Are moose a "resident species" in Wisconsin?

Methods

Summary

This project will utilize a combination of camera traps and field observations to monitor moose populations in Northern Wisconsin. A stratified-random design will guide trail camera placement across diverse habitats, including wetlands, dense forests, open areas, and transitional zones, following the methodology outlined by Kays et al. (2020) in their study on camera trap design.

Field observations will supplement the camera data whenever possible, providing additional context and insights. Community involvement is a crucial component of this project, as local residents frequently report moose sightings, offering valuable real-time information that enhances the study’s scope. However, community reports will be included in data collection only when verification can be made through photo or video documentation with time, date, and location stamps and/or visual evidence to definitively indicate the location of the sighting. Alternatively, the principal researcher will return to the site to confirm sightings using visual evidence, which may include subsequent sightings, signs of moose presence (browse, tracks, scat, bed), or other corroborating information.

This multi-method approach ensures balanced and extensive coverage across key habitats. Cameras will be deployed proportionally based on habitat importance, with sensitivity adjustments made to reduce false triggers. The combination of automated data collection and direct human observations will capture the full range of moose behavior, supporting the goal of proving their residency in Northern Wisconsin. This methodology, along with careful camera placement and periodic adjustments, can be readily replicated by other researchers studying wildlife in challenging environments.

Challenges

One significant challenge is the vast area moose cover, which makes it difficult to ensure full monitoring coverage across their range. Moose habitat spans large, rugged landscapes that include marshes, swamps, and wetlands, which create physical barriers to both navigation and the precise placement of cameras in key transitional zones. Navigating these complex environments to locate optimal camera locations and corridors for moose movement can be logistically challenging. Additionally, the size and remoteness of these areas require careful planning to balance comprehensive coverage with limited resources.

Another challenge is the voluntary nature of this study, as managing time between fieldwork and other work commitments can lead to delays. The time required to reach these remote locations, especially in difficult terrain, can slow down the implementation of camera placement or necessary adjustments. Balancing these demands with work schedules and other obligations sometimes causes delays in accessing key areas or making changes to camera setups when new data suggests adjustments are needed.

False triggers from environmental factors like wind, moving water, or vegetation present an additional challenge. To overcome this, I will adjust camera sensitivity settings and placement angles, while also conducting regular reviews to optimize locations based on early data. The terrain and unpredictability of moose behavior may complicate fieldwork, but flexible deployment strategies and the use of community-reported sightings will help mitigate these risks.

Pre Analysis Plan

The primary hypothesis is that moose in Northern Wisconsin use a variety of habitats year-round, providing evidence of their residency in the region. To test this hypothesis, I will analyze the combined data from camera traps, field observations, and community-reported sightings to assess moose activity across different habitat types and seasons.

Using generalized linear models (GLMs), which are suitable for modeling complex ecological data with various response types (such as counts or binary presence/absence), I will compare detection rates across habitat types and seasons. I will examine potential correlations between moose presence and environmental variables, including seasonality, vegetation type, proximity to water sources, and additional factors like temperature and snow depth.

I will also incorporate temporal trends to study movement patterns, testing for significant variations in moose activity throughout the year. To handle multiple outcomes, such as differences in habitat use or the influence of community sightings, I will employ a multi-model approach, utilizing model selection criteria like AIC or BIC to determine the best-fitting models. Sensitivity analyses will be conducted to address variance and outliers, specifically by applying methods to identify and manage anomalous data points.

This plan ensures a robust analysis of the data, providing strong evidence to support the claim that moose are an established resident species in Northern Wisconsin. Additionally, visualizations will be used to effectively communicate findings, and I will address potential limitations in the data collection process, particularly regarding community-reported sightings. Ultimately, this analysis aims to inform conservation efforts and management strategies for moose populations in the region.