Addressing regional relationships between white-tailed deer densities and land classes

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible platform that bridges the gap between genomics and biotechnological solutions.

Re-framing deer herbivory as a natural disturbance regime with ecological and socioeconomic outcomes in the eastern United States

Natural disturbances are critical ecosystem processes, with both ecological and socioeconomic benefits and disadvantages. Large herbivores are natural disturbances that have removed plant biomass for millions of years, although herbivore influence likely has declined during the past thousands of years corresponding with extinctions and declines in distributions and abundances of most animal species. Nonetheless, the conventional view, particularly in eastern North America, is that herbivory by large wild herbivores is at unprecedented levels, resulting in unnatural damage to forests. Here, we propose consideration of large herbivores as a natural disturbance that also imparts many crucial ecological advantages, using white-tailed deer (Odocoileus virginianus), the only wild large herbivore remaining throughout the eastern U.S., as our focal species. We examined evidence of detrimental effects of browsing on trees and forbs. We then considered that deer contribute to both fuel reduction and ecological restoration of herbaceous plants and historical open forests of savannas and woodlands by controlling tree and shrub densities, mimicking the consumer role of fire. Similarly to other disturbances, deer disturbance 'regimes' are uneven in severity across different ecosystems and landscapes, resulting in heterogeneity and diversity. In addition to biodiversity support and fuel reduction, socioeconomic benefits include >$20 billion dollars per year by 10 million hunters that support jobs and wildlife agencies, non-consumptive enjoyment of nature by 80 million people, cultural importance, and deer as ecological ambassadors, whereas costs include about $5 billion and up to 450 human deaths per year for motor vehicle accidents, along with crop damage and disease transmission. From a perspective of historical ecology rather than current baselines, deer impart a fundamental disturbance process with many ecological benefits and a range of socioeconomic effects.

White-tailed deer (Odocoileus virginianus) may serve as a wildlife reservoir for nearly extinct SARS-CoV-2 variants of concern

The spillover of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from humans to white-tailed deer (WTD) and its ability to transmit from deer to deer raised concerns about the role of WTD in the epidemiology and ecology of the virus. Here, we present a comprehensive cross-sectional study assessing the prevalence, genetic diversity, and evolution of SARS-CoV-2 in WTD in the State of New York (NY). A total of 5,462 retropharyngeal lymph node samples collected from free-ranging hunter-harvested WTD during the hunting seasons of 2020 (Season 1, September to December 2020, n = 2,700) and 2021 (Season 2, September to December 2021, n = 2,762) were tested by SARS-CoV-2 real-time RT-PCR (rRT-PCR). SARS-CoV-2 RNA was detected in 17 samples (0.6%) from Season 1 and in 583 samples (21.1%) from Season 2. Hotspots of infection were identified in multiple confined geographic areas of NY. Sequence analysis of SARS-CoV-2 genomes from 164 samples demonstrated the presence of multiple SARS-CoV-2 lineages and the cocirculation of three major variants of concern (VOCs) (Alpha, Gamma, and Delta) in WTD. Our analysis suggests the occurrence of multiple spillover events (human to deer) of the Alpha and Delta lineages with subsequent deer-to-deer transmission and adaptation of the viruses. Detection of Alpha and Gamma variants in WTD long after their broad circulation in humans in NY suggests that WTD may serve as a wildlife reservoir for VOCs no longer circulating in humans. Thus, implementation of continuous surveillance programs to monitor SARS-CoV-2 dynamics in WTD is warranted, and measures to minimize virus transmission between humans and animals are urgently needed.

A model-based estimate of winter distribution and abundance of white-tailed deer in the Adirondack Park

In the Adirondack Park region of northern New York, USA, white-tailed deer (Odocoileus virginianus) and moose (Alces alces) co-occur along a temperate-boreal forest ecotone. In this region, moose exist as a small and vulnerable low-density population and over-browsing by white-tailed deer is known to reduce regeneration, sustainability, and health of forests. Here, we assess the distribution and abundance of white-tailed deer at a broad spatial scale relevant for deer and moose management in northern New York. We used density surface modeling (DSM) under a conventional distance sampling framework, tied to a winter aerial survey, to create a spatially explicit estimate of white-tailed deer abundance and density across a vast, northern forest region. We estimated 16,352 white-tailed deer (95% CI 11,762–22,734) throughout the Adirondack Park with local density ranging between 0.00–5.73 deer/km². Most of the Adirondack Park (91.2%) supported white-tailed deer densities of ≤2 individuals/km². White-tailed deer density increased with increasing proximity to anthropogenic land cover such as timber cuts, roads, and agriculture and decreased in areas with increasing elevation and days with snow cover. We conclude that climate change will be more favorable for white-tailed deer than for moose because milder winters and increased growing seasons will likely have a pronounced influence on deer abundance and distribution across the Adirondack Park. Therefore, identifying specific environmental conditions facilitating the expansion of white-tailed deer into areas with low-density moose populations can assist managers in anticipating potential changes in ungulate distribution and abundance and to develop appropriate management actions to mitigate negative consequences such as disease spread and increased competition for limiting resources.

Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing

In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mixed-age heterogeneous landscapes that provide abundant herbaceous forage in warm months and coniferous forage during winter. Heterogeneity of forest stands is driven by management activities or natural disturbance, resulting in a multi-age forest at a landscape scale. Here, we present a method to estimate landscape carrying capacity of moose by combining remote sensing classification of forest cover class with literature or field-based estimates of class-specific forage abundance. We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. Our three-classes yielded a mean land cover prediction accuracy of 94.3% (Khat = 0.91) and 86.9% (Khat = 0.76) for ANFR and AP, respectively. In the AP, we applied previously calculated summer crude protein values to our predicted cover types, resulting in an estimated average carrying capacity of 760 moose (SD ± 428) across all sampling years, similar in magnitude to a density estimate of 716 moose (95% CI = 566–906) calculated during the same time. Our approach was able to accurately identify forest timber treatments across landscapes at differing spatial and temporal scales and provide an alternative method to estimate landscape-level ungulate carrying capacity. The ability to accurately identify areas of potential conflict from overbrowsing, or to highlight areas in need of land cover treatments can increase the toolset for ungulate management in managed forest landscapes.