Human population growth, land conversion, and hunting are accelerating defaunation in tropical forests. We asked how anthropogenic and ecological factors shape the site use (occupancy) of medium- to large-bodied mammals in an unprotected Amazonian landscape. From 2015–2022 we deployed camera traps at 293 stations across 12 spatially independent grids in four areas along the Las Piedras River, Madre de Dios, Peru. Using single-season, single-species occupancy models for 17 species, we evaluated anthropogenic covariates (distance to settlements, proximity to agriculture, land-use class: Conservation vs Mixed-Use) and environmental covariates (macrohabitat: floodplain vs terra firme, distance to river, Enhanced Vegetation Index, small/large prey indices). Detection was modeled with trail type (human vs wildlife trails/roads) and operable trap nights. We recorded 14,849 detections. Persecuted species showed strong responses to human disturbance: lowland tapir occupancy was lower near agriculture, and jaguar avoided agricultural sites. Environmental gradients were also important: for example, collared peccary occupancy increased near rivers, and ocelot and lowland tapir were more frequent in floodplain forest. Detection varied among species and was influenced by trail type—large felids were more often detected on human trails, whereas some prey were more frequently detected on wildlife trails. Both anthropogenic pressure and habitat features structure mammal assemblages in this unprotected region. Persecuted species provide sensitive indicators of ecosystem condition in mixed-use forests. Management should prioritize protecting riverine habitats and mitigating disturbance near agriculture, while tailoring actions to species-specific responses.

Species recovery can be influenced by a wide variety of factors, such that predicting the spatiotemporal dynamics of recovering species can be exceedingly difficult. These predictions, however, are valuable for decision makers tasked with managing species and determining their legal status. We applied a spatially explicit projection model to estimate population growth and viability of gray wolves (Canis lupus) from 2021 to 2070 in the state of Washington, USA, where wolves have been naturally recolonizing since the establishment of the first resident pack in 2008. Using this model, we predicted the effects of 12 scenarios relating to management actions (e.g., lethal removals by the state agency, translocation, recreational harvest) and system uncertainties (e.g., immigration from out of state, disease) on the probability of meeting Washington's wolf recovery goals, the probability of extinction, and other metrics related to population status. Population recovery was defined under Washington's Wolf Conservation and Management Plan as four breeding pairs in each of three recovery regions and three additional breeding pairs anywhere in the state. The baseline and two translocation scenarios indicated a high (> 90%) probability of wolf recovery in Washington by 2070, but scenarios related to harvest mortality (removal of 5% of the population every 6 months), increased lethal removals (removal of 8.53% of the population across the state each year), and cessation of immigration from out of state resulted in probabilities of < 0.20 (0.01, 0.04, and 0.17, respectively) of meeting recovery goals by 2070. Only two scenarios of 12 (increased harvest and lethal removals scenarios) resulted in a geometric mean of population growth <= 1, indicating long-term population stability or growth for most scenarios. Our results suggest that wolves will continue to recolonize Washington and that recovery goals are likely to be met so long as harvest and lethal removals are not at unsustainable levels and adjacent populations support immigration into Washington.

Predator hunting strategies, such as stalking versus coursing behaviors, are hypothesized to influence antipredator behaviors of prey and can describe the movement behaviors of predators themselves. Predators and prey may alter their movement in relation to predator hunting modes, yet few studies have evaluated how these strategies influence movement behaviors of free-ranging animals in a multiple-predator, multiple-prey system. We fit hidden Markov models (HMM) with movement data derived from >400 GPS-collared ungulates and large predators in eastern Washington, USA. We used these models to test our hypotheses that stalking (cougars [Puma concolor]) and coursing (gray wolves [Canis lupus]) predators would exhibit different broad-scale movement behaviors consistent with their respective hunting strategies in areas that increased the likelihood of encountering or capturing ungulate prey (e.g., habitats selected by deer [Odocoileus spp.]). Similarly, we expected that broadscale movement behaviors of prey would change in response to background levels of predation risk associated with each predator's hunting strategy. We found that predators and ungulate prey adjusted their broadscale movements in response to one another's long-term patterns of habitat selection but not based on differences in predator-hunting strategies. Predators changed their movement behaviors based on the type of prey, whereas ungulates generally reduced movement in areas associated with large predators, regardless of the predator's hunting strategy. Both predator and prey movements varied in response to landscape features but not necessarily based on habitat that would facilitate specific hunting behaviors. Our results suggest that predators and prey adjust their movements at broad temporal scales in relation to long-term patterns of risk and resource distributions, potentially influencing their encounter rates with one another at finer spatiotemporal scales. Habitat features further influenced changes in movement, resulting in a complex combination of movement behaviors in multiple-predator, multiple-prey systems.

As global wildfire activity increases, wildlife are facing greater exposure to hazardous smoke pollution - with unknown consequences for biodiversity. Research on the effects of smoke on wild animals is extremely limited, in part due to the inherent logistical challenges of observing how animals respond to smoke in real time. Passive acoustic monitoring may be a powerful tool to safely and effectively monitor biodiversity before, during, and after major smoke events. In this study, we used data collected from a large-scale network of bioacoustic recorders at 92 sites in central and eastern Washington state during August-September, 2019-2020 to investigate the effect of wildfire smoke on dawn soundscapes and, by extension, acoustically active wildlife. We used acoustic indices to document and characterize changes in soundscapes related to smoke exposure, including the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Normalized Difference Soundscape Index (NDSI). Higher values of these indices likely indicate higher levels of biodiversity in our study area. We hypothesized that wildfire smoke would reduce bird vocalizations, leading to declines in ACI, BI, and NDSI at dawn, when birds are most active. We used linear and quantile regression models to test for an effect of daily exposure to fine particulate matter (PM2.5), a marker of wildfire smoke, on the mean daily values and the upper 90th percentile of each index at dawn. We also conducted a before-during-after analysis of a particularly hazardous smoke event that impacted our study area on September 12-14, 2020. We did not observe linear effects of daily PM2.5 on average or peak daily values of acoustic indices; however, we did observe a significant reduction in ACI and BI during the three-day smoke event in 2020 and in the two weeks following this air pollution episode. Our results indicate that, on average, ACI and BI were reduced by 2.7 % and 15.9 % during and 1.5 % and 11.0 % afterward, respectively. These findings add further evidence that wildfire smoke alters soundscapes, likely due to changes in the presence, abundance, or behavior of acoustically active animals. Furthermore, our study demonstrates that wildfire smoke may have delayed and/or cumulative effects on acoustically active wildlife. Our study highlights the potential for passive acoustic monitoring to

Understanding relationships between environmental characteristics and variation in species occurrence and density can provide information for managing human activities, protected species, and species of commercial importance in a dynamic system. To identify environmental drivers associated with variation in harbor seal (Phoca vitulina) densities in the Salish Sea, Washington, we analyzed 20 years of boat-based survey data and environmental covariates using a hierarchical distance sampling model. We included spatial, temporal, and spatiotemporal environmental covariates in our model and produced fine-scale predictive maps displaying in-water estimated densities from our model results. We found that spatial covariates were the strongest predictors for harbor seal densities in the Salish Sea. Harbor seals were more abundant closer to major river mouths, near shore, in shallower waters, and in areas with more haul-out sites. Additionally, harbor seal density varied with shoreline type. Changes in predicted harbor seal spatial use of the Salish Sea varied but with little difference between breeding/molting and nonbreeding/nonmolting seasons. Our results revealed spatiotemporal variation in harbor seal fine-scale density in the Salish Sea, which are particularly important for conservation planning, as spatiotemporal variation in harbor seal density can exert heterogenous top-down effects on prey species populations, some of which are threatened.

The non-consumptive effects of predator-prey interactions are well-known for their ability to impact ecosystem structure and function. As anthropogenic pressures increase worldwide, it is essential to understand how they influence the non-consumptive effects of predator-prey interactions. Two anthropogenic activities that occur worldwide, hunting and livestock grazing, are known to impact the activity and space use of individual species. However, their effects on predator-prey interactions remain less understood. We evaluated how cattle and human hunting activities influenced the spatial and temporal overlap of five predator (black bear [Ursus americanus], bobcat [Lynx rufus], cougar [Puma concolor], coyote [Canis latrans], and gray wolf [C. lupus]) and four ungulate prey species (elk [Cervus canadensis], moose [Alces alces], mule deer [Odocoileus hemionus], and white-tailed deer [O. virginianus]) in Washington, USA, from 2018 – 2020. We used data from a large-scale camera trap study to test our hypotheses that anthropogenic activities influence spatiotemporal overlap of predators and prey depending on whether they avoid disturbance and risk or are attracted to potential food subsidies. We found cattle and hunter activity influenced species-specific occurrence and activity patterns but had more limited effects on predator-prey overlap. Namely, mesopredators and deer were generally more likely to use areas with cattle activity whereas cougars avoided cattle in the absence of wild prey. Predators and moose used areas with greater hunter activity, possibly creating a human shield for other ungulates where hunter activity was lower. Finally, most species moderately shifted their daily activity patterns in response to cattle and hunter activity. The resulting spatiotemporal patterns were only partially consistent with our expectations owing to the diverse and nuanced responses of predators and prey to cattle and hunter activity. Anthropogenic activities may help reduce predation risk under certain circumstances but do not necessarily create a human shield, underscoring the importance of considering anthropogenic effects on predator-prey interactions.

To reduce costs associated with nest building, some birds steal (kleptoparasitize) nest material. While this behavior is rarely reported in solitary nesting birds, it has been previously documented in 2 species of white-eyes, the Japanese White-eye (Zosterops japonicus) and the Indian White-eye (Z. palpebrosus). During surveys for Tinian Monarchs (Monarcha takatsukasae) on Tinian Island in the Northern Mariana Islands, we documented nest material kleptoparasitism by the Bridled White-eye (Z. conspicillatus). Through camera-trap footage and real-time observation, we observed Bridled White-eyes stealing material from 2 other forest bird species: the Micronesian Rufous Fantail (Rhipidura versicolor; n = 1 observation) and Tinian Monarch (n = 7). We documented nest material piracy during multiple nesting phases including building, incubation, and post-fledge, and from abandoned and depredated nests. This behavior was previously undocumented in the Bridled White-eye, and nest material kleptoparasitism is not documented in any other forest birds in the Mariana Islands. Kleptoparasitism of nest material has been known to cause destruction or abandonment of the host's nest. Given the potential implications of nest material kleptoparasitism on host nesting behavior and success, and that the Bridled White-eye is estimated to be the most abundant forest bird on Tinian, our observations warrant inquiry into how this behavior could be affecting the broader ecology of forest birds in the Mariana Islands. Guaha' p & aring;luma ni ma & aring;'& aring;mot m & aring;teriat ginen otru na chonchon pat gi otru n finihu', ma kleptoparasitize i materiat (kleptoparasitize - betbu/kleptoparasitism - n & aring;'an: esti na pal & aring;bra kumekeilelek-& ntilde;a i chinile' yan i usun materiat ni esta mana'setbe gi otru na chonchon) para u ma na'libi & aring;nu mama'chonchon ya para u na'rib & aring;ha lokkue' i tiempo para u ma e'materiat. Achokka' hassan esti na bida masusedi gi ayu na kl & aring;sen paluma ni manmama'chonchon na maisa, malili'e' esti na aktibid & aring;t gi dos na espisis nosa', i nosa' Hapon (Zosterops japonicus), yan i nosa' India (Z. palpebrosus). In dokumenta i kleptoparasitism gi nosa' (Z. conspicillatus) annai in chechegue' inaligaon i chichirikan Tini'an (Monarcha takatsukasae) gi iya Tini'an gi i Sankattan na Islas Mari & aring;nas. In li'e' i nosa' manma & ntilde;& aring;& ntilde;& aring;kki m & aring;teriat ginen i chenchon dos na espsis p & aring;luma: patitkulamenti i na'abak (Gu/Lu/Ti/Sa)/burike' ande'(Lu)/chichirika(Gu/Ti/Sa) (Micronesian Rufous Fantail/Rhipidura rufifrons; n = 1 kuentas ni mali'e'), yan i chichirikan Tini'an (n = 6). In dokumenta I kleptoparasitism gi i tiempon ma & ntilde;& aring;da' enteramente, achokka' mama'chonchon i paluma, kumuleleka i paluma, despues di ma dingu i chenchon i pechon, yan lokkue' gi eyu na chonchon ni esta maabandona. Ti dinekumenta esti na aktibid & aring;t gi i nosa' & aring;ntes di in li'e', yan lokkue' ti madokumenta i kleptoparasitism gi otru na espesis p & aring;luman h & aring;lomt & aring;no' guini gi i islas Mari & aring;nas. Matungo' ha' na i kleptoparasitism si & ntilde;a' ha distrosa i chenchon ni ma chuchule' i materi & aring;t, pat o sin & oacute; ha ma abandona i paluma ni chenchon-& ntilde;iha. Debi di maestudi & aring;yi taimanu inafefekta i lina'la' p & aring;luman h & aring;lomt & aring;no' ni kleptoparasitism guini gi i islas Mari & aring;nas sa' si & ntilde;a' i kleptoparasitism ginen i nosa' (matataha na guiya i mas abund & aring;nsia na p & aring;luman h & aring;lomt & aring;no' guini giya Tini'an) u m & aring;s ha na'chatsaga' i tiempon ma & ntilde;& aring;da' asta i uttimo-& ntilde;a.

The Amazon rainforest faces escalating human disturbances such as logging, mining, agriculture, and urbanization, leading to the conversion of primary forest into matrix habitat. This transformation's impact on mesocarnivores, specifically ocelots (Leopardus pardalis), is still largely unknown. In 2021, we deployed camera traps across a 174 km(2) study area in Las Piedras, Madre de Dios, Peru, containing mixed land use and protected forests. Utilizing kernel density functions, we compared temporal activity patterns and employed spatially explicit capture-recapture (SECR) models to assess density, encounter probability, and movement by habitat and trail type, as well as sex. Of the 293 captures, we identified 39 ocelots (21 females, 18 males), estimating an average density of 31.46 (SE 5.15) individuals per 100 km(2) with no significant difference between protected and mixed-use areas. Baseline detection and movement varied by sex, with male and female home ranges estimated at 17.14 and 4.10 km(2), respectively. Ocelot temporal activity patterns differed between the protected area and the mixed-use area, with increased nocturnality in areas of higher human impact. Our SECR results highlight that matrix habitats can support ocelot populations, emphasizing the need for further research on ocelot demographics in human-modified Amazon rainforest areas facing varying degrees of anthropogenic disturbance. Abstract in Spanish is available with online material.

ABSTRACTRecolonizing species exhibit unique population dynamics, namely dispersal to and colonization of new areas, that have important implications for management. A resulting challenge is how to simultaneously model demographic and movement processes so that recolonizing species can be accurately projected over time and space. Integrated population models (IPMs) have proven useful for making inference about population dynamics by integrating multiple data streams related to population states and demographic rates. However, traditional IPMs are not capable of representing complex dispersal and colonization processes, and the data requirements for building spatially explicit IPMs to do so are often prohibitive. Contrastingly, individual-based models (IBMs) have been developed to describe dispersal and colonization processes but do not traditionally integrate an estimation component, a major strength of IPMs. We introduce a framework for spatially explicit projection modeling that answers the challenge of how to project an expanding population using IPM-based parameter estimation while harnessing the movement modeling made possible by an IBM. Our model has two main components: [1] a Bayesian IPM-driven age- and state-structured population model that governs the population state process and estimation of demographic rates, and [2] an IBM-driven spatial model describing the dispersal of individuals and colonization of sites. We applied this model to estimate current and project future dynamics of gray wolves (Canis lupus) in Washington State, USA. We used data from 74 telemetered wolves and yearly pup and pack counts to parameterize the model, and then projected statewide dynamics over 50 years. Mean population growth was 1.29 (95% CRI 1.26-1.33) during initial recolonization from 2009-2020 and decreased to 1.03 (IQR 1.00-1.05) in the projection period (2021-2070). Our results suggest that gray wolves have a >99% probability of colonizing the last of Washington State’s three specified recovery regions by 2030, regardless of alternative assumptions about how dispersing wolves select new territories. The spatially explicit modeling framework developed here can be used to project the dynamics of any species for which spatial spread is an important driver of population dynamics.

Large terrestrial mammals increasingly rely on human-modified landscapes as anthropogenic footprints expand. Land management activities such as timber harvest, agriculture, and roads can influence prey population dynamics by altering forage resources and predation risk via changes in habitat, but these effects are not well understood in regions with diverse and changing predator guilds. In northeastern Washington state, USA, white-tailed deer (Odocoileus virginianus) are vulnerable to multiple carnivores, including recently returned gray wolves (Canis lupus), within a highly human-modified landscape. To understand the factors governing predator-prey dynamics in a human context, we radio-collared 280 white-tailed deer, 33 bobcats (Lynx rufus), 50 cougars (Puma concolor), 28 coyotes (C. latrans), and 14 wolves between 2016 and 2021. We first estimated deer vital rates and used a stage-structured matrix model to estimate their population growth rate. During the study, we observed a stable to declining deer population (lambda = 0.97, 95% confidence interval: 0.88, 1.05), with 74% of Monte Carlo simulations indicating population decrease and 26% of simulations indicating population increase. We then fit Cox proportional hazard models to evaluate how predator exposure, use of human-modified landscapes, and winter severity influenced deer survival and used these relationships to evaluate impacts on overall population growth. We found that the population growth rate was dually influenced by a negative direct effect of apex predators and a positive effect of timber harvest and agricultural areas. Cougars had a stronger effect on deer population dynamics than wolves, and mesopredators had little influence on the deer population growth rate. Areas of recent timber harvest had 55% more forage biomass than older forests, but horizontal visibility did not differ, suggesting that timber harvest did not influence predation risk. Although proximity to roads did not affect the overall population growth rate, vehicle collisions caused a substantial proportion of deer mortalities, and reducing these collisions could be a win-win for deer and humans. The influence of apex predators and forage indicates a dual limitation by top-down and bottom-up factors in this highly human-modified system, suggesting that a reduction in apex predators would intensify density-dependent regulation of the deer population owing to limited forage availability.

Background Due to anthropogenic climate change and historic fire suppression, wildfire frequency and severity are increasing across the western United States. Whereas the indirect effects of fire on wildlife via habitat change are well studied, less is known about the impacts of wildfire smoke on animal health and behavior. In this study, we explore the effects of wildfire smoke on the behavior of eight medium- to large-bodied mammalian species in a heterogenous study area in Washington, USA. We linked population-level activity metrics derived from camera trap data to concentrations of fire-specific fine particulate matter (PM2.5). We hypothesized that mammalian activity would decline during smoke events, as animals attempt to reduce potential health impacts of smoke inhalation. We used occupancy models and Poisson regression models to test the effect of fire-specific PM2.5 levels on daily detection probability and the number of detections per day, respectively, for each study species.Results While we did not observe any significant responses to daily mean concentrations of PM2.5 in the occupancy models, we found three species with significant responses in their rates of detections per day in the Poisson regression. Specifically, for each standard deviation increase in the daily mean concentration of PM2.5, there was a 12.9% decrease in the number of bobcat detections per day, an 11.2% decrease in the number of moose detections per day, and a 5.8% increase in the number of mule deer detections per day. In general, the effects of PM2.5 were small compared to other relevant covariates.Conclusions We generally found little evidence to support our hypothesis that animals would reduce their activity in response to wildfire smoke. However, our study demonstrated that mammals exhibited species-specific behavioral responses to smoke, which are possibly adaptive responses to reduce health impacts from smoke inhalation. Though we found only a few immediate behavioral responses to smoke exposure, we note that longer-term health consequences of smoke exposure for wildlife are also likely and generally unknown. Our study shows how camera traps, which are already widely used to study wildlife, can also be used to investigate the impacts of wildfire smoke on animal behavior and provides a step towards developing methods to better understand this increasing source of environmental stress on wildlife.

Abstract Human population growth and associated land conversion in tropical regions have led to habitat fragmentation, resource extraction, and increased hunting pressure, resulting in defaunation. To assess the impacts of human disturbances on 17 mammal species spanning both predator and prey guilds, we conducted an eight-year (2015–2022) camera trap study in the unprotected region of Madre de Dios, Peru. Analyzing 14,849 mammal captures across 293 sites with occupancy models, we assessed the influence of eight ecological and anthropogenic covariates on species occupancy and detection. We hypothesized that the most persecuted species would be most negatively affected by human disturbance. Our results supported this hypothesis; persecuted species including jaguars (Panthera onca), tapirs (Tapirus terrestris), brocket deer (Mazama spp.), collared peccaries (Pecari tajacu), and agoutis (Dasyprocta variegata) exhibited significant changes in occupancy probability in relation to distance from settlements, proximity to agriculture, and land use type. Contrary to our prediction, ocelot and white-lipped peccary occupancy probability did not change with human disturbance covariates; however, there were no detections of white-lipped peccaries in the first seven years of the study. We found that distance to the river was the most common ecological covariate where species occupancy was higher closer to the river. Given the importance of rivers as access points for humans, this emphasizes the significance of protecting riverine habitats in lowland Peruvian Amazonia. Our findings highlight the importance of considering species-specific responses and the need for targeted conservation measures to mitigate the negative effects of human activities on vulnerable and highly persecuted species.

Birds living in developed areas contend with numerous stressors, including human disturbance and light, noise, and air pollution. COVID-19 pandemic lockdowns presented a unique opportunity to disentangle these effects during a period of reduced human activity. We launched a community science project in spring 2020 to explore drivers of site use by and detection of common birds in cities under lockdown in the U.S. Pacific Northwest. Our goals were twofold: (1) consider how intensity of urbanization, canopy cover, and availability of bird feeders and bird baths influenced avian habitat use; and (2) quantify how daily changes in weather, air pollution, and human mobility influenced detection of birds. We analyzed 6,640 surveys from 367 volunteers at 429 monitoring sites using occupancy models for 46 study species. Neither land cover nor canopy cover influenced site use by 50% of study species, suggesting that backyard birds may have used a wider range of habitats during lockdowns. Human mobility affected detection of 76% of study species, suggesting that birds exhibited species-specific behavioral responses to day-to-day changes in human activity beginning shortly after initial lockdown restrictions were implemented. Our study also showcases how existing community science platforms can be leveraged to support local monitoring efforts.

Over the last decade, spatial capture–recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies — individual animals observed at specific locations and times — can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional “building blocks” of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.

Abstract Public interest in nature‐based recreation is growing, including visitation to protected areas. However, the level of recreation in these areas that causes detectable changes in wildlife behaviour remains unknown, and many studies that investigate wildlife responses to humans do so in high‐visitation areas. We used camera traps to investigate the spatial and temporal responses of brown bears (Ursus arctos), black bears (Ursus americanus), moose (Alces alces) and wolves (Canis lupis) to experimentally manipulated levels of human activity in Glacier Bay National Park, Alaska during summers 2017 and 2018. Human activity was restricted at some sites and concentrated at others, and these human impact treatments were swapped mid‐season. The park has very low on‐land visitation (~40,000 on‐land tourists per year), making it a unique study system to investigate wildlife responses to low levels of human activity. Detections did not exceed five per week for any species unless human activity was absent (zero photos of humans were taken). However, spatial and temporal patterns of wildlife activity in relation to human activity were nuanced and species specific. Moose shifted their activity patterns to better align with when people were most active. Black bears were more likely to be detected in areas of high human activity but used high‐use areas less intensely than low‐use areas. Wolves used areas of high human impact more intensely, but shifted their activity to be more strongly nocturnal. Our results highlight the importance of considering both spatial and temporal responses of wildlife to human activity. Additionally, and arguably most importantly, we detected changes in wildlife behaviour in response to humans in a national park with relatively low tourism. Although natural processes may dominate in protected areas, our results indicate that even low levels of human activity can alter wildlife behaviour. Synthesis and applications. We demonstrated that nearly any level of human activity in a protected area may alter wildlife behaviour. However, it is unreasonable to expect protected areas to be completely devoid of human activity. Thus, management of these areas will need to balance the desires of humans to view wildlife with the likely impacts. Read the free Plain Language Summary for this article on the Journal blog.

Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018-2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.

Tourism is increasing in tundra ecosystems across the world, yet its influence on bird communities and its interaction with other drivers of change is poorly known. To help fill this gap, we paired an interview-based survey of 11 people with local knowledge of Denali National Park and Preserve, with an occupancy study of 15 bird species in relation to road proximity, traffic volume and hiking. Interviewees noted declines in American Golden Plover Pluvialis dominica, Arctic Tern Sterna paradisaea, Long-tailed Jaeger Stercorarius longicaudus and Northern Wheatear Oenanthe oenanthe over the past five decades. Our occupancy study confirmed these reports as we detected no Arctic Terns, few Northern Wheatears, and found both plovers and jaegers to be sensitive to hiking. Occupancy of tundra and shrub habitats by American Golden Plover, American Tree Sparrow Spizelloides arborea, Lapland Longspur Calcarius lapponicus, Long-tailed Jaeger and Willow Ptarmigan Lagopus lagopus declined with increasing hiking intensity. We found that occupancy probability of tundra by Horned Lark Eremophia alpestris increased, while that of the shrub-tolerant Wilson's Warbler Cardellina pusilla decreased, with distance from the park road. Detection of species varied based on survey length, noise, start time, presence of a trail and date. The knowledge gained from this study reveals a loss in avian diversity over the past few decades that has the potential to cause a shifted baseline syndrome, and ongoing threats of hiking to sensitive tundra-breeding birds. Park managers should seek to balance human recreation with the needs of sensitive tundra-breeding birds to further protect species of conservation concern. This may be done by not building new trails in tundra, limiting access to tundra hiking areas during the early breeding season, reducing the spatial extent of hiking by improving maintained trails and designating a single, maintained path in areas with multiple unofficial hiking tracks, educating tourists about tundra-nesting birds and closing especially important nesting areas to the public.

ABSTRACT Wildfire smoke is likely to have direct health effects on birds as well as influence movement, vocalization, and other avian behaviors. These behavioral changes may affect if and how birds are observed in the wild, although research on the effects of wildfire smoke on bird behavior is limited. To evaluate how wildfire smoke affects detection of birds, we combined data from eBird, an online community science program, with data from an extensive network of air quality monitors in the state of Washington over a 4-year period. We assessed how PM2.5, a marker of smoke pollution, affected the probability of observing 71 bird species during the wildfire seasons of 2015–2018 using bird observations from 62,908 eBird checklists. After accounting for habitat, weather conditions, seasonality, and survey effort, we found that PM2.5 affected the probability of observing 37% of study species. The ambient concentration of PM2.5 was negatively associated with the probability of observing 16 species and positively associated with the probability of observing 10 species, indicating that birds exhibit species-specific behavioral changes during wildfire smoke events that influence how they are observed. Our results suggest that wildfire smoke impacts the presence, availability, and/or perceptibility of birds. Impacts of smoke pollution on human observers, such as impaired visibility, may also influence detection of birds. These results provide a foundation for developing mechanistic hypotheses to explain how birds, and our studies of them, are impacted by wildfire smoke. Given the projected increase in large-scale wildfire smoke events under future climate change scenarios, understanding how birds are affected by wildfire smoke—and how air pollution may influence our ability to detect them—are important next steps to inform wildlife research and avian conservation. LAY SUMMARY Wildfire smoke events threaten public health and are likely to impact the health and behavior of birds, influencing their perceptibility. In this study, we characterized how wildfire smoke affects detection of birds. We assessed how PM2.5, a marker of wildfire smoke, affected the probability of observing 71 bird species in Washington State. We found that outdoor concentrations of PM2.5 affected the probability of observing 37% of study species. PM2.5 was negatively related to the detection of 16 species and positively related to the detection of 10 species. Our results suggest that wildfire smoke impacts the presence, availability, and/or perceptibility of birds. Given the projected increase in the frequency of large-scale wildfire smoke events, understanding how birds are affected by smoke—and how air pollution may influence our ability to detect birds—are critical next steps in avian conservation.