Global species extinction rates are orders of magnitude above the background rate documented in the fossil record. However, recent data syntheses have found mixed evidence for patterns of net species loss at local spatial scales. For example, two recent data meta-analyses have found that species richness is decreasing in some locations and is increasing in others. When these trends are combined, these papers argued there has been no net change in species richness, and suggested this pattern is globally representative of biodiversity change at local scales. Here we reanalyze results of these data syntheses and outline why this conclusion is unfounded. First, we show the datasets collated for these syntheses are spatially biased and not representative of the spatial distribution of species richness or the distribution of many primary drivers of biodiversity change. This casts doubt that their results are representative of global patterns. Second, we argue that detecting the trend in local species richness is very difficult with short time series and can lead to biased estimates of change. Reanalyses of the data detected a signal of study duration on biodiversity change, indicating net biodiversity loss is most apparent in studies of longer duration. Third, estimates of species richness change can be biased if species gains during post-disturbance recovery are included without also including species losses that occurred during the disturbance. Net species gains or losses should be assessed with respect to common baselines or reference communities. Ultimately, we need a globally coordinated effort to monitor biodiversity so that we can estimate and attribute human impacts as causes of biodiversity change. A combination of technologies will be needed to produce regularly updated global datasets of local biodiversity change to guide future policy. At this time the conclusion that there is no net change in local species richness is not the consensus state of knowledge.

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As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products.

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil properties. Slope, soils, land cover and climate are also important factors that require consideration. Many modelling tools and datasets have been developed to assist remediation teams, but process-based and spatially explicit models are currently underutilised compared with simpler, lumped models because they are difficult to set up and require properly formatted spatial inputs. To facilitate the use of models in conjunction with remote sensing observations, we developed an online spatial database that rapidly generates properly formatted modelling datasets modified by user-supplied soil burn severity maps. Although assembling spatial model inputs can be both challenging and time-consuming, the methods we developed to rapidly update these inputs in response to a natural disaster are both simple and repeatable. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

Soil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network's elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring.