Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for measuring Essential Biodiversity Variables. Recent advances in camera technology and methods enable researchers to estimate changes in abundance and distribution for entire communities of animals and to identify global drivers of biodiversity trends. We suggest that interconnected networks of remote cameras will soon monitor biodiversity at a global scale, help answer pressing ecological questions, and guide conservation policy. This global network will require greater collaboration among remote‐camera studies and citizen scientists, including standardized metadata, shared protocols, and security measures to protect records about sensitive species. With modest investment in infrastructure, and continued innovation, synthesis, and collaboration, we envision a global network of remote cameras that not only provides real‐time biodiversity data but also serves to connect people with nature.

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models.amplification of temperature increase ͉ climate change ͉ vector-transmitted disease ͉ mosquito population dynamics

The survival of approximately 235 000 individual tropical trees and saplings in the 50 ha permanent plot on Barro Colorado Island (BCI), Panama was analyzed over a 13‐year interval (1982–1995) as a function of four biotic neighborhood variables: (i) total stem density; (ii) conspecific density; (iii) relative plant size; and (iv) relative species richness. These neighborhood variables were measured in annular rings of width 2.5 m, extending 30 m from a given focal plant, and in one more distant annulus at 47.5–50 m. Because survival was spatially autocorrelated, a Gibbs sampler and a Monte Carlo Markov chain method were used for fitting an autologistic regression model to obtain unbiased estimates of parameter variances for hypothesis testing. After pooling all species at the community level, results showed that all four variables had significant and often strong effects on focal plant survival. Three of the four variables had negative effects on focal plant survival; relative plant size was the only variable with a positive effect (18% increase in the survival odds ratio). The variables with a negative effect on the survival odds ratio, in order of their effect strength in the nearest annulus, were: stem density (a 70% reduction in the survival odds ratio), conspecific density (50% reduction) and species richness (13% reduction). A guild‐level analysis revealed considerable heterogeneity among guilds in their responses to these variables. For example, survival of gap species showed a much larger positive response to relative plant size than did survival of shade‐tolerant species. Survival of shrub species was positively affected by conspecific density, but canopy tree survival was negatively affected. Conspecific density negatively affected survival of rare species much more strongly than survival of common species. The neighborhood effects of conspecific density disappear within approximately 12–15 m of the focal plant. Although locally strong, the rapid spatial decay of these effects raises unanswered questions about their quantitative contribution to the maintenance of tree diversity on landscape scales in the BCI forest.

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter-or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solv...