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1. Extensive research has demonstrated that urbanization strongly alters ecological processes, often perniciously. However, quantifying the magnitude of urban effects and determining how generalized they can be across systems depends on the ways in which urbanization is measured and modelled. 2. We coupled a formal literature survey with a novel conceptual framework to document and synthesize the myriad of metrics used to quantify urbanization. The framework enables clear cataloguing of urban metrics by identifying (a) the urban component measured, (b) the method of measurement, (c) the metric's spatial scale and (d) the metric's temporal nature. Thus, the framework comprehensively captures the what, how, where and when of urban metrics.3. We documented striking variability in urban metrics with respect to which urban components were measured as well as how, where and when they were quantified. Overall, our survey revealed that they tended to be: (a) structurally focused, (b) methodologically simplistic, (c) spatially variable and (d) temporally static. 4. Synthesis and applications. Many metrics are used to quantify urbanization or 'urbanness'. The variation in urban metrics complicates the development of theory, comparisons of findings across studies, and the implementation of management and conservation actions. To pave a clear path forward for more efficient and policyrelevant urban research, we systematically organized urban metrics using a simple, flexible and comprehensive framework. The framework clarifies what urbanization actually means in empirical practice and identifies several crucial areas for future research, including: (a) systematic assessments of urban metrics across multiple scales, (b) an increased and judicious use of more complex urban metrics aimed at evaluating both mechanistic and broad-scale correlative ecological hypotheses, and (c) an increased emphasis on the socio-economic aspects of urban effects. K E Y W O R D S framework, human-natural systems, spatio-temporal scale, urban gradient, urban metrics, urbanization, urban-ness, variability

Spatial scale is fundamental in understanding species-landscape relationships because species' responses to landscape characteristics typically vary across scales. Nonetheless, such scales are often unidentified or unreliably predicted by theory. Many landscapes worldwide are urbanizing, yet the spatial scaling of species' responses to urbanization is poorly understood. We investigated the spatial scaling of urbanization effects on a community of 15 mammal species using ~60 000 wildlife detections collected from a constellation of 207 camera traps across an extensive urban park system. We embedded a bivariate Gaussian kernel in hierarchical multi-species models to determine two scales of effect (a scale of maximal effect and a broader scale of cumulative landscape effect) for two biological responses (occupancy and site visit frequency) across two seasons (winter and summer) for each species. We then assessed whether scales of effect varied according to theoretical predictions associated with biological responses and species traits (body size and mobility). Scales of effect ranged from < 50 m to > 9000 m and varied among species, but not as predicted by theory. Species' occupancy generally showed a weak response to urbanization and the scale of this effect was both highly uncertain and consistent across species. We did not detect any relationship between scales of effect and species' body size or mobility, nor was there any evident pattern of scaling across biological response or seasons. These results imply that 1) urbanization effects on mammals manifest across a very broad spectrum of spatial scales, and 2) current theories that a priori predict the scale at which urbanization affects mammals may be of limited use within a given system. Overall, this study suggests that developing general theory regarding the scaling of species-landscape relationships requires additional empirical work conducted across multiple species, systems and timescales.

Context The use of camera traps in ecological research has grown exponentially over the past decade, but questions remain about the effect of camera-trap settings on ecological inference. The delay-period setting controls the amount of time that a camera trap is idle between motion-activated triggers. Longer delay periods may potentially extend battery life, reduce data-storage requirements, and shorten data-analysis time. However, they might result in lost data (i.e. missed wildlife detections), which could bias ecological inference and compromise research objectives. Aims We aimed to examine the effect of the delay period on (1) the number of camera-trap triggers, (2) detection and site-occupancy probabilities for eight mammalian species that varied in size, movement rate and commonness and (3) parameter estimates of habitat-based covariates from the occupancy models for these species. Methods We deployed 104 camera traps for 4 months throughout an extensive urban park system in Cleveland, Ohio, USA, using a spatially random design. Using the resultant data, we simulated delay periods ranging from 10s to 60min. For each of these delay periods and for each of our eight focal species, we calculated the number of camera-trap triggers and the parameter estimates of hierarchical Bayesian occupancy models. Key results A simulated increase in the delay period from 10s to 10min decreased the number of triggers by 79.6%, and decreased detection probability and occupancy probability across all species by 1.6% and 4.4% respectively. Further increases in the delay period (i.e. from 10 to 60min) resulted in modest additional reductions in the number of triggers and detection and occupancy probabilities. Variation in the delay period had negligible effects on the qualitative interpretations of habitat-based occupancy models for all eight species. Conclusions Our results suggest that delay-period settings ranging from 5 to 10min can drastically reduce data-storage needs and analysis time without compromising inference resulting from occupancy modelling for a diversity of mammalian species. Implications Broadly, we provide guidance on designing camera-trap studies that optimally trade-off research effort and potential bias, thereby increasing the utility of camera traps as ecological research tools.

ContextCamera traps are one of the most popular tools used to study wildlife worldwide. Numerous recent studies have evaluated the efficiency and effectiveness of camera traps as a research tool. Nonetheless, important aspects of camera-trap methodology remain in need of critical investigation. One such issue relates to camera-trap viewshed visibility, which is often compromised in the field by physical obstructions (e.g. trees) or topography (e.g. steep slopes). The loss of visibility due to these obstructions could affect wildlife detection rates, with associated implications for study inference and management application. AimsWe aimed to determine the effect of camera-trap viewshed obstruction on wildlife detection rates for a suite of eight North American species that vary in terms of ecology, commonness and body size. MethodsWe deployed camera traps at 204 sites throughout an extensive semi-urban park system in Cleveland, Ohio, USA, from June to September 2016. At each site, we quantified camera-trap viewshed obstruction by using a cover-board design. We then modelled the effects of obstruction on wildlife detection rates for the eight focal species. Key resultsWe found that detection rates significantly decreased with an increasing viewshed obstruction for five of the eight species, including both larger and smaller mammal species (white-tailed deer, Odocoileus virginianus, and squirrels, Sciurus sp., respectively). The number of detections per week per camera decreased two- to three-fold as visibility at a camera site decreased from completely free of obstruction to mostly obstructed. ConclusionsThese results imply that wildlife detection rates are influenced by site-level viewshed obstruction for a variety of species, and sometimes considerably so. ImplicationsResearchers using camera traps should address the potential for this effect to ensure robust inference from wildlife image data. Accounting for viewshed obstruction is critical when interpreting detection rates as indices of abundance or habitat use because variation in detection rate could be an artefact of site-level viewshed obstruction rather than due to underlying ecological processes.