Alexej P. K. Siren

Obtaining accurate information on demographic states, such as the age and sex classes of animals, is an important step for monitoring wildlife populations. Traditionally, demographic data are collected from harvest, aerial surveys and telemetry studies. However, these methods can be expensive, limited to small spatial scales, or biased due to human behavior. Remote cameras have become a mainstay for studying and monitoring wildlife as they are relatively inexpensive, can be deployed over large spatial scales, and effort can be accounted for during surveys. For some species, a variety of demographic information, such as sex and age classes, can be obtained from pictures. Moose Alces alces are a photogenic species found across boreal and semi‐boreal forests of the Northern Hemisphere. Previous studies have used demographic data from remote cameras to estimate demographic parameters and population dynamics. A primary assumption is that these age and sex classes are accurately classified. However, numerous factors can influence the ability of observers to identify age and sex classes of moose captured on cameras. We used data from 84 cameras from a 3‐year period (2021–2024) in northern Maine, USA, to evaluate how temporal, environmental, site‐level, and endogenous factors influence observers' ability to classify age and sex classes of moose. Using Bayesian categorical regression models, we found that temporal variability, position and proximity of moose from cameras, and the behavior of moose influenced our ability to identify age and sex classes. This information can be used to decide which periods to use data for population modeling and how to design studies to reduce the amount of uncertainty associated with different age and sex classes. We anticipate that our approach could also be used for other species whose age and sex classes can be differentiated using remote cameras.

We created the SiMPL wildlife magnet—a baited camera trap design that allows passive monitoring of wildlife, particularly small‐ to medium‐sized mammals. The SiMPL wildlife magnet is inexpensive and easy to construct. To evaluate its effectiveness, we conducted a two‐year case study using 9 camera stations along an elevation gradient in the White Mountains of the northeastern United States. We examined how the detection probability of mammal species changed with the inclusion of a SiMPL wildlife magnet using data from pre‐and post‐establishment. We found a significant increase in community‐level detection probability with the use of SiMPL wildlife magnets and for individual species, including red squirrels ( Tamiasciurus hudsonicus ), American marten ( Martes americana ), and fisher ( Pekania pennanti ). Moreover, we were able to capture more species with SiMPL wildlife magnets than without, including flying squirrels ( Glaucomys spp.), various rodents ( Cricetidae spp.), black bears ( Ursus americanus ), moose ( Alces alces ), owls, and other birds. The SiMPL wildlife magnet is an effective, low‐cost method for surveying wildlife communities, especially rodents and mesocarnivores. It addresses the limited field of view presented by other techniques for capturing small mammals on camera traps and enables efficient collection of phenology data, including vegetation and snowpack. This tool has several applications, including monitoring species' responses to management practices and global change.

The DeepFaune New England model classifies wildlife species in trail camera images, identifying 24 taxa from northeastern North America with high (97%) accuracy. The model was adapted from the DeepFaune model for identifying European wildlife, demonstrating the practicality of transfer learning across continents. The majority of training data is openly licensed, and the model itself is open source, enabling easy integration into camera trapping workflows. The open source software is available at ( https://code.usgs.gov/vtcfwru/deepfaune‐new‐england ), and has been further integrated into the PyTorch‐Wildlife framework.

Climate change is reducing snowpack across temperate regions with negative consequences for human and natural systems. Because forest canopies create microclimates that preserve snowpack, managing forests to support snow refugia—defined here as areas that remain relatively buffered from contemporary climate change over time that sustain snow quality, quantity, and/or timing appropriate to the landscape—could reduce climate change impacts on snow cover, sustaining the benefits of snow. We review the current understanding of how forest canopies affect snow, finding that while closed‐conifer forests and snow interactions have been extensively studied in western North America, there are knowledge gaps for deciduous and mixed forests with dormant season leaf loss. We propose that there is an optimal, intermediate zone along a gradient of dormant season canopy cover (DSCC; the proportion of the ground area covered by the canopy during the dormant season), where peak snowpack depth and the potential for snow refugia will be greatest because the canopy‐mediated effects of snowpack sheltering (which can preserve snowpack) outweigh those of snowfall interception (which can limit snowpack). As an initial test of our hypothesis, we leveraged snowpack measurements in the northeastern United States spanning the DSCC gradient (low, 50% DSCC), including from 2 sites in Old Town, Maine; 12 sites in Acadia National Park, Maine; and 30 sites in the northern White Mountains of New Hampshire. Medium DSCC forests (typically mature mixed coniferous–deciduous forests) exhibited the deepest peak snowpacks, likely due to reduced snowfall interception compared to high DSCC forests and reduced snowpack loss compared to low DSCC forests. Many snow accumulation or snowpack studies focus on the contrast between coniferous and open sites, but our results indicate a need for enhanced focus on mixed canopy sites that could serve as snow refugia. Measurements of snowpack depth and timing across a wider range of forest canopies would advance understanding of canopy–snow interactions, expand the monitoring of changing winters, and support management of forests and snow‐dependent species in the face of climate change.

Context: Winter is generally considered the limiting season for wildlife populations living in high-latitude/altitude regions. Logically, managers have used temperature and snow variables or indices to predict survival and adjust harvest quotas according to winter severity. A common approach is to use a winter severity index (WSI) to evaluate the effects of winter severity on game species (e.g. ungulates). However, a WSI requires both snow and temperature data, ideally collected at broad spatial scales and at high temporal resolution (e.g. weekly), and can take considerable effort to collect the data. Aims: We evaluated the influence of several winter variables, including a WSI that has been used for predicting winter survival over the past several decades, on adult female white-tailed deer (Odocoileus virginianus) survival in northern New England. We also considered the influence of humans (anthropogenic influence), primarily in the form of artificial feeding, on deer survival. Methods: We monitored 200 radio-collared deer in four different management units that spanned the gradient of winter climate and anthropogenic influence in the region over the course of seven winters (2015–2021). We used known-fate, staggered-entry, binomial models in a Bayesian framework to evaluate factors that influenced deer survival. Key results: We recorded 64 mortalities that were primarily associated with predation in the northern regions but also from deer–vehicle collisions. Average (95% credible intervals [CI]) winter survival was 78% (66–89%), with the lowest and highest estimates occurring during the harshest (72%; CI = 54–85%) and mildest (82%; CI = 67–93%) winters respectively. The top survival model included snow depth under closed canopy and an additive effect of anthropogenic influence; snow depth had a negative effect, and anthropogenic influence had a positive effect on survival. However, WSI had low predictive power, and predicted higher survival in most winters and regions. Conclusions: Our study indicated that winter survival rates of adult female deer were most influenced by snow and anthropogenic factors in northern New England and that WSI indices for evaluating the impacts of winter on deer populations were not robust for monitoring survival and determining harvest quotas. Implications: Natural resource agencies should consider using alternative field methods (e.g. remote cameras) for monitoring annual snowpack and deer survival in high latitude regions. Further research is needed to determine which anthropogenic factors have a negative and positive impact on deer survival.

A primary prediction of climate change ecology is that species will track their climate niche poleward and upslope. However, studies have shown species responding in surprising ways. In this study, we aim to understand the impact of global change on species ranges by considering both climate and habitat changes. Using occupancy analysis of acoustic survey data in the mountains of the northeastern United States, we tested specific predictions of range responses to warming (shifting upslope), precipitation change (shifting downslope), and forest composition change (shifting downslope). We found that American red squirrels Tamiasciurus hudsonicus, key nodes in northern North American food webs, are not tracking increasing temperatures upslope, despite substantial warming in recent decades. Structural equation modeling indicates that red squirrel abundance is primarily influenced by red‐spruce forest cover, which has shifted downslope with recovery from historical logging and acid deposition. Accounting for the multiple dimensions of global change will enable better predictions and more effective conservation strategies.

Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail‐Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated four commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, and 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose (Alces alces) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy, and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental states are clearly identifiable. We discuss several avenues for future research and caveats for using multistate DM models for large‐scale population monitoring.

An important symbolic and subsistence animal for many Native American Tribes, the moose (Alces alces; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick (Dermacentor albipictus) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot‐based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western‐central Massachusetts, USA. We analyzed the data using occupancy and N‐mixture models. Detection probability peaked during mid‐October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub‐optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected.

Aim: Spatiotemporal variation in resource availability is a strong driver of animal distributions. In the northern hardwood and boreal forests of the northeastern United States, tree mast events provide resource pulses that drive the population dynamics of small mammals, including the American red squirrel (Tamiasciurus hudsonicus), a primary songbird nest predator. This study sought to determine whether mast availability ameliorates their abiotic limits, enabling red squirrel elevational distributions to temporarily expand and negatively impact high‐elevation songbirds.Location: Northeastern United States.Methods: We used two independent datasets to evaluate our hypotheses. First, we fit a dynamic occupancy model using data from camera trap surveys to evaluate red squirrel distributional responses to pulses in the tree mast. We also assessed population responses using systematic auditory surveys analysed with an open‐population binomial mixture model. Further, we used modelled red squirrel abundance in nest‐survival models to evaluate whether their abundance is correlated with the daily nest survival of three songbird species.Results: The tree mast provided a critical resource pulse that resulted in a two‐fold increase in the annual elevational distribution of red squirrels. The elevational distribution of red squirrels ranged from a minimum of ~450 m (range: 663–1145 m asl) following two consecutive years without a masting event to a maximum of over 1000 m (range: 443–1545 m asl) after a large mast event. The daily nest survival of three songbird species tended to decline with an increase in the abundance of red squirrels.Main Conclusions: Tree mast is a central biological phenomenon in many temperate and boreal forests. This study reveals how this resource pulse results in range changes in a small mammal that is both a seed and bird predator, as well as prey for many carnivores. Thus, understanding this phenomenon can inform the conservation and management of northern forests, including breeding songbirds.