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Cache Valley virus (CVV), a mosquitoborne virus, can cause neuroinvasive disease in humans and adverse reproductive outcomes in sheep and goats. In 2023, CVV RNA was detected in an aborted lamb from a flock in Arkansas, USA. We conducted a One Health investigation to explore the potential effects of CVV in Arkansas.

Approximately 100 years ago, unregulated harvest nearly eliminated white‐tailed deer (Odocoileus virginianus) from eastern North America, which subsequently served to catalyze wildlife management as a national priority. An extensive stock‐replenishment effort soon followed, with deer broadly translocated among states as a means of re‐establishment. However, an unintended consequence was that natural patterns of gene flow became obscured and pretranslocation signatures of population structure were replaced. We applied cutting‐edge molecular and biogeographic tools to disentangle genetic signatures of historical management from those reflecting spatially heterogeneous dispersal by evaluating 35,099 single nucleotide polymorphisms (SNPs) derived via reduced‐representation genomic sequencing from 1143 deer sampled statewide in Arkansas. We then employed Simpson's diversity index to summarize ancestry assignments and visualize spatial genetic transitions. Using sub‐sampled transects across these transitions, we tested clinal patterns across loci against theoretical expectations of their response under scenarios of re‐colonization and restricted dispersal. Two salient results emerged: (A) Genetic signatures from historic translocations are demonstrably apparent; and (B) Geographic filters (major rivers; urban centers; highways) now act as inflection points for the distribution of this contemporary ancestry. These results yielded a statewide assessment of contemporary population structure in deer as driven by historic translocations as well as ongoing processes. In addition, the analytical framework employed herein to effectively decipher extant/historic drivers of deer distribution in Arkansas is also applicable for other biodiversity elements with similarly complex demographic histories.

Wild waterfowl are primary reservoirs of avian influenza viruses (AIV). However the role of sea ducks in the ecology of avian influenza, and how that role differs from freshwater ducks, has not been examined. We obtained and analyzed sera from North Atlantic sea ducks and determined the seroprevalence in those populations. We also tested swab samples from North Atlantic sea ducks for the presence of AIV. We found relatively high serological prevalence (61%) in these sea duck populations but low virus prevalence (0.3%). Using these data we estimated that an antibody half-life of 141 weeks (3.2 years) would be required to attain these prevalences. These findings are much different than what is known in freshwater waterfowl and have implications for surveillance efforts, AIV in marine environments, and the roles of sea ducks and other long-lived waterfowl in avian influenza ecology.

Cache Valley virus (CVV), a mosquitoborne virus, can cause neuroinvasive disease in humans and adverse reproductive outcomes in sheep and goats. In 2023, CVV RNA was detected in an aborted lamb from a flock in Arkansas, USA. We conducted a One Health investigation to explore the potential effects of CVV in Arkansas.Cache Valley virus (CVV), a mosquitoborne virus, can cause neuroinvasive disease in humans and adverse reproductive outcomes in sheep and goats. In 2023, CVV RNA was detected in an aborted lamb from a flock in Arkansas, USA. We conducted a One Health investigation to explore the potential effects of CVV in Arkansas.

Despite calls for improved responses to emerging infectious diseases in wildlife, management is seldom considered until a disease has been detected in affected populations. Reactive approaches may limit the potential for control and increase total response costs. An alternative, proactive management framework can identify immediate actions that reduce future impacts even before a disease is detected, and plan subsequent actions that are conditional on disease emergence. We identify four main obstacles to developing proactive management strategies for the newly discovered salamander pathogen Batrachochytrium salamandrivorans (Bsal). Given that uncertainty is a hallmark of wildlife disease management and that associated decisions are often complicated by multiple competing objectives, we advocate using decision analysis to create and evaluate trade‐offs between proactive (pre‐emergence) and reactive (post‐emergence) management options. Policy makers and natural resource agency personnel can apply principles from decision analysis to improve strategies for countering emerging infectious diseases.

Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer ( Odocoileus virginianus ) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/ .

Avian hemosporidian parasites are a genetically diverse group of parasites with a near cosmopolitan distribution. Over the past 2 decades, several PCR protocols have been designed to detect these parasites. The majority of these protocols amplify part of or the entire mitochondrial cytochrome b gene. However, many of these protocols co-amplify 2 genera (Haemoproteus and Plasmodium), making it impossible to determine which genus is amplified without post-PCR analysis. A uniform database (MalAvi), containing sequences amplified with the primers HAEMF and HAEMR2, has been developed to increase comparability across studies. We analyzed sequences from the MalAvi database and new sequences and found that digestion with EcoRV could be used to distinguish Haemoproteus from the majority of Plasmodium sequences. In addition, we tested 220 wild birds from Costa Rica and the United States for avian hemosporidians and assessed the ability of EcoRV to distinguish these 2 genera. Thirty-six positive samples were sequenced to confirm the restriction profiles, and we also analyzed 63 new hemosporidian sequences from ongoing studies in the United States for the restriction site. Among these new samples, all of the 85 Haemoproteus (subgenus Parahaemoproteus) and 14 Plasmodium were distinguishable. Overall, 887 of 898 (98.8%) sequences from our studies and the MalAvi database were assigned to the correct genus. Of these samples, all Haemoproteus samples were correctly identified and all but 11 Plasmodium samples were correctly identified by the EcoRV assay. Overall, this restriction enzyme protocol is able to quickly and efficiently classify these 2 genera of avian malarial parasites and would be useful for researchers interested in identifying parasites to genus-level, studies focused on sequence analysis of only a single genus, or for detecting co-infections that would need cloning prior to sequence analysis.

Cache Valley virus (CVV), a mosquitoborne virus, can cause neuroinvasive disease in humans and adverse reproductive outcomes in sheep and goats. In 2023, CVV RNA was detected in an aborted lamb from a flock in Arkansas, USA. We conducted a One Health investigation to explore the potential effects of CVV in Arkansas.

ABSTRACT Approximately 100 years ago, unregulated harvest nearly eliminated white-tailed deer (Odocoileus virginianus) from eastern North America, which subsequently served to catalyze wildlife management as a national priority. An extensive stock-replenishment effort soon followed, with deer broadly translocated among states as a means of re-establishment. However, an unintended consequence was that natural patterns of gene flow became obscured and pre-translocation signatures of population structure were replaced. We applied cutting-edge molecular and biogeographic tools to disentangle genetic signatures of historical management from those reflecting spatially heterogeneous dispersal by evaluating 35,099 single nucleotide polymorphisms (SNPs) derived via reduced-representation genomic sequencing from 1,143 deer sampled state-wide in Arkansas. We then employed Simpson’s diversity index to summarize ancestry assignments and visualize spatial genetic transitions. Using sub-sampled transects across these transitions, we tested clinal patterns across loci against theoretical expectations of their response under scenarios of recolonization and restricted dispersal. Two salient results emerged: (A) Genetic signatures from historic translocations are demonstrably apparent; and (B) Geographic filters (major rivers; urban centers; highways) now act as inflection points for the distribution of this contemporary ancestry. These results yielded a state-wide assessment of contemporary population structure in deer as driven by historic translocations as well as ongoing processes. In addition, the analytical framework employed herein to effectively decipher extant/historic drivers of deer distribution in Arkansas are also applicable for other biodiversity elements with similarly complex demographic histories. Competing Interest Statement The authors have declared no competing interest. Footnotes * Email: (TKC) tkchafin{at}uark.edu; (BTM) btm002{at}uark.edu; (ZDZ) zdzbinde{at}uark.edu; (MED) med1{at}uark.edu; (MRD) mrd1{at}uark.edu, Email: (CRM) christopher.middaugh{at}agfc.ar.gov; (JRB) jennifer.ballard{at}agfc.ar.gov; (MCG) cory.gray{at}agfc.ar.gov * Minor changes in discussion and acknowledgements

Chronic-wasting disease (CWD) is a prion-derived fatal neurodegenerative disease that has affected wild cervid populations on a global scale. Susceptibility has been linked unambiguously to several amino acid variants within the prion protein gene (PRNP). Quantifying their distribution across landscapes can provide critical information for agencies attempting to adaptively manage CWD. Here we attempt to further define management implications of PRNP polymorphism by quantifying the contemporary geographic distribution (i.e., phylogeography) of PRNP variants in hunter-harvested white-tailed deer (WTD; Odocoileus virginianus, N=1433) distributed across Arkansas (USA), including a focal spot for CWD since detection of the disease in February 2016. Of these, PRNP variants associated with the well-characterized 96S non-synonymous substitution showed a significant increase in relative frequency among older CWD-positive cohorts. We interpreted this pattern as reflective of a longer life expectancy for 96S genotypes in a CWD-endemic region, suggesting either decreased probabilities of infection or reduced disease progression. Other variants showing statistical signatures of potential increased susceptibility, however, seemingly do so as an artefact of population structure. We also showed marked heterogeneity across the landscape in the prevalence of reduced susceptibility genotypes. This may indicate, in turn, that differences in disease susceptibility among WTD in Arkansas are an innate, population-level characteristic that is detectable through phylogeographic analysis. Competing Interest Statement The authors have declared no competing interest.