Matthew Gonnerman

Highly pathogenic avian influenza viruses (HPAIV) have had disastrous, worldwide effects on wild birds and domestic poultry since the emergence of the A/goose/Guangdong/1/1996 (Gs/GD/96) lineage. The currently circulating H5N1 clade 2.3.4.4b has an expanded set of susceptible hosts, including many migratory wild birds, and is associated with higher transmission rates, increased susceptibility among wild bird hosts, and a greater number of wildlife reservoirs. Certain wild bird life‐history strategies and behaviors have been suggested to explain avian hosts’ susceptibility and exposure to HPAIV. These biological traits include gregariousness, such as colonial nesting and mixed flock foraging, predation or scavenging on wild birds, and association with aquatic habitats. Variation in host infection responses (e.g., infectability, shedding rates and duration, mortality rate, antibody development) informs the overall infection risk across avian species, yet the specific role of biological traits is often inconsistent and unclear across taxa. Moreover, the interactions and potential compounding effects among these biological traits remain largely unknown. To develop a more holistic understanding of cumulative risk across bird species, we integrate existing information on infection risk factors (i.e., susceptibility, immunological response, and behavioral traits) into a qualitative multivariate analysis. This approach enabled us to examine how infection risk factors relate to biological traits (e.g., phylogeny, physiology, behavior, species range) and to begin disentangling their complex interactions. We quantified and summarized these risk factors across host species and qualitatively ranked species by their viral responses along a proposed HPAIV response continuum, guided by expectations of traits and metrics associated with competence or vulnerability to HPAIV. In doing so, we aimed to better understand how viral responses and biological traits synergistically interact to influence cumulative risk across wild bird species. This work broadly expands on the previous avian influenza literature, which has focused on Anseriformes and Charadriiformes as primary viral reservoirs. We tie our findings to effective disease management responses with links to risk components, including descriptions of potential surveillance strategies applied to research and One Health goals, as well as a fuller understanding of how resources may be better deployed for rapid response when spillovers do inevitably occur. Additionally, we identified numerous areas where vital epidemiological information is lacking to best characterize the spread of these viruses. Ultimately, this improved understanding will help identify and inform disease management needs and decision making.

The lack of consolidated information regarding the response of wild bird species to infection with avian influenza virus (AIV) is a challenge to both conservation managers and researchers alike, with related sectors also impacted, such as public health and commercial poultry. Using two independent searches, we reviewed published literature for studies describing wild bird species experimentally infected with avian influenza to assess host species’ relative susceptibility to AIVs. Additionally, we summarize broad-scale parameters for elements such as shedding duration and minimum infectious dose that can be used in transmission modelling efforts. Our synthesis shows that waterfowl (i.e. Anatidae) compose the vast majority of published AIV pathobiology studies, whereas gulls and passerines are less represented in research despite evidence that they also are susceptible and contribute to highly pathogenic avian influenza disease dynamics. This study represents the first comprehensive effort to compile available literature regarding the pathobiology of AIVs in all wild birds in over a decade. This database can now serve as a tool to all researchers, providing generalized estimates of pathobiology parameters for a variety of wild avian families and an opportunity to critically examine and assess what is known and identify where further insight is needed.

Management boundaries are often delimited by political and social factors, whereas animal movements are affected by ecological and geophysical constraints. Thus, understanding connectivity among distinct management units is of considerable importance, particularly for harvested species, where quotas set in ignorance of connectivity may fail to meet management goals. We constructed an individual‐based model (IBM) to better understand wild turkey movements at large scales, benefiting from multiple data sources that are often available for harvested species. We built an IBM from spring seasonal movements of wild turkey, using data from ringed, radio‐, and GPS‐marked turkeys captured in Maine, USA. Our IBM accommodated variation in individual turkey response to landscape connectivity metrics and identified emergent migratory connectivity dynamics among harvest management regions. We calculated a low degree of connectivity among wildlife management districts (WMD) which, in combination with the substantial number of boundary crossings observed, indicated a more diffuse distribution of turkeys among WMDs. Synthesis and applications: Estimates of turkeys moving between districts provided a clear delineation of where immigration was strongest, identifying which WMDs should be managed as singular population units. This approach has widespread utility for any species or system where harvest management decisions are made at finer spatial scales than the movement dynamics affecting population processes.

The lack of consolidated information regarding wild bird species’ response to avian influenza virus (AIV) infection is a challenge for conservation managers, researchers, and related sectors such as public health and commercial poultry. Such information could be used to model complex disease dynamics within communities, prioritize species for surveillance and conservation efforts, or identify species more likely to facilitate spillover into domestic animals or humans. Using two independent searches, we reviewed published literature for studies describing wild bird species experimentally infected with avian influenza to assess host species’ relative susceptibility to AIVs. Additionally, we summarized broad-scale parameters for elements such as shedding duration and minimum infectious dose that can be used in transmission modeling efforts. Our synthesis documented that waterfowl (i.e., Anatidae) comprise the vast majority of published AIV pathobiology studies, whereas gulls and passerines were less represented in research despite evidence that they also are susceptible and contribute to high pathogenicity avian influenza disease dynamics. This study represents the first comprehensive effort to compile available literature regarding the pathobiology of AIV’s in all wild birds in over a decade. This database provides an opportunity to critically examine and assess what is known and identify where further insight is needed.

Context Stress or injury resulting from capture and marking of animals is a potential cause of mortality following release. Multiple methods have been developed to identify sources of post-capture mortality, but these are most often applied following completion of field work to identify an appropriate censor window for other analyses. Aims Following unacceptable levels of post-capture mortality (13 of 53) in radio-marked individuals in the first year of a larger wild turkey research project in Maine, USA, we assessed post-capture survival as data became available to inform proactive changes to capture protocols, with the goals of improving animal welfare and data quality. Methods We evaluated potential sources of post-capture mortality related to the capture and marking process, individual characteristics of the turkey, and local weather conditions. We then used results from the preliminary analysis to inform adaptive changes to capture protocols in subsequent years and confirmed the effectiveness of these changes through a final analysis. Key results We found that greater handling time was positively correlated with increased post-capture survival, possibly in response to releasing turkeys in larger groups to facilitate regrouping. We also found that transmitter style impacted post-capture survival, such that female turkeys fitted with backpack-style transmitters experienced a survival rate of 0.787 (0.677–0.861 95% CI), compared with 0.903 (0.538–0.976 95% CI) for those fitted with a necklace transmitter, although adjustments to the fit of backpack transmitters appeared to help mitigate such issues. Conclusions Following informed adjustments to our capture protocols, we observed a dramatic increase in post-capture survival such that no mortalities were experienced in the first 30 days post capture in the final year of our study (n = 65). Although our estimated censor window was similar to other studies (~10 days), differences in effects of external stressors further the need for adaptive capture protocols because local stressors and risks may vary according to climate and ecosystem characteristics such as predator communities and habitat type. Implications We recommend that when possible, investigators continuously assess their protocols throughout the capture process and adapt accordingly to limit negative repercussions of capture and handling to wildlife.

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.

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.

Limiting the potential for disturbance of wild turkey ( Meleagris gallopavo) breeding and nesting is often stated as a concern for management, particularly when associated with the spring hunting season. The timing of wild turkey nesting has been shown to vary with latitude, with spring phenology, and among individuals, but local information on nesting timing is needed to fully evaluate local harvest regulations. Our objectives were to characterize wild turkey nesting phenology and identify overlap with current hunting season timing and hunter effort in Maine, USA. We also identified sources of variation related to land cover and individual female characteristics such as body condition and pathogen infection status. Finally, we compared the temporal distributions of nest initiation across the 2 years of our study. We assessed nesting phenology of eastern wild turkeys ( M. g. silvestris ) in Penobscot, Hancock, and Cumberland counties, Maine, by monitoring females for nesting activity using VHF and GPS transmitters. During April–July 2018 and 2019, we located 69 nests from radiomarked females and 3 nests from unmarked females. Of the nests discovered, 56 were presumed first attempts, 12 were second attempts, and one was a third attempt. The median date of nest initiation (i.e., date first egg was laid) for first nests was April 27 in 2018 and May 5 in 2019. Females that nested in areas with greater residential and urban development initiated nests earlier than females that used more rural agricultural land covers. Despite a delay in the onset of nesting in 2019, the distribution of nest initiation among females did not change throughout the season. Our results indicate that the majority of male harvest occurred concurrently with egg laying. Thus, current hunting season timing likely limits risks associated with disturbance of mating. Given that Maine's wild turkey population is thought to be stable or increasing, our data support maintaining the current hunting season structure. However, breeding and nesting phenology of wild turkeys should continue to be monitored in relation to changing environmental conditions, trends in the turkey population, hunter numbers, and spring harvest, so that future changes can be incorporated into harvest management and decision making.