Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population

Influence of an extreme event-the COVID-19 pandemic-On establishment of and data collection by a citizen science project

The recent rising incidence of extreme natural events may significantly influence the implementation of citizen science projects, including the success of outreach strategies and the quality and scope of data collection. The MassMammals Watch and subsidiary MassBears citizen science projects, initiated during the height of the pandemic, recruit volunteers to submit sightings of black bears and other mammals. In this study, we evaluated the methods we employed for engaging and retaining community volunteers during a period of intense social restrictions, and we assessed whether such conditions were associated with spatial biases in our collected data. Newspaper features were more likely to recruit volunteers who engaged with the project multiple times, but social media and internet presence were important for reaching a larger audience. Bear sighting submissions peaked in number and were more likely to be in forested areas during 2020, the height of the pandemic, compared to later years, a pattern which we suggest stems from an increased desire to participate in outdoor activities in light of social distancing measures during that year. Such shifts in patterns of data collection are likely to continue, particularly in response to increasing extreme weather events associated with climate change. Here, we both make recommendations on optimal outreach strategies for others initiating citizen science programs and illustrate the importance of assessing potential biases in data collection imposed by extreme circumstances.

Nearby night lighting, rather than sky glow, is associated with habitat selection by a top predator in human-dominated landscapes

Artificial light at night (ALAN) is increasing in extent and intensity across the globe. It has been shown to interfere with animal sensory systems, orientation and distribution, with the potential to cause significant ecological impacts. We analysed the locations of 102 mountain lions (Puma concolor) in a light-polluted region in California. We modelled their distribution relative to environmental and human-disturbance variables, including upward radiance (nearby lights), zenith brightness (sky glow) and natural illumination from moonlight. We found that mountain lion probability of presence was highly related to upward radiance, that is, related to lights within approximately 500 m. Despite a general pattern of avoidance of locations with high upward radiance, there were large differences in degree of avoidance among individuals. The amount of light from artificial sky glow was not influential when included together with upward radiance in the models, and illumination from moonlight was not influential at all. Our results suggest that changes in visibility associated with lunar cycles and sky glow are less important for mountain lions in their selection of light landscapes than avoiding potential interactions with humans represented by the presence of nearby lights on the ground. This article is part of the theme issue 'Light pollution in complex ecological systems'.

Citizen science reporting indicates geographic and phenotypic drivers of road use and mortality in a threatened rattlesnake

Roads may influence selection on phenotypic traits of wildlife. In particular, the likelihood of vehicle collisions with wildlife may vary depending on body coloration in contrast to the road, which may be exaggerated by cultural attitudes towards the species. The timber rattlesnake Crotalus horridus is a threatened species that varies widely in coloration, and their color pattern could influence thermoregulatory use of roads and visibility to motorists. Moreover, better camouflaged snakes may have higher road mortality in areas where environmental interest is lower and, perhaps, negative attitudes towards wildlife are more prevalent. We used citizen scientist observations of timber rattlesnakes from iNaturalist and categorized for each rattlesnake the surface they were on, color pattern, and whether they were alive. We combined iNaturalist data with Google Trends data to characterize regional variation in environmental interest. We discovered that lighter-colored snakes were more likely to be found on roads, as were snakes further south, west, and on warmer days. Once on a road, coloration didn’t influence survival regardless of road type or environmental interest. However, snakes on asphalt roads or on southern roads were more likely to be found dead. The higher likelihood of lighter colored snakes being found on roads suggests that they are at greater overall risk of road death, potentially selecting for darker coloration. Citizen scientist behavior may at least partly underlie the influence of latitude on the results, however, and further work in the application of citizen science data to such research questions is warranted.

Understanding Species–Habitat Associations: A Case Study with the World’s Bears

Habitat modeling is one of the most common practices in ecology today, aimed at understanding complex associations between species and an array of environmental, bioclimatic, and anthropogenic factors. This review of studies of seven species of terrestrial bears (Ursidae) occupying four continents examines how habitat models have been employed, and the functionality of their predictions for management and conservation. Bear occurrence data have been obtained at the population level, as presence points (e.g., sign surveys or camera trapping), or as locations of individual radio-collared animals. Radio-collars provide greater insights into how bears interact with their environment and variability within populations; they are more commonly used in North America and Europe than in South America and Asia. Salient problematic issues apparent from this review included: biases in presence data; predictor variables being poor surrogates of actual behavioral drivers; predictor variables applied at a biologically inappropriate scale; and over-use of data repositories that tend to detach investigators from the species. In several cases, multiple models in the same area yielded different predictions; new presence data occurred outside the range of predicted suitable habitat; and future range projections, based on where bears presently exist, underestimated their adaptability. Findings here are likely relevant to other taxa.

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.

Assessing the Vulnerabilities of Vertebrate Species to Light and Noise Pollution: Expert Surveys Illuminate the Impacts on Specialist Species

Global expansion of lighting and noise pollution alters how animals receive and interpret environmental cues. However, we lack a cross-taxon understanding of how animal traits influence species vulnerability to this growing phenomenon. This knowledge is needed to improve the design and implementation of policies that mitigate or reduce sensory pollutants. We present results from an expert knowledge survey that quantified the relative influence of 21 ecological, anatomical, and physiological traits on the vulnerability of terrestrial vertebrates to elevated levels of anthropogenic lighting and noise. We aimed not only to quantify the importance of threats and the relative influence of traits as viewed by sensory and wildlife experts, but to examine knowledge gaps based on the variation in responses. Identifying traits that had less consensus can guide future research for strengthening ecologists' and conservation biologists' understanding of sensory abilities. Our findings, based on 280 responses of expert opinion, highlight the increasing recognition among experts that sensory pollutants are important to consider in management and conservation decisions. Participant responses show mounting threats to species with narrow niches; especially habitat specialists, nocturnal species, and those with the greatest ability to differentiate environmental visual and auditory cues. Our results call attention to the threat specialist species face and provide a generalizable understanding of which species require additional considerations when developing conservation policies and mitigation strategies in a world altered by expanding sensory pollutant footprints. We provide a step-by-step example for translating these results to on-the-ground conservation planning using two species as case studies.