To understand ecological phenomena, it is necessary to observe their behaviour across multiple spatial and temporal scales. Since this need was first highlighted in the 1980s, technology has opened previously inaccessible scales to observation. To help to determine whether there have been corresponding changes in the scales observed by modern ecologists, we analysed the resolution, extent, interval and duration of observations (excluding experiments) in 348 studies that have been published between 2004 and 2014. We found that observational scales were generally narrow, because ecologists still primarily use conventional field techniques. In the spatial domain, most observations had resolutions ≤1 m and extents ≤10,000 ha. In the temporal domain, most observations were either unreplicated or infrequently repeated (>1 month interval) and ≤1 year in duration. Compared with studies conducted before 2004, observational durations and resolutions appear largely unchanged, but intervals have become finer and extents larger. We also found a large gulf between the scales at which phenomena are actually observed and the scales those observations ostensibly represent, raising concerns about observational comprehensiveness. Furthermore, most studies did not clearly report scale, suggesting that it remains a minor concern. Ecologists can better understand the scales represented by observations by incorporating autocorrelation measures, while journals can promote attentiveness to scale by implementing scale-reporting standards.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of onroad concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R 2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1−2) repeated drives but obtained better cross-validation R 2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

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Highlights• DIYlandcover crowdsources the generation of landcover data, using human pattern recognition skill to create accurate maps with rich geometric detail.• It incorporates representative sampling and worker-specific accuracy assessment protocols, and connects to a large online job market. This design addresses three problems with crowdsourced mapping: representativity; data reliability; product delivery speed.• In a trial case, South African cropland was mapped with 91% accuracy by novice workers. A scaling up analysis found that an Africa-wide cropland map could potentially be developed using this software for $2-3 million within 1.2-3.8 years. AbstractAccurate landcover maps are fundamental to understanding socio-economic and environmental patterns and processes, but existing datasets contain substantial errors. Crowdsourcing map creation may substantially improve accuracy, particularly for discrete cover types, but the quality and representa- * Corresponding author

Lower-cost tropical forest restoration methods, particularly those framed as win-win business-protected area partnerships, could dramatically increase the scale of tropical forest restoration activities, thereby providing a variety of societal and ecosystem benefits, including slowing both global biodiversity loss and climate change. Here we describe the long-term regenerative effects of a direct application of agricultural waste on tropical dry forest. In 1998, as part of an innovative agricultural waste disposal service contract, an estimated 12,000 Mg of processed orange peels and pulp were applied to a 3 ha portion of a former cattle pasture with compacted, rocky, nutrient-poor soils characteristic of prolonged fire-based land management and overgrazing in Área de Conservación Guanacaste, northwestern Costa Rica. After 16 years, the experimental plot showed a threefold increase in woody plant species richness, a tripling of tree species evenness (Shannon Index), and a 176% increase in aboveground woody biomass over an adjacent control plot. Hemispheric photography showed significant increases in canopy closure in the area where orange waste was applied relative to control. Orange waste deposition significantly elevated levels of soil macronutrients and important micronutrients in samples taken 2 and 16 years after initial orange waste application. Our results point to promising opportunities for valuable synergisms between agricultural waste disposal and tropical forest restoration and carbon sequestration.