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Existing weather models are known to have poor skill over Africa, where there are regular threats of drought and floods that present significant risks to people's lives and livelihoods. Improved precipitation forecasts could help mitigate the negative effects of these extreme weather events, as well as providing significant financial benefits to the region. Building on work that successfully applied a state-of-the-art machine learning method (a conditional Generative Adversarial Network, cGAN) to postprocess precipitation forecasts in the UK, we present a novel way to improve precipitation forecasts in East Africa. We address the challenge of realistically representing tropical convective rainfall in this region, which is poorly simulated in conventional forecast models. We use a cGAN to postprocess ECMWF high resolution forecasts at 0.1 degree resolution and 6-18h lead times, using the iMERG dataset as ground truth, and investigate how well this model can correct bias, produce reliable probability distributions and create samples of rainfall with realistic spatial structure. We will also present performance in extreme rainfall events. This has the potential to enable cost effective improvements to early warning systems in the affected areas.

Skillful weather and climate forecasts, if utilized effectively, have the potential to improve preparedness and disaster risk reduction. Forecast-based Action (FbA) is a framework for aiding decisions on preparedness in advance of weather/climate hazards, through use of forecasts. Here, we present a summary of research results and pilot project work within the Arid and Semi-Arid Land (ASAL) areas of Kenya conducted under the Towards Forecast-based Preparedness and Action (ForPAc) project. We also present opportunities for scaling up FbA across the Greater Horn of Africa region through leveraging on connected projects and initiatives like Down2Earth. Skill assessment of a pool of weather/climate models has established the most skilful multi-model combinations for monthly-seasonal timescale. Co-production initiatives between forecast users and producers established the forecast variables best aligned with Kenya’s existing Drought Early Warning Systems (DEWS); Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI) and soil moisture, as well as optimum forecast delivery time required by the DEWS processes. Our analysis shows that rainfall forecasts have skill across ‘seamless’ sub-seasonal to seasonal lead times, offering the potential to improve the anticipatory actions within the DEWS of Kitui county of Kenya. Working with multiple stake-holders from across local and national government, humanitarian agencies, forecasting services and climate researchers, we have explored the potential for a more anticipatory, proactive DEWS using forecast information. The Down2Earth project, which aims at translating climate information for adaptation and climate-resilience across decision-making levels is leveraging on gains of ForPAc by advancing FbA approaches within the rural communities of Kenya, Somalia and Ethiopia. To facilitate the institutionalization of FbA, we have developed a regional roadmap to guide implementation within National, regional and international humanitarian actors.

The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness for such events. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used, making it important to better understand the strengths and weaknesses of such models. Here we define a simple statistical forecast model, which is used as a tool to shed light on the dynamics that link SSTs and rainfall across time and space, as well as on why such models sometimes fail. Our model is a linear regression, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: too-wet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by changes in the IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions.

Summary Background Non-facility-based antiretroviral therapy (ART) delivery for people with stable HIV might increase sustainable ART coverage in low-income and middle-income countries. Within the HPTN 071 (PopART) trial, two interventions, home-based delivery (HBD) and adherence clubs (AC), which included groups of 15–30 participants who met at a communal venue, were compared with standard of care (SoC). In this trial we looked at the effectiveness and feasibility of these alternative models of care. Specifically, this trial aimed to assess whether these models of care had similar virological suppression to that of SoC 12 months after enrolment. Methods This was a three-arm, cluster-randomised, non-inferiority trial, done in two urban communities in Lusaka, Zambia included in the HPTN 071 trial. The two communities were split into zones, which were randomly assigned (1:1:1) to the three treatment strategies: 35 zones to the SoC group, 35 zones to the HBD group, and 34 zones to the AC group. ART and adherence support were delivered once every 3 months at home for the HBD group, in groups of 15–30 people in the AC group, or in the clinic for the SoC group. Adults with HIV who were receiving first-line ART for at least 6 months, virally suppressed using national HIV guidelines in the last 12 months, had no other health conditions requiring the clinicians attention, live in the study catchment area, and provided written informed consent, were eligible for inclusion. The primary endpoint was viral suppression at 12 months (with a 6 month final measurement window [ie, 9–15 months]), defined as less than 1000 HIV RNA copies per mL, with a non-inferiority margin of 5%. Findings Between May 5 and Dec 19, 2017, 9900 individuals were screened for inclusion, of whom 2489 (25·1%) participants were enrolled into the trial: 781 (31%) in the SoC group, 852 (34%) in the HBD group, and 856 (34%) in the AC group. A higher proportion of participants had viral load measurements in the primary outcome window in the HBD (581 [61%]of 852 participants) and AC (485 [57%] of 856 participants) groups than in the SoC (390 [50%] of 781 patients) group (p=0·0021). Of the 1096 missing observations, 152 (13·8%) were attributable to either deaths (25 [16%] participants), relocations (37 [24%] participants), or lost to follow-up (90 [59%]); 690 (63·0%) participants had viral load results outside the window period; and 254 (23·2%) did not have a viral load result. The prevalence of viral suppression was estimated to be 98·3% (95% CI 96·6 to 99·7) in the SoC group, 98·7% (97·5 to 99·6) in the HBD group, and 99·2% (98·4 to 99·8) in the AC group. This gave an estimated risk difference of 0·3% (95% CI −1·5 to 2·4) for the HBD group compared with the SoC group and 0·9% (−0·8 to 2·8) for the AC group compared with the SoC group. There was strong evidence (p

We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

Wind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly-to-seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub-daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub-optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub-daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day-to-day variability.

While seasonal outlooks have been operational for many years, until recently the extended-range timescale referred to as subseasonal-to-seasonal (S2S) has received little attention. S2S prediction fills the gap between short-range weather prediction and long-range seasonal outlooks. Decisions in a range of sectors are made in this extended-range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications-relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision-making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended-range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

In 2018 the long rains season in Kenya (March-May) was the wettest ever recorded. The country experienced several multi-day heavy rainfall episodes, leading to dam collapse, land and mudslides. 186 people died due to flooding and 300,000 were left displaced. The Kenya Meteorological Department issued several advisories during the season that warned of heavy rainfall events a few days before their occurrence. Ahead of this no warnings were given.However subseasonal forecasts gave strong indications of the heaviest rainfall episodes, several weeks in advance. With this extra lead time, preparedness actions may have been taken in order to reduce flood risk and save lives. To this end, the ForPAc project (Toward Forecast-Based Preparedness Action) has been working in partnerships across Kenya and the UK to evaluate and build trust in subseasonal forecasts, and explore preparedness actions which could be taken in response. Most recently ForPAc has been granted access to real-time subseasonal data as part of phase two of the S2S pilot.In this presentation we will first show analysis of the S2S hindcasts over East Africa, demonstrating the relatively high levels of subseasonal forecast skill and linking this to a strong MJO teleconnection that models capture relatively well.In the second part we will describe work with stakeholders to co-design forecast products derived from the S2S data, concluding with a report on the forecasts for the ongoing 2020 long rains season and an evaluation of the way in which these have influenced disaster preparedness.

New seasonal retrospective forecasts for 1901-2010 show that skill for predicting ENSO, NAO and PNA is reduced during mid-century periods compared to earlier and more recent high-skill decades. Forecasts of seasonal climate anomalies using physically-based global circulation models are routinely made at operational meteorological centers around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years which are used to estimate skill and to calibrate the forecasts. Hindcasts are usually produced over a period of around 20-30 years. However, recent studies have demonstrated that seasonal forecast skill can undergo pronounced multi-decadal variations. These results imply that relatively short hindcasts are not adequate for reliably testing seasonal forecasts and that small hindcast sample sizes can potentially lead to skill estimates that are not robust. Here we present new and unprecedented 110-year-long coupled hindcasts of the next season over the period 1901 to 2010. Their performance for the recent period is in good agreement with those of operational forecast models. While skill for ENSO is very high during recent decades, it is markedly reduced during the 1930s to 1950s. Skill at the beginning of the 20th Century is, however, as high as for recent high-skill periods. Consistent with findings in atmosphere-only hindcasts, a mid-century drop in forecast skill is found for a range of atmospheric fields including large-scale indices such as the NAO and the PNA patterns. As with ENSO, skill scores for these indices recover in the early 20th Century suggesting that the mid-century drop in skill is not due to lack of good observational data. A public dissemination platform for our hindcast data is available and we invite the scientific community to explore them.

Climate services entail providing timely and tailored climate information to end-users in order to facilitate and improve decision-making processes. Climate services are instrumental in socio-economic development and benefit substantially from interdisciplinary collaborations, particularly when including Early Career Researchers (ECRs). This commentary critically discusses deliberations from an interdisciplinary workshop involving ECRs from the United Kingdom and South Africa in 2017, to discuss issues in climate adaptation and climate services development in water resources, food security and agriculture. Outcomes from the discussions revolved around key issues somewhat marginalized within the broader climate service discourse. This commentary discusses what constitutes “effective” communication, framings (user framings, mental models, narratives, co-production) and ethical dimensions in developing climate services that can best serve end-users. It also reflects on how ECRs can help tackle these important thematic areas and advance the discourse on climate services.

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this paper. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving a greater attention than 5 to 10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and to other components of the Earth system as well as the overall computational efficiency of representing model uncertainty.

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service. The authors acknowledge funding support from the RESILIENCE (CGL2013-41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion. We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau Manubens for their technical support at different stages of this project.

Decadal climate prediction is a branch of climate modelling with the theoretical potential to anticipate climate impacts years in advance. Here we present analysis of the ENSEMBLES decadal simulations, the first multi-model decadal hindcasts, focusing on the skill in prediction of temperature and precipitation - important for impact prediction. Whilst previous work on this dataset has focused on the skill in multi-year averages, we focus here on the skill in prediction at smaller timescales. Considering annual and seasonal averages, we look at correlations, potential predictability and multi-year trend correlations. The results suggest that the prediction skill for temperature comes from the long-term trend, and that precipitation predictions are not skilful. The potential predictability of the models is higher for annual than for seasonal means and is largest over the tropics, though it is low everywhere else and is much lower for precipitation than for temperature. The globally averaged temperature trend correlation is significant at the 99% level for all models and is higher for annual than for seasonal averages; however, for smaller spatial regions the skill is lower. For precipitation trends, the correlations are not skilful on either annual or seasonal scales. Whilst climate models run in decadal prediction mode may be useful by other means, the hindcasts studied here have limited predictive power on the scales at which climate impacts and the results presented suggest that they do not yet have sufficient skill to drive impact models on decadal timescales. © 2012 IOP Publishing Ltd.

The basic level air photo interpretation and mapping of the National Mapping Programme can provide a guide to the existence and form of complex archaeological landscapes such as those found in the Yorkshire Dales. Using the evidence from the NMP a small area (80 ha) of upper Wharfedale was chosen to be the focus of a continuing education air photo interpretation training course. Reinterpretation and mapping of the features at a larger scale, along with the use of limited field observations has revealed the major stages in the development of the landscape from the Iron Age to the post-medieval periods. The value and accuracy of the original mapping has been demonstrated, whilst also showing the benefits of getting out on the ground. The potential of using this methodology to assist the understanding and management of extensive earthwork landscapes is considered.

Lateral gene transfers (LGT) (also called horizontal gene transfers) have been a major force shaping the Thermosipho africanus TCF52B genome, whose sequence we describe here. Firmicutes emerge as the principal LGT partner. Twenty-six percent of phylogenetic trees suggest LGT with this group, while 13% of the open reading frames indicate LGT with Archaea .

Saturated thalassic brines are among the most physically demanding habitats on Earth: few microbes survive in them. Salinibacter ruber is among these organisms and has been found repeatedly in significant numbers in climax saltern crystallizer communities. The phenotype of this bacterium is remarkably similar to that of the hyperhalophilic Archaea (Haloarchaea). The genome sequence suggests that this resemblance has arisen through convergence at the physiological level (different genes producing similar overall phenotype) and the molecular level (independent mutations yielding similar sequences or structures). Several genes and gene clusters also derive by lateral transfer from (or may have been laterally transferred to) haloarchaea. S. ruber encodes four rhodopsins. One resembles bacterial proteorhodopsins and three are of the haloarchaeal type, previously uncharacterized in a bacterial genome. The impact of these modular adaptive elements on the cell biology and ecology of S. ruber is substantial, affecting salt adaptation, bioenergetics, and photobiology.

Background Since Darwin's Origin of Species, reconstructing the Tree of Life has been a goal of evolutionists, and tree-thinking has become a major concept of evolutionary biology. Practically, building the Tree of Life has proven to be tedious. Too few morphological characters are useful for conducting conclusive phylogenetic analyses at the highest taxonomic level. Consequently, molecular sequences (genes, proteins, and genomes) likely constitute the only useful characters for constructing a phylogeny of all life. For this reason, tree-makers expect a lot from gene comparisons. The simultaneous study of the largest number of molecular markers possible is sometimes considered to be one of the best solutions in reconstructing the genealogy of organisms. This conclusion is a direct consequence of tree-thinking: if gene inheritance conforms to a tree-like model of evolution, sampling more of these molecules will provide enough phylogenetic signal to build the Tree of Life. The selection of congruent markers is thus a fundamental step in simultaneous analysis of many genes. Results Heat map analyses were used to investigate the congruence of orthologues in four datasets (archaeal, bacterial, eukaryotic and alpha-proteobacterial). We conclude that we simply cannot determine if a large portion of the genes have a common history. In addition, none of these datasets can be considered free of lateral gene transfer. Conclusion Our phylogenetic analyses do not support tree-thinking. These results have important conceptual and practical implications. We argue that representations other than a tree should be investigated in this case because a non-critical concatenation of markers could be highly misleading.

Background When organismal phylogenies based on sequences of single marker genes are poorly resolved, a logical approach is to add more markers, on the assumption that weak but congruent phylogenetic signal will be reinforced in such multigene trees. Such approaches are valid only when the several markers indeed have identical phylogenies, an issue which many multigene methods (such as the use of concatenated gene sequences or the assembly of supertrees) do not directly address. Indeed, even when the true history is a mixture of vertical descent for some genes and lateral gene transfer (LGT) for others, such methods produce unique topologies. Results We have developed software that aims to extract evidence for vertical and lateral inheritance from a set of gene trees compared against an arbitrary reference tree. This evidence is then displayed as a synthesis showing support over the tree for vertical inheritance, overlaid with explicit lateral gene transfer (LGT) events inferred to have occurred over the history of the tree. Like splits-tree methods, one can thus identify nodes at which conflict occurs. Additionally one can make reasonable inferences about vertical and lateral signal, assigning putative donors and recipients. Conclusion A tool such as ours can serve to explore the reticulated dimensionality of molecular evolution, by dissecting vertical and lateral inheritance at high resolution. By this, we mean that individual nodes can be examined not only for congruence, but also for coherence in light of LGT. We assert that our tools will facilitate the comparison of phylogenetic trees, and the interpretation of conflicting data.

An analysis of the taxonomic distribution, evolutionary rates and phylogenies of 65 proteins related to the nuclear pore complex shows high heterogeneity of evolutionary rates between these proteins., Background The origin of the nuclear compartment has been extensively debated, leading to several alternative views on the evolution of the eukaryotic nucleus. Until recently, too little phylogenetic information was available to address this issue by using multiple characters for many lineages. Results We analyzed 65 proteins integral to or associated with the nuclear pore complex (NPC), including all the identified nucleoporins, the components of their anchoring system and some of their main partners. We used reconstruction of ancestral sequences of these proteins to expand the detection of homologs, and showed that the majority of them, present all over the nuclear pore structure, share homologs in all extant eukaryotic lineages. The anchoring system, by contrast, is analogous between the different eukaryotic lineages and is thus a relatively recent innovation. We also showed the existence of high heterogeneity of evolutionary rates between these proteins, as well as between and within lineages. We show that the ubiquitous genes of the nuclear pore structure are not strongly conserved at the sequence level, and that only their domains are relatively well preserved. Conclusion We propose that an NPC very similar to the extant one was already present in at least the last common ancestor of all extant eukaryotes and it would not have undergone major changes since its early origin. Importantly, we observe that sequences and structures obey two very different tempos of evolution. We suggest that, despite strong constraints that froze the structural evolution of the nuclear pore, the NPC is still highly adaptive, modern, and flexible at the sequence level.

The origin of the nuclear compartment has been extensively debated, leading to several alternative views on the evolution of the eukaryotic nucleus. Until recently, too little phylogenetic information was available to address this issue by using multiple characters for many lineages. We analyzed 65 proteins integral to or associated with the nuclear pore complex (NPC), including all the identified nucleoporins, the components of their anchoring system and some of their main partners. We used reconstruction of ancestral sequences of these proteins to expand the detection of homologs, and showed that the majority of them, present all over the nuclear pore structure, share homologs in all extant eukaryotic lineages. The anchoring system, by contrast, is analogous between the different eukaryotic lineages and is thus a relatively recent innovation. We also showed the existence of high heterogeneity of evolutionary rates between these proteins, as well as between and within lineages. We show that the ubiquitous genes of the nuclear pore structure are not strongly conserved at the sequence level, and that only their domains are relatively well preserved. We propose that an NPC very similar to the extant one was already present in at least the last common ancestor of all extant eukaryotes and it would not have undergone major changes since its early origin. Importantly, we observe that sequences and structures obey two very different tempos of evolution. We suggest that, despite strong constraints that froze the structural evolution of the nuclear pore, the NPC is still highly adaptive, modern, and flexible at the sequence level.

When organismal phylogenies based on sequences of single marker genes are poorly resolved, a logical approach is to add more markers, on the assumption that weak but congruent phylogenetic signal will be reinforced in such multigene trees. Such approaches are valid only when the several markers indeed have identical phylogenies, an issue which many multigene methods (such as the use of concatenated gene sequences or the assembly of supertrees) do not directly address. Indeed, even when the true history is a mixture of vertical descent for some genes and lateral gene transfer (LGT) for others, such methods produce unique topologies. We have developed software that aims to extract evidence for vertical and lateral inheritance from a set of gene trees compared against an arbitrary reference tree. This evidence is then displayed as a synthesis showing support over the tree for vertical inheritance, overlaid with explicit lateral gene transfer (LGT) events inferred to have occurred over the history of the tree. Like splits-tree methods, one can thus identify nodes at which conflict occurs. Additionally one can make reasonable inferences about vertical and lateral signal, assigning putative donors and recipients. A tool such as ours can serve to explore the reticulated dimensionality of molecular evolution, by dissecting vertical and lateral inheritance at high resolution. By this, we mean that individual nodes can be examined not only for congruence, but also for coherence in light of LGT. We assert that our tools will facilitate the comparison of phylogenetic trees, and the interpretation of conflicting data.