Remington J. Moll

With the suppression of continuous tree canopies in cities, some arboreal mammals resort to using overhead power line networks to navigate urban landscapes. Surprisingly, although sightings of mammals moving on electrical wires are common in tropical and temperate cities, we still know very little about mammal activity patterns on these human-built structures. Importantly, electrocution is a significant cause of death for mammal species that travel along overhead wires and climb onto transformers. Here, we deployed arboreal camera traps to quantify spatial and temporal activity patterns of canopy-dwelling mammals moving on electric wires around one of the largest urban forests in Brazil. Also, we investigated seasonal variation in their temporal activity patterns across the austral dry and rainy seasons. We obtained 9,158 independent records of four species of arboreal or semi-arboreal mammals, with the Black capuchin monkey ( Sapajus nigritus ) accounting for most of the detections, followed by the Paraguayan hairy dwarf porcupine ( Coendou spinosus ). Overall, species-specific activity patterns remained consistent across seasons and aligned with those observed in natural habitats, indicating a convergent diel niche in urban and natural environments. Our results shed new light on the neglected ecology and natural history of mammals that use electric wires to navigate urban spaces, providing insights to attenuate human-wildlife conflicts and to inform the planning of more wildlife-friendly cities.

Accurate estimates of wildlife densities are important for understanding population dynamics and informing management decisions, but obtaining such estimates can be logistically challenging and costly. Drones and associated sensors provide a novel and potentially efficient pathway to estimate wildlife densities but require critical evaluation of efficacy across large spatial scales. We evaluated the capability of drones to estimate moose ( Alces alces ) density across a large landscape (~12,000 km ² ) in northern New Hampshire, USA, using infrared imagery collected via local drone surveys. Our objective was to model moose population densities using drones while accounting for imperfect detection (sightability) in forested landscapes. We also compared drone‐based density estimates to existing estimates derived from hunter surveys. Using a stratified random study design, we conducted 62 drone surveys that covered 64.6 km ² (0.5% of the study area) during January and February 2024. We fit 2 Bayesian binomial mixture models that accounted for sightability to 33 adult moose detections. A simple survey model resulted in a density estimate of 0.64 moose/km ² across the study area (90% credible interval [CrI] = 0.56–0.77 moose/km ² ). A second spatial model incorporated covariates to make extrapolated predictions of moose densities at the wildlife management unit (WMU) scale across the study area. Overall, both drone‐based densities were approximately twice the moose density estimated from deer hunter surveys. The precision of drone estimates from the survey model was within the benchmark range for moose management (90% CrI within ±25% of mean), but extrapolated predictions from the spatial model based on environmental covariates were less precise and exhibited somewhat weak correlation (Spearman's correlation = 0.42) with hunter survey estimates at the WMU scale. Our study is among the first to employ a thermal sensor via drone to estimate the density of an unmarked ungulate across a large spatial scale (thousands of square kilometers) using local sampling within a stratified random study design. Our work suggests that drones offer a promising method for estimating density over broad spatial scales without the need for tagged or global positioning system (GPS)‐collared individuals.

White‐tailed deer ( Odocoileus virginianus ) ecology has received significant research attention, yet there is a need for further synthetic work on the species across environmental contexts and research areas. Opportunistic natural history accounts can help identify new conceptual links and research directions by offering new observations and ideas, synthesizing context from the literature, and highlighting gaps in understanding. We provide an opportunistic account of a 2.5 km movement of a white‐tailed deer walking, wading, and swimming along Nantasket Beach in a suburban context in Massachusetts, USA. The deer displayed behaviors that could potentially be reflective of trauma, disorientation, or illness, including possibly symptomatic head movements and crouching behavior. The behavior of the deer may also have been influenced by the coastal and suburban context in which the observation took place. Our account explores swimming, deer behavior, and possible illness in a coastal, suburban context. We highlight the need for additional research on the ecology and management of white‐tailed deer, including swimming and disease in coastal, marine, and developed contexts, and intersections across these areas.

Primary dispersers of seeds and spores play critical roles in structuring the distributions of species, yet the role of predators as secondary dispersers remains largely unknown. This is especially true of mycorrhizal fungi, which often rely on small mammals to consume and disperse spores. We investigated how predator size, diet, and movement influence secondary spore dispersal in a terrestrial carnivore community by quantifying spore loads in scats (dispersal quantity) and integrating movement rates with gut passage time to determine dispersal distance (dispersal quality). Spores in carnivore scats increased with consumption of small mammals and transport of spores closely tracked home range movements. Larger carnivores deposited fewer spores but moved them farther from their source, creating a continuum between the quantity and quality of dispersal effectiveness. Our findings highlight the importance of carnivores as long‐distance dispersers of mycorrhizal fungi and reveal how trophic interactions contribute to ecosystem functioning through secondary dispersal.

Circadian rhythms are a mechanism by which species adapt to environmental variability and fundamental to understanding species behavior. However, we lack data and a standardized framework to accurately assess and compare temporal activity for species during rapid ecological change. Through a global network representing 38 countries, we leveraged 8.9 million mammalian observations to create a library of 14,587 standardized diel activity estimates for 445 species. We found that less than half the species’ estimates were in agreement with diel classifications from the reference literature and that species commonly used more than one diel classification. Species diel activity was highly plastic when exposed to anthropogenic change. Furthermore, body size and distributional extent were strongly associated with whether a species is diurnal or nocturnal. Our findings provide essential knowledge of species behavior in an era of rapid global change and suggest the need for a new, quantitative framework that defines diel activity logically and consistently while capturing species plasticity.

Many small mammal populations exhibit dramatic annual fluctuations, with abundance sometimes varying by orders of magnitude across years. However, there remains much debate about the factors that drive changes in abundance. Long-term datasets provide important opportunities to further investigate these phenomena. Using 30 yr of data from a study at Bartlett Experimental Forest in north-central New Hampshire, USA, we compare the relative effects of pulsed food resources (beech mast), forest structure, and weather on the population dynamics of 5 rodents and 4 shrew species. We estimated detection-corrected abundances using Bayesian N-mixture models. Mast, forest structure, and weather all influenced abundance; however, responses varied among species, and no single variable proved to be broadly influential across all species. More generally, our results suggest that for most species analyzed, mast availability and weather patterns have a greater influence on abundance fluctuations than forest structure. However, our results suggest that species are not responding to changes in the environment in the same way. Given that small mammals are ubiquitous and serve key ecological roles, a better understanding of the drivers of small mammal population fluctuations could have broad-reaching implications for forest ecology and management.

Variation in animal abundance is shaped by scale‐dependent habitat, competition, and anthropogenic influences. Coyotes Canis latrans have dramatically increased in abundance while expanding their range over the past 100 years. Management goals typically seek to lower coyote populations to reduce their threats to humans, pets, livestock and sensitive prey. Despite their outsized ecological and social roles in the Americas, the factors affecting coyote abundance across their range remain unclear. We fit Royle–Nichols abundance models at two spatial scales in a Bayesian hierarchical framework to three years of data from 4587 camera trap sites arranged in 254 arrays across the contiguous USA to assess how habitat, large carnivores, anthropogenic development and hunting regulations affect coyote abundance. Coyote abundance was highest in southwestern USA and lowest in the northeast. Abundance responded to some factors as expected, including positive (soft mast, agriculture, grass/shrub habitat, urban–natural edge) and negative (latitude and forest cover) relationships. Colonization date had a negative relationship, suggesting coyote populations have not reached carrying capacity in recently colonized regions. Several relationships were scale‐dependent, including urban development, which was negative at local (100‐m) scales but positive at larger (5‐km) scales. Large carnivore effects were habitat‐dependent, with sometimes opposing relationships manifesting across variation in forest cover and urban development. Coyote abundance was higher where human hunting was permitted, and this relationship was strongest at local scales. These results, including a national map of coyote abundance, update ecological understanding of coyotes and can inform coyote management at local and landscape scales. These findings expand results from local studies suggesting that directly hunting coyotes does not decrease their abundance and may actually increase it. Ongoing large carnivore recoveries globally will likely affect subordinate carnivore abundance, but not in universally negative ways, and our work demonstrates how such effects can be habitat and scale dependent.

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point‐count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out‐performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species‐specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision‐making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.