Figure Comparing the factors, the correlation closest to 1 was found between the factors based on the principle of effect equivalence and the typical dose principle, r = 0.54 equimolar versus the principle of effect equivalence, and −0.32 equimolar compared to factors based on the typical doses). Supplementary Figures The correlations of the factors based on the equimolar principle with the other factors were considerably lower . We started out by considering aggregating drugs without using any conversion inappropriate because it is important to adjust for the different potencies or toxicities of the drugs, as stated in the literature, for example References According to the criteria from Munn et al.In a literature search, 35 articles were identified which used or justified conversion factors for 26 substances (excluding the 12 reference substances) based on principles which can be summarized as effect equivalence. The literature review did not yield such conversion factors for 11 relevant chemotherapeutic substances used in treatment protocols in Germany. Further 10 papers suggested the equimolar principle using molecular weights.r = 0.83) and on average close to being identical. The correlation of the factors derived from molecular weights with the other factors was moderate or close to zero.Comparing these three types of conversion factors, we found the effect equivalence‐based and the typical dose‐based factors to be highly correlated  or were not listed in PubMed or Web of Science as they were a guidelinen = 2).The literature search was rather broad in scope to ensure we would not miss any relevant papers. However, the fact that we identified 13 additional papers from reference lists or through prior knowledge indicates that there were potential blind spots. These 13 articles applied substance conversion outside the topic we were primarily interested in  or presented factors additional to or different from the ones cited in the original articles (n = 5). It is open for discussion to which extent conversion factors, specifically referring to cardiotoxic or hematotoxic side effects, are transferable to other endpoints in late effects research. No article referred explicitly to an equivalence of effect regarding second neoplasms, unless we equate hematoxicity with second leukemia. However, the literature search also showed that authors referring to different bases for their respective conversion factors nevertheless came to rather similar factors.The scientific basis for the various types of effect equivalence was often not stated (9 articles), did not yield a definitive factor : Besides technical reasons (incomplete availability of publications before 1985), we wanted to include the latest available literature in the literature review.When comparing typical dose‐based factors to effect equivalence‐based factors from the literature, the slope from linear regression was 0.74; however, ideally the slope should have been even closer to 1.2.It needs to be stated that all approaches described here are not meant for a clinical setting, for example, when replacing one substance with another for the treatment of an individual patient. This is also true for the typical dose approach. The protocols where the typical doses were derived from are used in clinical setting. However, the calculation of the typical doses was across all protocols and therefore all diagnoses, age groups and combination of drugs and was based on typical doses of single therapy blocks. Therefore, they need not be valid in all special clinical settings. Hence, all factors presented here are meant for and are particularly useful for population‐based epidemiological research. Practical application requires harmonizing units before applying the conversion factor. If height and weight of a patient are available, mg per kg can be converted into mg per mMost of the literature cited here was about post‐hoc treatment assessment in a late effects research setting.This study gives an overview over dose conversion factors of anticancer agents to a reference substance within their class by mode of action with an emphasis on usage in childhood cancer late effects research. We were able to present factors for 49 substances.As a first step we present results from a literature review. The factors based on effect‐equivalence seem to be more widely used and well justified for late effects research. For substances for which no such conversion factors could be found in the literature, we proposed factors from a rather simple approach, relating typical doses. Our original question had been whether we could justify filling in these factors for the 11 substances where we could not find an effect‐equivalence factor in the literature. Based on our comparison results we consider this justified. The data base for the typical dose approach was specific for pediatric oncology in Germany; therefore, our factors may not be directly applicable to adults or in other countries.A smaller number of articles suggested factors derived from molecular weights (equimolar). Obtaining such factors is straightforward using publicly available mole weights. These factors were basically independent from the other approaches. Results in terms of dose effects in late effects research using these factors may not be comparable to results based on data using effect equivalence‐based factors.These conversion factors in general and their underlying principles potentially have great value for research with aggregated data, such as epidemiological late effects research.Meike Ressing: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; software ; validation ; visualization ; writing – original draft (lead); writing – review and editing (lead). Cornelia Becker: Conceptualization ; investigation ; methodology ; project administration ; writing – review and editing (supporting). Christian Müller: Investigation ; resources ; validation ; writing – review and editing (supporting). Seyed Hamidreza Mahmoudpour: Methodology ; validation ; writing – review and editing (supporting). Gabriele Calaminus: Investigation ; resources ; writing – review and editing (supporting). Thorsten Langer: Investigation ; resources ; writing – review and editing (supporting). Friederike Erdmann: Funding acquisition ; supervision ; writing – review and editing (supporting). Mathias Voigt: Data curation ; writing – review and editing (supporting). Melanie Kaiser: Data curation ; writing – review and editing (supporting). Peter Kaatsch: Conceptualization ; funding acquisition ; resources ; supervision ; writing – review and editing (supporting). Maria Blettner: Conceptualization ; funding acquisition ; resources ; supervision ; writing – review and editing (supporting). Claudia Spix: Conceptualization ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; resources ; supervision ; visualization ; writing – original draft (supporting); writing – review and editing (supporting).This study was funded by Deutsche Krebshilfe, Grant Number: 70112099.Seyed Hamidreza Mahmoudpour is currently an employee of Merck KGaA, Darmstadt, Germany. All other authors have stated explicitly that there are no conflicts of interest in connection with this article.The authors declare that this work has been done in accordance to Wiley “Best Practice Guidelines on Research Integrity and Publishing Ethics” and that is has been performed in an ethical and responsible way, with no research misconduct, which includes, but is not limited to data fabrication and falsification, plagiarism, image manipulation, unethical research, biased reporting, authorship abuse, redundant or duplicate publication, and undeclared conflicts of interestData S1: Supporting Information.Click here for additional data file.


Individual decisions regarding how, why and when organisms interact with one another and with their environment scale up to shape patterns and processes in communities. Recent evidence has firmly established the prevalence of intraspecific variation in nature and its relevance in community ecology, yet challenges associated with collecting data on large numbers of individual conspecifics and heterospecifics have hampered integration of individual variation into community ecology.Nevertheless, recent technological and statistical advances in GPS‐tracking, remote sensing and behavioural ecology offer a toolbox for integrating intraspecific variation into community processes. More than simply describing where organisms go, movement data provide unique information about interactions and environmental associations from which a true individual‐to‐community framework can be built.By linking the movement paths of both conspecifics and heterospecifics with environmental data, ecologists can now simultaneously quantify intraspecific and interspecific variation regarding the Eltonian (biotic interactions) and Grinnellian  factors underpinning community assemblage and dynamics, yet substantial logistical and analytical challenges must be addressed for these approaches to realize their full potential.Across communities, empirical integration of Eltonian and Grinnellian factors can support conservation applications and reveal metacommunity dynamics via tracking‐based dispersal data. As the logistical and analytical challenges associated with multi‐species tracking are surmounted, we envision a future where individual movements and their ecological and environmental signatures will bring resolution to many enduring issues in community ecology. Eltonian and Grinnelian dynamics inferred from multi‐species tracking data. Panel (a): Tracks of five individuals from three different species reveal intraspecific and interspecific interactions through time, thereby enabling the construction of interaction topologies including both conspecifics and heterospecifics at an individual level. Panel (b): Interactions in the Eltonian arena can be mapped via temporally explicit tracks, allowing for spatiotemporal analysis of interactions across landscapes. Therefore, the intersection of tracks with environmental data (e.g. remote sensing layers) in space and time quantifies environmental associations and facilitates assessments of population‐ and community‐wide Grinnellian niche partitioning. The inhAlthough species‐level studies have greatly advanced our understanding about community assembly and functioning over the last half century, a rapidly growing body of literature has revealed the significance of intraspecific ecological variation in communities  and fine‐scale remotely sensed data now enable simultaneous quantification of how individuals interact with conspecifics, heterospecifics and their environment —referred to as Eltonian factors . Like their direct counterparts, indirect factors propagate along consumptive and non‐consumptive pathways Abrams, . Importa5n‐dimensional environmental niche space  now support the characterization of intraspecific and interspecific niche partitioning and plasticity. Such auxiliary information enables deeper insights into the behavioural mechanisms shaping niches  species of birds and mammals inhabiting the same region at the same time, specific marine regions critical for breeding and foraging at the community level were identified  will change interactions between conspecifics and heterospecifics , however, are poised to expand the catalogue of species and guilds possessing movement data . Importantly, some ecological actors will inevitably be missed since it is impossible to track every individual in a community, and these missed actors could induce substantial bias in community‐level inference. For example, Creel et al. . This challenge is not trivial, but new data fusion algorithms are rapidly developing (Brum‐Bastos et al., The benefit of multispecies tracking datasets appears to be increasingly acknowledged and employed. For example, by tracking several species at the same time in same region, key breeding and foraging sites and migratory routes needed to sustain multiple species at a single site can be identified (Davies et al., 10The historical duality between Eltonian and Grinnellian approaches in community ecology has hampered our understanding of how communities are organized in space and time Soberón, . AdditioThis individual‐level framework outlined here should complement—and needs to be complemented by—more traditional population‐ and community‐level approaches to estimate key parameters such as population size, phenotypic trait distribution and local community structure. Combining these data sources certainly entails overcoming major logistical, financial and analytical challenges, yet we are optimistic that recent and future technical developments are beginning to coalesce into Hutchinson's dream toolbox: a toolset capable of simultaneously quantifying multiple niche dimensions across multiple coexisting species. The intuitive, but still largely unexplored, integration between animal movements and community ecology has the potential to shed light on long‐standing questions in ecological theory and help develop new predictive models for gauging the effects of global change on communities.The authors have no conflict of interest to declare.All authors conceived the ideas, wrote collectively the manuscript and contributed critically to the drafts. We all gave final approval for publication.


We investigated the proteins of erythrocytes from stem cell transplantation patients and found decreased expression of band3 and C‐terminal‐truncated peroxiredoxin 2 (PRDX2) only during severe graft‐versus‐host disease (GVHD), using time‐of‐flight mass spectrometry (TOF‐MS) analysis and Western blotting. During the same period, PRDX2 dimerization and calpain‐1 activation were observed, indicating severe oxidative stress. We also found a putative cleavage site for calpain‐1 in the C‐terminal‐truncated site of PRDX2. Decreased band3 expression impairs the plasticity and stability of erythrocytes, and C‐terminal‐truncated PRDX2 induces irreversible dysfunction of antioxidant activity. These effects may exacerbate microcirculation disorders and the progression of organ dysfunction. GVHDgraft‐versus‐host diseasePRDXperoxiredoxinROSreactive oxygen speciesSCTstem cell transplantationTOF‐MStime‐of‐flight mass spectrometry1Reactive oxygen species (ROS) are not only produced in a basic electron transport system of the life activity, but also from various external stimulations and inflammation. Each organism has various mechanisms to remove ROS because they are harmful to the living body. However, excessive production of ROS beyond the permissible level causes serious damage to the living body. It has been reported that ROS are involved and play crucial roles during the progression of diabetes and arteriosclerotic diseases.Erythrocytes are the most powerful buffer against ROS‐induced in vivo oxidative stress. Erythrocytes carry oxygen from the lungs to the entire body and are consequently exposed to oxidative stress. The most abundant and powerful substance against oxidative stress in erythrocytes is peroxiredoxin 2 (PRDX2), which removes ROS via the glutathione system . PRDX2 hDuring hematopoietic stem cell transplantation (SCT), ROS are induced by acute and chronic graft‐versus‐host disease (GVHD) as well as anticancer drugs and irradiation used as a conditioning regimen. In a mouse model of acute GVHD, erythrocytes are subjected to oxidative stress .2We investigated the proteins of erythrocytes from SCT patients sequentially using SDS‐PAGE and found the decreased expression of band3 during severe GVHD, which was determined by Western blotting Figure . Band3 iExamination of the erythrocyte membrane proteins of 16 recipients of allogeneic hematopoietic cells showed that these changes were detected only in six cases with GVHD3 or 4, but not in cases without GVHD or with GVHD1 or 2 Table . Long‐teHowever, it has been reported that the erythrocyte protease calpain is activated through autoproteolysis induced by oxidative stress via calcium, and moves to the cell membrane from the cytoplasm . AdditioIt might be possible that degradation of band3 or PRDX2 is induced by physical impact, such as hemolysis or fragmentation in thrombotic microangiopathy. However, it is difficult to explain the constant cutting site of PRDX2 in this context. Furthermore, none of the patients we examined presented clinically determined thrombotic microangiopathy or hemolytic disorder before the onset of degradation of band3 and PRDX2.The decreased expression of band3 affects the plasticity and stability of erythrocytes , and theMasayuki Nagasawa conceptualized and designed the study, analyzed the data, and wrote the manuscript.The author declares no conflicts of interest.This study was performed in compliance with the ethical treatment policy of human and animal research participants and the Declaration of Helsinki. This study was approved by institutional review board of Tokyo Medical and Dental University.Written informed consent was obtained from the guardians of each SCT patient.Supporting InformationClick here for additional data file.


Anteroposterior (AP) and lateral shoulder projections are routinely performed as part of a post‐reduction shoulder x‐ray series in the emergency department (ED). Research has shown that these projections alone are insufficient to demonstrate post‐dislocation injuries, particularly Hill‐Sachs and Bankart lesions. These concomitant pathologies are best demonstrated on axial shoulder projections but are difficult to obtain in trauma patients with limited range of motion. The diagnostic quality and the pathology demonstrated by different projections is crucial so that doctors and other ED staff can triage patients appropriately, radiologists can report on the presence or absence of post‐dislocation shoulder injuries, and the orthopaedic team can plan for follow‐up or treatment. Different modified axial views were reported to improve the post‐dislocation pathology sensitivity in the shoulder series. However, all of these shoulder axial views require patient movement. The modified trauma axial (MTA) is an alternative projection that is suitable for trauma patients that does not depend on patient movement. This paper presents several cases where the MTA shoulder projection had clinical importance when used as part of the post‐reduction shoulder series in the ED or radiology department. The modified trauma axial (MTA) is an axial shoulder projection used to demonstrate shoulder dislocation related injuries to the humeral head and glenoid. It does not require patient movement and is easily implemented in an acute trauma situation. This pictorial essay presents several case studies where the MTA projection has been an important addition in the post‐reduction shoulder series. The glenohumeral joint is a commonly dislocated joint, and recurrent dislocations are a frequent problem.Radiographs before reduction are beneficial, particularly if it is the patient's first dislocation, as fractures are more commonly seen in the first dislocation than in recurrent incidences.A study conducted by Neep and Aziz found that using the AP and modified trauma axial (MTA) projection detected a significantly higher number of post‐dislocation pathologies than the shoulder AP and lateral scapula projections, particularly for greater tuberosity avulsions, Hill‐Sachs lesions, and glenoid rim fractures.The patient presented to ED for a query dislocation and pre‐ and post‐reduction radiographs were taken Fig. . The iniThe patient presented to the radiology department for follow‐up imaging with a known history of a shoulder dislocation and greater tuberosity fracture Fig. . On prevThe patient presented to ED with a dislocated shoulder and radiographs were requested pre‐ and post‐reduction Fig. . The HilThe patient was taken to ED with a shoulder dislocation that had been relocated in the ambulance Fig. . Hence, Most radiology departments routinely perform pre‐ and post‐reduction shoulder radiographs for patients with shoulder dislocations consisting of AP and lateral scapula shoulder projections.Riebe et al. reported that as many as four views  are required to improve the sensitivity of Hill‐Sachs lesions in post‐reduction radiographs.An accurate diagnosis made from post‐reduction radiographs is crucial for patient management. The addition of the axial projection can impact patient management and reduce the dependence on further imaging to detect shoulder injuries.Some scholars have reported using the modified axillary viewOne limitation of the MTA view that warrants consideration is the use of a 45° caudal angle of the x‐ray tube. Therefore, the anatomy of the MTA view is elongated. Thus, the image is not suitable for measurement. However, this should not stop one from using the MTA view because a CT scan is the imaging method of choice for the quantification of Hill‐Sachs and Bankart lesions after initial radiographs.Lastly, further research on the clinical impact of the MTA view with a larger cohort is needed. An MTA view in‐service training for ED staff, radiologists, orthopaedic doctors, and radiographers may be required to understand its application and use within the post‐reduction shoulder series.The MTA plays an important role in demonstrating shoulder dislocation pathologies in the case studies presented in this pictorial essay. The MTA view alone detected a Hill‐Sachs lesion and Bankart lesion in case studies 1 Fig.  and 2 F, respectNo funds, grants, or other support were received. The authors declare no conflict of interest.


Arrhythmogenic cardiomyopathy (ACM) is a genetic heart muscle disease, structurally characterized by progressive fibro‐fatty replacement of the normal myocardium and clinically by ventricular arrhythmias (VAs). Predominantly thanks to the use of cardiac magnetic resonance, we have learnt that the spectrum of the disease encompasses not only the classical right ventricular phenotype, but also biventricular and left dominant variants. Sport activity contributes to the phenotypic expression and progression of ACM and may trigger life‐threatening VAs and sudden cardiac death (SCD). We conducted a review of the literature about ACM and its implications in Sport Cardiology and summarized the main findings in this topic. Early identification of affected athletes through preparticipation screening (PPS) is fundamental but, while classical right‐ventricular or biventricular phenotypes are usually suspected because of electrocardiogram (ECG) and echocardiographic abnormalities, variants with predominant left ventricular involvement are often characterized by normal ECG and unremarkable echocardiography. Usually the only manifestations of such variants are exercise‐induced VAs and for this reason exercise testing may empower the diagnostic yield of the PPS. Patients with ACM are not eligible to competitive sports activity, but low‐to‐moderate intensity physical activity under medical supervision is possible in most cases. Preparticipation screening offers the possibility to early identify asymptomatic athletes with arrhythmogenic cardiomyopathy through step‐by‐step research for signs and subsequent further investigations. After the diagnosis, competitive sport activity should be discouraged and patients should be guided to safe recreational physical activity. CMR, cardiac magnetic resonance; ICD, implantable cardioverter defibrillator; PVBs, premature ventricular beats. Indeed, ventricular fibrillation (VF) and sudden cardiac death (SCD) are typical of younger patients, may be because of acute electrical instability in the context of the so‐called “hot‐phases,” which are characterized by myocarditis‐mediated bouts of acute myocyte necrosis. On the other hand, sustained monomorphic ventricular tachycardia (VT) is common in older patients and originates from re‐entry circuits around stable fibro‐fatty myocardial scars.Preparticipation screening (PPS) offers the possibility to early identify asymptomatic patients with ACM and, by modulating the intensity and type of exercise, to reduce the risk of exercise‐related disease progression and SCD.In this review we addressed the new diagnostic criteria of ACM that take into account the evolving spectrum of the disease, the diagnosis of the disease  in the athlete and the implications for competitive sports eligibility.2The diagnosis of ACM is challenging, because of the absence of a single sensitive and specific test. Therefore, formal criteria based on a multiparametric approach were introduced to facilitate and standardize the clinical diagnosis. In 1994 the first Task ForceThe “Padua criteria” are organized in two different sets of criteria to identify, respectively, clinical signs of RV and LV involvement. In both sets, the traditional organization in six diagnostic categories was maintained, which includes morpho‐functional changes, tissue characterization, repolarization and depolarization ECG abnormalities, VAs and family history/genetic testing. The criteria are differentiated in “major” and “minor” depending on their specificity for diagnosing ACM. Only one major or minor criterion for each category can be considered Table .An important element of novelty of the Padua Criteria is that, because ACM is a structural disease, at least one morpho‐functional or structural criterion needs to be satisfied. Another is that noninvasive tissue characterization by CMR and invasive endomyocardial biopsy were placed side‐by‐side.According to the number of positive LV and RV criteria, the diagnosis can be one of the three different phenotypic variants: “right‐dominant” variant, which is the classical form with RV involvement; “biventricular” variant, with involvement of both ventricles; “left‐dominant” variant, with only LV involvement. After that, the likelihood of disease is defined according to the combination of major and minor criteria that are fulfilled, and the diagnosis can be “definite,” “borderline,” or “possible” Figure . The dia3PPS is a fundamental tool for early diagnosing ACM in asymptomatic athletes Table . Medicalpersonal history positive for pericarditis and/or myocarditis, especially if recurrent, should be thoroughly investigated in suspecting a “hot phase” of ACM.family history should be extended beyond first‐degree relatives. Relevant information is unexplained sudden death at young age, ICD implantation, and of course familial ACM.A ECG shows abnormalities in the majority of ARVC patients and in up to 90% of patients with a history of VT.The ET is essential in placing the suspicion of ACM. This is because VAs, sometimes even isolated PVBs, are often the first manifestation in both right‐ and left‐dominant forms. PVBs or VT with LBBB/superior axis morphology are more specific for ACM as they originate from the RV free wall or interventricular septum. Instead, VAs with a LBBB/inferior axis morphology are less specific (minor criterion), and are often idiopathic, in keeping with an origin from RV outflow tract, but they should still be investigated at least by echocardiography.The 24‐h ECG Holter  including a training session is an important second‐level test not only to record the VA burden over a longer period, but also to assess the occurrence of arrhythmias during the habitual sports activity.In case of abnormal finding at PPS, second and third level investigations should be performed. The Echocardiography and CMR are prescribed respectively as second and third level imaging investigations in case ACM is suspected.In comparison to echocardiography, CMR allows a more precise assessment of RV function and provides tissue characterization.Molecular genetic testing is indicated in an index athlete who already fulfils phenotypic diagnostic criteria at least for borderline ACM, while it should not be used in the absence of a clear clinical suspicion  because the results of genetic testing are often not clear‐cut and may be confounding.4The incidence of SCD due to ARCV has been significantly reduced thanks to the routine use of ECG during PPS, leaving left‐dominant variants as an emergent substrate at post‐mortem investigation.ECG in ALVC is often normal. The presence of TWI in V4‐V6 is considered an abnormal finding in an athlete's ECG, and it is a criterion for ALVC . The physiological ventricular remodeling in athletes is characterized by increase in QRS voltages. Therefore, the presence of low‐amplitude QRS complexes (peak to peak < 0.5 mV) in the limb leads should be considered suspicious even if it is not included in the current recommendations for interpretation of the athlete's ECG, because it can reflect the decrease of LV myocardial mass by fibro‐fatty replacement and requires additional examination to be performed.The echocardiography is often normal because the wave‐front of myocardial loss and fibrofatty replacement in the LV wall proceeds from the epicardium to the endocardium, typically in the inferior‐lateral wall, sparing the subendocardial layer which mostly accounts for the regional LV wall contractility.In ALVC, the ET and any subsequent Holter ECG (preferably 12‐lead and including high‐intensity training session) are key examinations for raising the suspicion of ALVC  and volume . Moreover, at multivariate analysis intensity rather than duration was independently associated with arrhythmias.The fact that engaging in competitive sports activity may be dangerous does not mean that any exercise should be avoided. Young subjects diagnosed with ACM should be guided to safe recreational physical activity prescribed by experienced physician on a case‐by‐case basis and avoid a sedentary behavior that could carry adverse consequences . Lie et al. evaluated the relative effects of exercise and intensity, and their relationship to disease progression in ARVC patients and family members with positive genotype. They observed more prevalent VAs in patients performing exercise of greater intensity (74% vs. 20% performed > 6 METS, 6The early diagnosis of ACM through PPS in competitive athletes may reduce the disease progression and the risk of SCD, modifying its natural history. Sometimes the diagnosis of ACM can be challenging because of the potential overlap with the athlete's heart and the low sensitivity of resting ECG and echocardiography in ALVC. ET can empower the diagnostic yield of the PPS with the detection of exercise‐induced PVBs that are often the only manifestation of the disease. In the athlete, the diagnosis of ALVC relies on the tissue characterization ability of CMR, which should be performed in selected cases with high‐risk VAs irrespective of ECG and echocardiography findings. Finally, although competitive sports may be dangerous in patients with ACM, prescribing adequate and safe exercise by experienced physicians should prevent the diagnosis of ACM from becoming a reason for sedentariness that can be equally harmful for patients.The authors declare no conflict of interest.


Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home‐range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional‐scale patterns of spread in a novel region . It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence‐only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much‐needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management.Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision‐making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence‐only distribution data and minimal demographic information to predict the spread of roe deer ( This study developed a hybrid species distribution model using presence‐only distribution data and minimal demographic information to predict the spread of roe deer in Great Britain. The model utilised a novel approach to representing the environment by constraining the size of habitat patches to the home‐range area of an individual, simplifying population dynamics to a set of generic rules. The model was evaluated and applied to predict regional‐scale patterns of spread in a novel region, demonstrating the utility of presence‐only data for predicting animal species' spread while retaining environmental detail and biological realism. Generalising the continuous raster grid produced by a correlative model into a landscape of patches typically requires the application of a suitability threshold. Neighbouring cells with suitability values at or above this threshold are then aggregated to delineate discrete patches of suitable habitat embedded in a matrix of less hospitable environments Berec, . Patch‐bCurrently, there are limited tools available to implement conceptually simple hybrid models  population in Wales, UK provides a good example of a wildlife management scenario that can be informed by predictive modelling. Although native to Britain, the numbers and geographic range of roe deer have expanded rapidly over recent decades due to reduced persecution, afforestation and the absence of natural predators  are used to produce a habitat suitability map from a correlative species distribution model. This map is then generalised to represent the environment in a hybrid model as a landscape of small patches, based on the home‐range area of an individual . Basic demographic and dispersal information are used in simulations to predict regional‐scale patterns of population spread as a function of the size, quality and connectivity of individual‐sized patches. The hybrid model is first evaluated using observations of historical distribution change in England and Scotland and then applied to predict the population spread of roe deer in a novel region, Wales.To be an effective tool for management, it was important that our model outputs were easily interpretable by practitioners and produced at a fine enough spatial resolution to identify potentially vulnerable landscape features . Achieving temporal accuracy was considered less critical, as predicting the relative timing of colonisation events (e.g. region X is likely to be colonised before region Y) would be sufficient to set management priorities (e.g. targeted surveillance in region X). The objectives were to (1) evaluate the efficacy of representing the environment as a landscape of individual‐sized patches to predict patterns of population spread, (2) test different methods of generalising a habitat suitability map into individual‐sized patches, (3) predict the suitability of habitat and potential future range of the roe deer population in Wales and (4) predict the relative timing of colonisation events for roe deer in Wales, assuming the population realises its potential range.22.12), divided into two regions: England and Scotland , where roe deer populations are well established and Wales , where numbers are much lower  habitat suitability was estimated from a populated region (England and Scotland) using a correlative species distribution model, (Step 2) the resultant habitat suitability map (HSM) was generalised into a landscape of individual‐sized patches to represent the environment in a hybrid model, (Step 3) demographic parameters were simplified to simulate patch occupancy for multiple time steps, (Step 4) model evaluation was performed using historic distribution data and (Step 5) the model was applied to a novel region . Predictions of population spread were based on simulations made using a mechanistic modelling platform, RangeShifter  model implemented with the ‘dismo’ package  and regional wildlife trusts in Wales (Appendix n = 3843). The records from Wales (n = 37) were used for the evaluation of model performance in the Wales region only.Data on roe deer sightings were taken for the period 1953–2016 from the National Biodiversity Network Gateway , roads (Ordnance Survey (OS) Meridian™ 2; www.ordnancesurvey.co.uk, 10 m resolution), terrain  and climate . Environmental data were resampled to 100 m cell rasters to predict habitat suitability at a fine resolution, which was necessary for delineating irregularly shaped individual‐sized patches in the subsequent hybrid model  and 100,000 points were used for England and Scotland (1.2 points/km2). Linear, quadratic, hinge and product feature classes were used as well as the default value of 1.0 for the regularisation multiplier  but one, then evaluates the model on that point and repeats until all points have been evaluated . We therefore evaluated four methods: 1) Grid, 2) Voronoi, (3) Contiguity and (4) Voronoi‐Contiguity (Vor‐Con) within the Historic Area of Expansion . Studies have shown that roe deer home ranges generally decrease with increasing population density and habitat quality . Voronoi polygons were created to define the geometry of patches . Polygons were converted from a vector to a raster and then back to a vector to ensure patch boundaries aligned with cells of the HSM. The mean value of cells within patches was calculated and patches with a mean suitability below the threshold were removed  were removed and the mean value of cells within the remaining patches was calculated . It was presented to demonstrate the importance of patch size in the case study and to illustrate the conceptual basis of the Vor‐Con method.HSM cells with suitability values below the threshold were removed. Suitable cells that neighboured other suitable cells in any of the eight cardinal directions were considered part of the same patch Figure , panel c2), medium (0.06–1 km2) and large (>1 km2). Small patches were removed and medium patches were not modified. Large patches were divided into smaller patches using Voronoi polygons following the same procedure as the Voronoi method. Point features were created at an approximate density of 1 point per km2 . The points were distributed according to the probability distribution described by the HSM, using only the cells within the boundaries of large patches. Voronoi polygons were created and converted from a vector to a raster and then back to a vector to ensure patch boundaries aligned with cells of the HSM. The mean value of cells within patches was calculated  to simulate range expansions as a function of stochastic interactions between individuals and the environment . Density dependence acted on survival and fecundity and was implemented in RangeShifter as an exponential decay:xi is a parameter for survival or fecundity, xi,0 is the maximum value of the parameter at low densities, b is the strength of density dependence and Nt is the total number of individuals in the local population at time t  occupied patches produced a number of dispersers proportional to patch size and quality, (ii) dispersers interacted with the landscape to transfer between patches and (iii) dispersers settled in patches occupied below carrying capacity. This was implemented in RangeShifter as an asexual stage‐structured population model based on a Leslie transition matrix  of the scaled HSM using the embedded Stochastic Movement Simulator (SMS). The SMS simulated movement as a series of discrete nearest‐neighbour steps across a cost surface, similar to the Least Cost Path , which assumed movement costs were inversely related to habitat suitability. Dispersers would therefore be less likely to move through low‐quality habitat.Dispersal between patches was modelled as three discrete phases of emigration, transfer and settlement. All juveniles that survived and developed into dispersers, emigrated from their natal patch. Movement during the transfer phase was modelled at the finer scale  of the species and their tendency to follow a correlated random walk, defined as directional persistence  and calculating the True Skill Statistic  in both England and Scotland  and Wales (0.76 ± 0.25) regions. Three variables: woodland distance, land cover and woodland cover achieved the highest regularised training gain when isolated in the jackknife test , as indicated by mean TSS values >0.4 when simulations were compared to the observed species range (ObsRange) and to the distribution of species presences  of the total area. The population is predicted to spread through the centre of Wales, initially progressing from east to west. The range front is estimated to advance in the northern half of Wales towards the northeast and in the southern half of the country towards the southwest  covered approximately 26% 5268 km km2 of t4We developed a hybrid species distribution model to predict regional‐scale patterns of population spread for an animal species from limited distribution and demographic data. A correlative, MaxEnt model  values attained in this study are similar to those reported from previous studies of roe deer in the UK by Acevedo et al. (2) that is a 100 times finer than that used by Croft et al. (2) and 10,000 times finer than Acevedo et al. (2). The high resolution of the output was critical to subsequent modelling stages, as it allowed the suitability map to be generalised into a landscape of patches based on the home range area of an individual roe deer. This captured structural details of the landscape, such as the size, distribution and geometry of habitat patches, that are important in shaping patterns of population spread , o et al.  (0.64) ao et al.  (0.9). Ft et al.  (1 km2) o et al.  and Crofo et al.  . We feel that this was reasonable based on historic patterns of expansion in Great Britain and Europe and the biological characteristics of the species. The environmental conditions in Wales are similar to England and Scotland, where the roe deer population is widely distributed. The rates of annual survival and reproduction are also high for roe deer, so local extinctions are unlikely ; formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing . John R. Healey: Conceptualization (supporting); supervision (supporting); writing – review and editing (supporting). Line S. Cordes: Conceptualization (supporting); formal analysis (supporting); methodology (supporting); supervision (supporting); visualization (supporting); writing – review and editing (supporting). Andrew J. Davies: Formal analysis (supporting); methodology (supporting). Graeme Shannon: Conceptualization ; funding acquisition (lead); investigation (supporting); methodology (supporting); supervision (lead); writing – original draft (supporting); writing – review and editing .The authors declare that no competing interests exist.Appendix S1Click here for additional data file.