Dynamic winter weather moderates movement and resource selection of wild turkeys at high-latitude range limits.

For wide-ranging species in temperate environments, populations at high-latitude range limits are subject to more extreme conditions, colder temperatures, and greater snow accumulation compared with their core range. As climate change progresses, these bounding pressures may become more moderate on average, while extreme weather occurs more frequently. Individuals can mitigate temporarily extreme conditions by changing daily activity budgets and exhibiting plasticity in resource selection, both of which facilitate existence at and expansion of high-latitude range boundaries. However, relatively little work has explored how animals moderate movement and vary resource selection with changing weather, and a general framework for such investigations is lacking. We applied hidden Markov models and step selection functions to GPS data from wintering wild turkeys (Meleagris gallopavo) near their northern range limit to identify how weather influenced transition among discrete movement states, as well as state-specific resource selection. We found that turkeys were more likely to spend time in a stationary state as wind chill temperatures decreased and snow depth increased. Both stationary and roosting turkeys selected conifer forests and avoided land covers associated with foraging, such as agriculture and residential areas, while shifting their strength of selection for these features during poor weather. In contrast, mobile turkeys showed relatively weak resource selection, with less response in selection coefficients during poor weather. Our findings illustrate that behavioral plasticity in response to weather was context dependent, but movement behaviors most associated with poor weather were also those in which resource selection was most plastic. Given our results, the potential for wild turkey range expansion will partly be determined by the availability of habitat that allows them to withstand periodic inclement weather. Combining hidden Markov models with step selection functions is broadly applicable for evaluating plasticity in animal behavior and dynamic resource selection in response to changing weather. We studied turkeys at northern range limits, but this approach is applicable for any system expected to experience significant changes in the coming decade, and may be particularly relevant to populations existing at range peripheries.

Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance.

Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and total number of individuals harvested within an integrated band-recovery framework to estimate abundance at large scales. A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM. We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (Meleagris gallapavo) to estimate local and statewide abundance in Maine, USA. We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some subregions. Consistent with simulation results, this led to overestimation of both local and statewide abundance. Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large-scale band recovery data sets and has applicability for the estimation of population parameters in other ecological models as well.

PATHOGEN SURVEY AND PREDICTORS OF LYMPHOPROLIFERATIVE DISEASE VIRUS INFECTION IN WILD TURKEYS (MELEAGRIS GALLOPAVO).

Growing populations of Wild Turkeys (Meleagris gallopavo) may result in increased disease transmission among wildlife and spillover to poultry. Lymphoproliferative disease virus (LPDV) is an avian retrovirus that is widespread in Wild Turkeys of eastern North America, and infections may influence mortality and parasite co-infections. We aimed to identify individual and spatial risk factors of LPDV in Maine's Wild Turkeys. We also surveyed for co-infections between LPDV and reticuloendotheliosis virus (REV), Mycoplasma gallisepticum, and Salmonella pullorum to estimate trends in prevalence and examine covariance with LPDV. From 2017 to 2020, we sampled tissues from hunter-harvested (n=72) and live-captured (n=627) Wild Turkeys, in spring and winter, respectively, for molecular detection of LPDV and REV. In a subset of captured individuals (n=235), we estimated seroprevalence of the bacteria M. gallisepticum and S. pullorum using a plate agglutination test. Infection rates for LPDV and REV were 59% and 16% respectively, with a co-infection rate of 10%. Seroprevalence for M. gallisepticum and S. pullorum were 74% and 3.4%, with LPDV co-infection rates of 51% and 2.6%, respectively. Infection with LPDV and seroprevalence of M. gallisepticum and S. pullorum decreased, whereas REV infection increased, between 2018 and 2020. Females (64%), adults (72%), and individuals sampled in spring (76%) had higher risks of LPDV infection than males (47%), juveniles (39%), and individuals sampled in winter (57%). Furthermore, LPDV infection increased with percent forested cover (ß=0.014±0.007) and decreased with percent agriculture cover for juveniles (ß=-0.061±0.018) sampled in winter. These data enhance our understanding of individual and spatial predictors of LPDV infection in Wild Turkeys and aid in assessing the associated risk to Wild Turkey populations and poultry operations.

Genomically and biochemically accurate metabolic reconstruction of Methanosarcina barkeri Fusaro, iMG746.

Methanosarcina barkeri is an Archaeon that produces methane anaerobically as the primary byproduct of its metabolism. M. barkeri can utilize several substrates for ATP and biomass production including methanol, acetate, methyl amines, and a combination of hydrogen and carbon dioxide. In 2006, a metabolic reconstruction of M. barkeri, iAF692, was generated based on a draft genome annotation. The iAF692 reconstruction enabled the first genome-Scale simulations for Archaea. Since the publication of the first metabolic reconstruction of M. barkeri, additional genomic, biochemical, and phenotypic data have clarified several metabolic pathways. We have used this newly available data to improve the M. barkeri metabolic reconstruction. Modeling simulations using the updated model, iMG746, have led to increased accuracy in predicting gene knockout phenotypes and simulations of batch growth behavior. We used the model to examine knockout lethality data and make predictions about metabolic regulation under different growth conditions. Thus, the updated metabolic reconstruction of M. barkeri metabolism is a useful tool for predicting cellular behavior, studying the methanogenic lifestyle, guiding experimental studies, and making predictions relevant to metabolic engineering applications.

Genome-scale metabolic reconstruction and hypothesis testing in the methanogenic archaeon Methanosarcina acetivorans C2A.

Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis.