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The Yellow Warbler (Setophaga petechia) is a small songbird in the wood-warbler family (Parulidae) that exhibits phenotypic and ecological differences across a widespread distribution and is important to California's riparian habitat conservation. Here, we present a high-quality de novo genome assembly of a vouchered female Yellow Warbler from southern California. Using HiFi long-read and Omni-C proximity sequencing technologies, we generated a 1.22 Gb assembly including 687 scaffolds with a contig N50 of 6.80 Mb, scaffold N50 of 21.18 Mb, and a BUSCO completeness score of 96.0%. This highly contiguous genome assembly provides an essential resource for understanding the history of gene flow, divergence, and local adaptation in Yellow Warblers and can inform conservation management of this charismatic bird species.

Habitat-specific thermal responses are well documented in various organisms and likely determine the vulnerability of populations to climate change. However, the underlying roles of genetics and plasticity that shape such habitat-specific patterns are rarely investigated together. Here we examined the thermal plasticity of the butterfly Bicyclus dorothea originating from rainforest and ecotone habitats in Cameroon under common garden conditions. We also sampled wild-caught butterflies from forest and ecotone sites and used RADseq to explore genome-wide population differentiation. We found differences in the level of phenotypic plasticity across habitats. Specifically, ecotone populations exhibited greater sensitivity in wing eyespot features with variable development temperatures relative to rainforest populations. Known adaptive roles of wing eyespots in Bicyclus species suggest that this morphological plasticity is likely under divergent selection across environmental gradients. However, we found no distinct population structure of genome-wide variation between habitats, suggesting high level of ongoing gene flow between habitats is homogenizing most parts of the genome.

Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide-ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype-environment associations in the hermit thrush (Catharus guttatus), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype-environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature-associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature-related barriers to gene flow and/or temperature-driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype-environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population-level sensitivity to climate change and other stressors.

Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.

Preserving biodiversity under rapidly changing climate conditions is challenging. One approach for estimating impacts and their magnitude is to model current relationships between genomic and environmental data and then to forecast those relationships under future climate scenarios. In this way, understanding future genomic and environmental relationships can help guide management decisions, such as where to establish new protected areas where populations might be buffered from high temperatures or major changes in rainfall. However, climate warming is only one of many anthropogenic threats one must consider in rapidly developing parts of the world. In Central Africa, deforestation, mining, and infrastructure development are accelerating population declines of rainforest species. Here we investigate multiple anthropogenic threats in a Central African rainforest songbird, the little greenbul (Andropadus virens). We examine current climate and genomic variation in order to explore the association between genome and environment under future climate conditions. Specifically, we estimate Genomic Vulnerability, defined as the mismatch between current and predicted future genomic variation based on genotype-environment relationships modeled across contemporary populations. We do so while considering other anthropogenic impacts. We find that coastal and central Cameroon populations will require the greatest shifts in adaptive genomic variation, because both climate and land use in these areas are predicted to change dramatically. In contrast, in the more northern forest-savanna ecotones, genomic shifts required to keep pace with climate will be more moderate, and other anthropogenic impacts are expected to be comparatively low in magnitude. While an analysis of diverse taxa will be necessary for making comprehensive conservation decisions, the species-specific results presented illustrate how evolutionary genomics and other anthropogenic threats may be mapped and used to inform mitigation efforts. To this end, we present an integrated conceptual model demonstrating how the approach for a single species can be expanded to many taxonomically diverse species.

Predicting species' capacity to respond to climate change is an essential first step in developing effective conservation strategies. However, conservation prioritization schemes rarely take evolutionary potential into account. Ecotones provide important opportunities for diversifying selection and may thus constitute reservoirs of standing variation, increasing the capacity for future adaptation. Here, we map patterns of environmentally associated genomic and craniometric variation in the central African rodent Praomys misonnei to identify areas with the greatest turnover in genomic composition. We also project patterns of environmentally associated genomic variation under future climate change scenarios to determine where populations may be under the greatest pressure to adapt. While precipitation gradients influence both genomic and craniometric variation, vegetation structure is also an important determinant of craniometric variation. Areas of elevated environmentally associated genomic and craniometric variation overlap with zones of rapid ecological transition underlining their importance as reservoirs of evolutionary potential. We also find that populations in the Sanaga river basin, central Cameroon and coastal Gabon are likely to be under the greatest pressure from climate change. Lastly, we make specific conservation recommendations on how to protect zones of high evolutionary potential and identify areas where populations may be the most susceptible to climate change.

As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.

A prominent challenge for managing migratory species is the development of conservation plans that accommodate spatiotemporally varying distributions throughout the year. Migratory networks are spatially‐explicit models that incorporate migratory assignment and seasonal abundance data to define patterns of connectivity between stages of the annual cycle. These models are particularly useful for widespread application because different types of migratory data can be used to quantify individual and population‐level movement across the annual cycle of migratory species. While there are clear benefits of combining migratory assignment and abundance data for the development of conservation strategies, there is a concurrent need for corresponding user‐friendly software to facilitate the integration of these data for conservation.Here, we present mignette (migratory network tools ensemble), an R package for developing migratory network models to estimate network connectivity among migratory populations. We demonstrate the functionality of mignette with three empirical examples that highlight the use of different types of tracking data for migratory assignment.mignette facilitates the modelling of migratory networks by providing R functions to: (1) define breeding and nonbreeding nodes, (2) assemble abundance and assignment data and (3) model the migratory network. Additionally, mignette provides R functions to visualize modelled migratory networks.With increasing availability of migratory assignment and abundance data, mignette represents a valuable tool for developing effective conservation strategies for migratory species. [ABSTRACT FROM AUTHOR]

Infectious diseases that originate from multiple wildlife hosts can be complex and problematic to manage. A full understanding is further limited by large temporal and spatial gaps in sampling. However, these limitations can be overcome, in part, by using historical samples, such as those derived from museum collections. Here, we screened over 1000 museum specimens collected over the past 120 years to examine the historical distribution and prevalence of monkeypox virus (MPXV) in five species of African rope squirrel (Funisciurus sp.) collected across Central Africa. We found evidence of MPXV infections in host species as early as 1899, half a century earlier than the first recognized case of MPXV in 1958, supporting the suggestion that historic pox-like outbreaks in humans and non-human primates may have been caused by MPXV rather than smallpox as originally thought. MPX viral DNA was found in 93 of 1038 (9.0%) specimens from five Funisciurus species (F. anerythrus, F. carruthersi, F. congicus, F. lemniscatus and F. pyrropus), of which F. carruthersi and pyrropus had not previously been identified as potential MPXV hosts. We additionally documented relative prevalence rates of infection in museum specimens of Funisciurus and examined the spatial and temporal distribution of MPXV in these potential host species across nearly a hundred years (1899-1993).

The threatened eastern wolf is found predominantly in protected areas of central Ontario and has an evolutionary history obscured by interbreeding with coyotes and gray wolves, which challenges its conservation status and subsequent management. Here, we used a population genomics approach to uncover spatial patterns of variation in 281 canids in central Ontario and the Great Lakes region. This represents the first genome-wide single nucleotide polymorphism (SNP) dataset with substantial sample sizes of representative populations. Although they comprise their own genetic cluster, we found evidence of eastern wolf dispersal outside of the boundaries of protected areas, in that the frequency of eastern wolf genetic variation decreases with increasing distance from provincial parks. We detected eastern wolf alleles in admixed coyotes along the northeastern regions of Lake Huron and Lake Ontario. Our analyses confirm the unique genomic composition of eastern wolves, which are mostly restricted to small fragmented patches of protected habitat in central Ontario. We hope this work will encourage an innovative discussion regarding a plan for managed introgression, which could conserve eastern wolf genetic material in any genome regardless of their potential mosaic ancestry composition and the habitats that promote them.

Simian immunodeficiency virus (SIV) naturally infects two subspecies of chimpanzee: Pan troglodytes troglodytes from Central Africa (SIVcpzPtt) and P. t. schweinfurtii from East Africa (SIVcpzPts), but is absent in P. t. verus from West Africa and appears to be absent in P. t. ellioti inhabiting Nigeria and western Cameroon. One explanation for this pattern is that P. t. troglodytes and P. t schweinfurthii may have acquired SIVcpz after their divergence from P. t. verus and P. t. ellioti. However, all of the subspecies, except P. t. verus, still occasionally exchange migrants making the absence of SIVcpz in P. t. ellioti puzzling. Sampling of P. t. ellioti has been minimal to date, particularly along the banks of the Sanaga River, where its range abuts that of P. t. troglodytes. This study had three objectives. First, we extended the sampling of SIVcpz across the range of chimpanzees north of the Sanaga River to address whether under-sampling might account for the absence of evidence for SIVcpz infection in P. t. ellioti. Second, we investigated how environmental variation is associated with the spread and prevalence of SIVcpz in the two chimpanzee subspecies inhabiting Cameroon since environmental variation has been shown to contribute to their divergence from one another. Finally, we compared the prevalence and distribution of SIVcpz with that of Simian Foamy Virus (SFV) to examine the role of ecology and behavior in shaping the distribution of diseases in wild host populations. The dataset includes previously published results on SIVcpz infection and SFVcpz as well as newly collected data, and represents over 1000 chimpanzee fecal samples from 41 locations across Cameroon. Results revealed that none of the 181 P. t. ellioti fecal samples collected across the range of P. t. ellioti tested positive for SIVcpz. In addition, species distribution models suggest that environmental variation contributes to differences in the distribution and prevalence of SIVcpz and SFVcpz. The ecological niches of these two viruses are largely non-overlapping, although stronger statistical support for this conclusion will require more sampling. Overall this study demonstrates that SIVcpz infection is absent or very rare in P. t. ellioti, despite multiple opportunities for transmission. The reasons for its absence remain unclear, but might be explained by one or more factors, including environmental variation, viral competition, and/or local adaptation-all of which should be explored in greater detail through continued surveillance of this region.

Since its introduction to North America in 1999, West Nile virus (WNV) has had devastating impacts on native host populations, but to date these impacts have been difficult to measure. Using a continental-scale dataset comprised of a quarter-million birds captured over nearly two decades and a recently developed model of WNV risk, we estimated the impact of this emergent disease on the survival of avian populations. We find that populations were negatively affected by WNV in 23 of the 49 species studied (47%). We distinguished two groups of species: those for which WNV negatively impacted survival only during initial spread of the disease (n = 11), and those that show no signs of recovery since disease introduction (n = 12). Results provide a novel example of the taxonomic breadth and persistent impacts of this wildlife disease on a continental scale. Phylogenetic analyses further identify groups (New World sparrows, finches, and vireos) disproportionally affected by temporary or persistent WNV effects, suggesting an evolutionary dimension of disease risk. Identifying the factors affecting the persistence of a disease across host species is critical to mitigating its effects, particularly in a world marked by rapid anthropogenic change.

The adaptive significance of zebra stripes has thus far eluded understanding. Many explanations have been suggested, including social cohesion, thermoregulation, predation evasion and avoidance of biting flies. Identifying the associations between phenotypic and environmental factors is essential for testing these hypotheses and substantiating existing experimental evidence. Plains zebra striping pattern varies regionally, from heavy black and white striping over the entire body in some areas to reduced stripe coverage with thinner and lighter stripes in others. We examined how well 29 environmental variables predict the variation in stripe characteristics of plains zebra across their range in Africa. In contrast to recent findings, we found no evidence that striping may have evolved to escape predators or avoid biting flies. Instead, we found that temperature successfully predicts a substantial amount of the stripe pattern variation observed in plains zebra. As this association between striping and temperature may be indicative of multiple biological processes, we suggest that the selective agents driving zebra striping are probably multifarious and complex.

The northern Andes, with their steep elevational and climate gradients, are home to an exceptional diversity of flora and fauna, particularly rich in avian species that have adapted to divergent ecological conditions. With this diversity comes the opportunity for parasites to exploit a wide breadth of avian hosts. However, little research has focused on examining the patterns of prevalence and lineage diversity of avian parasites in the Andes. Here, we screened a total of 428 birds from 19 species (representing nine families) and identified 133 infections of avian haemosporidia (31%), including lineages of Plasmodium, Haemoproteus, and Leucocytozoon. We document a higher prevalence of haemosporidia at higher elevations and lower temperatures, as well as an overall high diversity of lineages in the northern Andes, including the first sequences of haemosporidians reported in hummingbirds (31 sequences found in 11 species within the family Trochilidae). Double infections were distinguished using PHASE, which enables the separation of distinct parasite lineages. Results suggest that the ecological heterogeneity of the northern Andes that has given rise to a rich diversity of avian hosts may also be particularly conducive to parasite diversification and specialization.

BackgroundThe 2009 pH1N1 influenza pandemic resulted in at least 18,500 deaths worldwide. While pH1N1 is now considered to be in a post-pandemic stage in humans it has nevertheless spilled back into swine in at least 20 countries. Understanding the factors that increase the risk of spillover events between swine and humans is essential to predicting and preventing future outbreaks. We assessed risk factors that may have led to spillover of pH1N1 from humans to swine in Cameroon, Central Africa. We sampled swine, domestic poultry and wild birds for influenza A virus at twelve sites in Cameroon from December 2009 while the pandemic was ongoing, to August 2012. At the same time we conducted point-count surveys to assess the abundance of domestic livestock and wild birds and assess interspecific contact rates. Random forest models were used to assess which variables were the best predictors of influenza in swine.ResultsWe found swine with either active pH1N1 infections or positive for influenza A at four of our 12 sites. Only one swine tested positive by competitive ELISA in 2011-2012. To date we have found pH1N1 only in the North and Extreme North regions of Cameroon (regions in Cameroon are administrative units similar to provinces), though half of our sites are in the Central and Western regions. Swine husbandry practices differ between the North and Extreme North regions where it is common practice in to let swine roam freely, and the Central and Western regions where swine are typically confined to pens. Random forest analyses revealed that the three best predictors of the presence of pH1N1 in swine were contact rates between free-ranging swine and domestic ducks, contact rates between free-ranging swine and wild Columbiformes, and contact rates between humans and ducks. Sites in which swine were allowed to range freely had closer contact with other species than did sites in which swine were kept penned.ConclusionsResults suggest that the practice of allowing swine to roam freely is a significant risk factor for spillover of influenza from humans into swine populations.

Environmentally imposed selection pressures are well known to shape animal signals. Changes in these signals can result in recognition mismatches between individuals living in different habitats, leading to reproductive divergence and speciation. For example, numerous studies have shown that differences in avian song may be a potent prezygotic isolating mechanism. Typically, however, detailed studies of environmental pressures on variation in animal behavior have been conducted only at small spatial scales. Here, we use remote-sensing data to predict animal behavior, in this case, bird song, across vast spatial scales. We use remotely sensed data to predict the song characteristics of the little greenbul (Andropadus virens), a widely distributed African passerine, found across secondary and mature rainforest habitats and the rainforest-savanna ecotone. Satellite data that captured ecosystem structure and function explained up to 66% of the variation in song characteristics. Song differences observed across habitats, including those between human-altered and mature rainforest, have the potential to lead to reproductive divergence, and highlight the impacts that both natural and anthropogenic change may have on natural populations. Our approach offers a novel means to examine the ecological correlates of animal behavior across large geographic areas with potential applications to both evolutionary and conservation biology.

Understanding the conditions underlying the proliferation of infectious diseases is crucial for mitigating future outbreaks. Since its arrival in North America in 1999, West Nile virus (WNV) has led to population-wide declines of bird species, morbidity and mortality of humans, and expenditures of millions of dollars on treatment and control. To understand the environmental conditions that best explain and predict WNV prevalence, we employed recently developed spatial modeling techniques in a recognized WNV hotspot, Orange County, California. Our models explained 85-95% of the variation of WNV prevalence in mosquito vectors, and WNV presence in secondary human hosts. Prevalence in both vectors and humans was best explained by economic variables, specifically per capita income, and by anthropogenic characteristics of the environment, particularly human population and neglected swimming pool density. While previous studies have shown associations between anthropogenic change and pathogen presence, results show that poorer economic conditions may act as a direct surrogate for environmental characteristics related to WNV prevalence. Low-income areas may be associated with higher prevalence for a number of reasons, including variations in property upkeep, microhabitat conditions conducive to viral amplification in both vectors and hosts, host community composition, and human behavioral responses related to differences in education or political participation. Results emphasize the importance and utility of including economic variables in mapping spatial risk assessments of disease.

Abstract Background The mosquito vectors of Plasmodium spp. have largely been overlooked in studies of ecology and evolution of avian malaria and other vertebrates in wildlife. Methods Plasmodium DNA from wild-caught Coquillettidia spp. collected from lowland forests in Cameroon was isolated and sequenced using nested PCR. Female Coquillettidia aurites were also dissected and salivary glands were isolated and microscopically examined for the presence of sporozoites. Results In total, 33% (85/256) of mosquito pools tested positive for avian Plasmodium spp., harbouring at least eight distinct parasite lineages. Sporozoites of Plasmodium spp. were recorded in salivary glands of C. aurites supporting the PCR data that the parasites complete development in these mosquitoes. Results suggest C. aurites, Coquillettidia pseudoconopas and Coquillettidia metallica as new and important vectors of avian malaria in Africa. All parasite lineages recovered clustered with parasites formerly identified from several bird species and suggest the vectors capability of infecting birds from different families. Conclusion Identifying the major vectors of avian Plasmodium spp. will assist in understanding the epizootiology of avian malaria, including differences in this disease distribution between pristine and disturbed landscapes.

Population genetic variation in Mallards (Arias platyrhynchos; n = 152) from Western Russia, North Asia, the Aleutian Islands, and mainland Alaska was investigated using 667 base pairs of the 5'-end of the mitochondrial DNA (mtDNA) control region. DNA sequencing revealed two clades that correspond to Avise et al.'s (1990) group A and B mtDNA haplotypes. Group A haplotypes (80.3%) were widespread in all localities from Western Russia to Alaska. Group B haplotypes (19.7%), by contrast, were found primarily in mainland Alaska, where they occurred at high frequency (77.4%), but they also occurred at low frequencies (declining east to west) in the Aleutian Islands (11.8%) and the Primorye region of North Asia (4.4%). Group B haplotypes were not observed in Western Russia or elsewhere in North Asia outside Primorye. Consequently, Mallards exhibited substantial genetic structure between Old World and New World ([[PHI].sub.ST] = 0.4112-0.4956) but possessed little genetic structure within the Old World continental area ([[PHI].sub.ST] = 0.0018). Nonetheless, when only group A haplotypes were included in the analysis, Mallards from the Aleutian Islands differed (albeit with low levels of divergence) from each of the other three sampled regions in the Old World and New World ([[PHI].sub.ST] = 0.0728-0.1461, P < 0.05). Mallards inhabit the Aleutian Islands year-round, so these insular populations may be isolated from Asian and North American populations that occur in the Aleutian Islands only during migration. Overall weak phylogeographic structure and low genetic differentiation within Asia, and between Asia and North America when only group A haplotypes were evaluated, is probably explained by large long-term population sizes and significant intra-continental dispersal. The coexistence and nonrandom distribution of two divergent mtDNA haplotype lineages in Alaska, the Aleutian Islands, and the Primorye region of North Asia, but not in Western Russia or elsewhere in North Asia, is consistent with historical and contemporary hybridization and incomplete sorting of A and B mtDNA haplotype lineages in Mallards and closely related species inhabiting the Old World and New World. Key words: Anas platyrhynchos, hybridization, incomplete lineage sorting, Mallard, paraphyly.

Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases. [ABSTRACT FROM AUTHOR]

Point process models, such as Hawkes and recursive models, have recently been shown to offer improved accuracy over more traditional compartmental models for the purposes of modeling and forecasting the spread of disease epidemics. To explicitly test the performance of these two models in a real‐world and ongoing epidemic, we compared the fit of Hawkes and recursive models to outbreak data on Ebola virus disease (EVD) in the Democratic Republic of the Congo in 2018–2020. The models were estimated, and the forecasts were produced, time‐stamped, and stored in real time, so that their prospective value can be assessed and to guard against potential overfitting. The fit of the two models was similar, with both models resulting in much smaller errors in the beginning and waning phases of the epidemic and with slightly smaller error sizes on average for the Hawkes model compared with the recursive model. Our results suggest that both Hawkes and recursive point process models can be used in near real time during the course of an epidemic to help predict future cases and inform management and mitigation strategies. [ABSTRACT FROM AUTHOR]