Global expansion of Cytisus scoparius (L.) Link over 200 years.

Cytisus scoparius (L.) Link, commonly referred to as Scotch broom, is a Mediterranean shrub capable of thriving in a variety of ecosystems that has invaded every habitable continent on Earth. Our research presents a timeline and estimated rate of expansion from 1816 to 2016. We then model its expected range over the next 70 years, highlighting the need for investigation into its expansion mechanisms and the establishment of monitoring programs.

Standardizing Ecosystem Morphological Traits from 3D Information Sources.

3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported.

Tracking changes in soil organic carbon across the heterogeneous agricultural landscape of the Lower Fraser Valley of British Columbia.

Increasing soil organic carbon (SOC) can improve the capacity of agricultural systems to both adapt to and mitigate climate change. Despite its importance, the current understanding of the magnitude or even the direction of SOC change in agricultural landscapes is limited. While changes in land use/land cover (LULC) and climate are among the main drivers of changes in SOC, their relative importance for the spatiotemporal assessment of SOC is unclear. This study evaluated LULC and SOC dynamics using archived and recent soil samples, remote sensing, and digital soil mapping in the Lower Fraser Valley of British Columbia, Canada. We combined both pixel- and object-based analysis of Landsat satellite imagery to assess LULC changes from 1984 to 2018. We achieved an overall accuracy of 81% and kappa coefficient of 0.77 for LULC classification using a random forest model. For predicting SOC for the same time period, we applied soil and vegetation indices derived from Landsat images, topographic indices, historic soil survey variables, and climate data in a random forest model. The SOC prediction of 2018 resulted in a coefficient of determination (R2) of 0.67, concordance correlation coefficient (CCC) of 0.76, and normalized root mean square error (nRMSE) of 0.12. For 1984, the SOC prediction accuracies were 0.46, 0.58, and 0.18 for R2, CCC, and nRMSE, respectively. We detected SOC loss in 61%, gain in 12%, while 27% remained unchanged across the study area. Although we detected large losses of SOC due to LULC change, the majority of the SOC losses across the landscape were attributed to areas that were remained in the same type of agricultural production since 1984. Climate variability did not, however, have a strong effect on SOC changes. These results can inform decision making in the study area to support sustainable LULC management for enhancing SOC sequestration.

EcoAnthromes of Alberta: An example of disturbance-informed ecological regionalization using remote sensing.

Humans influence ecosystems on magnitudes that often exceed that of natural forces such as climate and geology; however, frameworks rarely include anthropogenic disturbance when delineating unique ecological regions. A critical step toward understanding, managing and monitoring human-altered ecosystems is to incorporate disturbance into ecological regionalizations. Furthermore, quantitative regionalization approaches are desirable to provide cost-effective, repeatable and statistically sound stratification for environmental monitoring. We applied a two-stage multivariate clustering technique to identify 'EcoAnthromes' across a large area - the province of Alberta, Canada - at 30 m spatial resolution, and using primarily remotely sensed inputs. The EcoAnthrome clusters represent regions with unique ecological characteristics based on a combination of natural ecological potential (e.g., climatic and edaphic factors) and disturbance, both natural and anthropogenic. Compared to existing expert-derived Natural Subregions in Alberta, the model-based EcoAnthromes showed greater class separation and explained more variance for an assortment of variables related to land cover, disturbance and species intactness. The EcoAnthromes successfully separated important ecological regions that are defined by complex assemblages of topography, climate and disturbance, such as gravel-bed river valleys, boreal forests, grasslands, post-fire recovery areas and highly disturbed agricultural, industrial and urban landscapes. In addition to presenting a flexible method for EcoAnthrome regionalization, we group and describe the EcoAnthromes created for Alberta and discuss how they can complement expert-derived regionalizations to aid in environmental management efforts, such as species recovery planning and monitoring for threatened species.

Remote sensing of seasonal light use efficiency in temperate bog ecosystems.

Despite storing approximately half of the atmosphere's carbon, estimates of fluxes between wetlands and atmosphere under current and future climates are associated with large uncertainties, and it remains a challenge to determine human impacts on the net greenhouse gas balance of wetlands at the global scale. In this study we demonstrate that the relationship between photochemical reflectance index, derived from high spectral and temporal multi-angular observations, and vegetation light use efficiency was strong (r2 = 0.64 and 0.58 at the hotspot and darkspot, respectively), and can be utilized to estimate carbon fluxes from remote at temperate bog ecosystems. These results improve our understanding of the interactions between vegetation physiology and spectral characteristics to understand seasonal magnitudes and variations in light use efficiency, opening new perspectives on the potential of this technique over extensive areas with different landcover.

Estimating mean monthly incident solar radiation on horizontal and inclined slopes from mean monthly temperatures extremes.

Although satellite-borne sensors are now available to estimate cloud cover and incoming short-wave radiation across the Earth's surface, the study of climatic variation and its impact on terrestrial and marine ecosystems involves historical analyses of data from networks of weather stations that only record extremes in temperatures and precipitation on a daily basis. Similarly, when projections are made with global atmospheric circulation models, the spatial resolution of predicted radiation is too coarse to incorporate the effects of heterogeneous topography. In this paper, we review the development and set forth a set of general equations that allow both diffuse and direct solar radiation to be estimated for each month on the basis of mean daily maximum and minimum temperatures, latitude, elevation, slope, and aspect. Adjustments for differences in slope, aspect, and elevation are made by varying the fraction of diffuse and direct solar beam radiation. To test the equations on various slopes and under different climatic conditions, we drew on highquality radiation data recorded at a number of sites on three continents. On horizontal surfaces the set of equations predicted both direct and diffuse components of solar radiation within 1%-7% of recorded values. On slopes, estimates of monthly mean solar radiation were with 13% of observed values with a mean error of less than 2 MJ m(-2) day(-1) over any given month.