Broad Spatial Extents Research Articles

Supporting early detection of biological invasions through short-term spatial forecasts of detectability

Early detection of invasive species is crucial to prevent biological invasions. To increase the success of detection efforts, it is often essential to know when key phenological stages of invasive species are reached. This includes knowing, for example, when invasive insect species are in their adult phase, invasive plants are flowering or invasive mammals have finished their hibernation. Unfortunately, this kind of information is often unavailable or is provided at very coarse temporal and spatial resolutions. On the other hand, opportunistic records of the location and timing of observations of these stages are increasingly available from biodiversity data repositories. Here, we demonstrate how to apply these data for predicting the timing of phenological stages of invasive species. The predictions are made across Europe, at a daily temporal resolution, including in near real time and for multiple days ahead. We apply this to phenological stages of relevance for the detection of four well-known invasive species: the freshwater jellyfish, the geranium bronze butterfly, the floating primrose-willow and the garden lupine. Our approach uses machine-learning and statistical-based algorithms to identify the set of temporal environmental conditions (e.g. temperature values and trends, precipitation, snow depth and wind speed) associated with the observation of each phenological stage, while accounting for spatial and temporal biases in recording effort. Correlation between predictions from models and the actual timing of observations often exceeded values of 0.9. However, some inter-taxa variation occurred, with models using direct predictors of phenological drivers and trained on thousands of observation records outperforming those relying on indirect predictors and only a few hundred training records. The analysis of daily predictions also allowed mapping European-wide regions with similar phenological dynamics (i.e. ‘phenoregions’). Our results underscore the significant potential of opportunistic biodiversity observation data in developing models capable of predicting and forecasting species phenological stages across broad spatial extents. By enhancing our current ability to anticipate the phenological stages of invasive species, this approach has the potential to significantly improve decision-making in invasion surveillance and monitoring activities.

Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales.

Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD;ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free-ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation with Ophidiomyces ophidiicola. Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural-level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genus Pseudomonas was characteristic of disease-negative microbiomes, whereas, positive snakes were characterized by the pathobionts Chryseobacterium, Paracoccus, and Sphingobacterium. Geographic regions suitable for O. ophidiicola had high pathogen loads (>0.66 maximum sensitivity+specificity). We found that pathogen-induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen.

Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data

Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer’s and user’s accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.

Relevance of surface albedo to forestry policy in high latitude and altitude regions may be overvalued

Abstract Forest restoration and improved forest management are seen as options to enhance terrestrial carbon dioxide removal in many regions, yet concerns surrounding their potentially adverse surface albedo impacts exist, particularly in high latitude and altitude regions. Such concerns are often based on generalized conclusions rooted in analyses carried out over broad spatial extents at coarse resolutions. The impacts of surface albedo change are highly sensitive to local environmental factors governing both the surface albedo and solar radiation budgets, and many previous assessments either do not sufficiently deal with such sensitivities or do not qualify the conditions under which they are relevant. Using the country of Norway with its diverse gradients in topography and climate as an ideal case study region, we seek clarity to the question of whether surface albedo is relevant to consider in forestry planning, and if so, what are the important factors determining it. We find that the adverse impact of a forest’s albedo outweighs its carbon cycle benefit on only ∼4% of Norway’s total forested area, reducing to <∼1% when future climate changes are considered. Our findings challenge the common perception that surface albedo concerns are highly relevant to forestry planning at high latitudes and emphasize the importance of carrying out albedo impact assessments at spatial scales aligning with those of local forestry planning.

Broadscale spatial synchrony in a West Nile virus mosquito vector across multiple timescales

Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of the Culex tarsalis mosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony in Cx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explaining Cx. tarsalis synchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony in Cx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annual Cx. tarsalis spatial synchrony, and we found a marginally significant decrease in annual Cx. tarsalis synchrony. Significant Cx. tarsalis synchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector.

Multifaceted patterns of diversity and co-occurrence along an extensive survey of shrubland communities across China

Interpreting biodiversity patterns and the underlying processes is crucial for evaluating the mechanisms of community assembly, but the view of multifaceted diversity patterns spanning broad spatial extents is less strengthened. We implemented an inventory of 1260 vegetation plots from shrublands across China with standardized methods and analyzed patterns of taxonomic and phylogenetic diversity with differential weighting of common and rare species, as well as phylogenetic co-occurrence structures. Taxonomic and phylogenetic diversity were linearly correlated when common and rare species were weighted equally, but had a logarithmic correlation when species were weighted with their relative abundances. While most shrubland communities were phylogenetically unstructured, the correlation between taxonomic and phylogenetic diversity covaried with phylogenetic relatedness when incorporating relative abundance, but only weakly so in phylogenetically over-dispersed communities. When we correlated patterns of taxonomic and phylogenetic diversity with different weightings for common versus rare species, we found an important role for geographic (e.g., longitude, altitude), climatic (temperature, precipitation) and soil factors. The importance of underlying variables varied between facets of diversity. We found a strong role for altitude in taxonomic, but less so for phylogenetic diversity. Furthermore, the importance of several environmental drivers varied depending on whether diversity metrics were strongly influenced by rare species or put more weight on common and/or dominant species. Overall, our assessment highlights the importance of synthetic analyses of patterns and processes of different facets of biodiversity to capture the full complexity of diversity in conservation studies.

Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.

Divergent ecological responses to typhoon disturbance revealed via landscape-scale acoustic monitoring.

Climate change is increasing the frequency, intensity, and duration of extreme weather events across the globe. Understanding the capacity for ecological communities to withstand and recover from such events is critical. Typhoons are extreme weather events that are expected to broadly homogenize ecological dynamics through structural damage to vegetation and longer-term effects of salinization. Given their unpredictable nature, monitoring ecological responses to typhoons is challenging, particularly for mobile animals such as birds. Here, we report spatially variable ecological responses to typhoons across terrestrial landscapes. Using a high temporal resolution passive acoustic monitoring network across 24 sites on the subtropical island of Okinawa, Japan, we found that typhoons elicit divergent ecological responses among Okinawa's diverse terrestrial habitats, as indicated by increased spatial variability of biological sound production (biophony) and individual species detections. This suggests that soniferous communities are capable of a diversity of different responses to typhoons. That is, spatial insurance effects among local ecological communities provide resilience to typhoons at the landscape scale. Even though site-level typhoon impacts on soundscapes and bird detections were not particularly strong, monitoring at scale with high temporal resolution across a broad spatial extent nevertheless enabled detection of spatial heterogeneity in typhoon responses. Further, species-level responses mirrored those of acoustic indices, underscoring the utility of such indices for revealing insight into fundamental questions concerning disturbance and stability. Our findings demonstrate the significant potential of landscape-scale acoustic sensor networks to capture the understudied ecological impacts of unpredictable extreme weather events.

High uncertainty of evapotranspiration products under extreme climatic conditions

Terrestrial Evapotranspiration (ET) is an important process for understanding regional or global water, energy, and carbon cycles, and with the development of satellite observations and increased research investment, grid ET products covering a broad spatial extent are becoming more readily available. However, as global warming becomes a reality and extreme climate events occur worldwide, existing studies do not go far enough to verify that these grid products can still be used under extreme climate conditions. This study evaluates nine global ET products, including land surface reanalysis products (GLDAS_CLSM, GLDAS_NOAH, ERA5, and FLDAS), remote sensing (RS) products (GLEAM_v3.6b, MOD16A2, and PMLv2), and multi-source data fusion products (REA and Synthesized), using observations from 153 flux towers worldwide. The objective is to evaluate their performance in estimating ET under extreme climatic conditions (high temperature, high vapor pressure deficit (VPD), and drought). The results indicate that the estimation accuracy of all ET products is significantly reduced under extreme climatic conditions, showing high uncertainty, and this impact is most severe in the Americas (AM) region. Overall, multi-source data fusion products showed the best estimation performance and were less affected by extreme climatic conditions. Among the remote sensing modeling products, GLEAM_v3.6b showed the best performance, while MOD16A2 has the lowest estimation accuracy. Land surface reanalysis products were most affected by extreme conditions, with CLSM and NOAH showing similar performance and ERA5 having the largest errors (RMSE_ERA5 = 1.699 mm/d, MAE_ERA5 = 1.294 mm/d). The ET products show significant error fluctuations and overestimation (PBias > 0.5) in most of North America, and there is a decline in simulation accuracy in arid and semi-arid regions near 30°N, with most ET products showing overestimation. The most significant errors were observed in cropland areas (CRO) and deciduous broadleaf forest areas (DBF), with significant overestimation in mixed forest areas (MF). The results of this study provide valuable insights for researchers in selecting ET products under extreme climatic conditions and encourage product developers to consider uncertainty under such conditions, thereby improving product accuracy.

Diversity-stability relationships across organism groups and ecosystem types become decoupled across spatial scales.

The relationship between biodiversity and stability, or its inverse, temporal variability, is multidimensional and complex. Temporal variability in aggregate properties, like total biomass or abundance, is typically lower in communities with higher species diversity (i.e., the diversity-stability relationship [DSR]). At broader spatial extents, regional-scale aggregate variability is also lower with higher regional diversity (in plant systems) and with lower spatial synchrony. However, focusing exclusively on aggregate properties of communities may overlook potentially destabilizing compositional shifts. It is not yet clear how diversity is related to different components of variability across spatial scales, nor whether regional DSRs emerge across a broad range of organisms and ecosystem types. To test these questions, we compiled a large collection of long-term metacommunity data spanning a wide range of taxonomic groups (e.g., birds, fish, plants, invertebrates) and ecosystem types (e.g., deserts, forests, oceans). We applied a newly developed quantitative framework for jointly analyzing aggregate and compositional variability across scales. We quantified DSRs for composition and aggregate variability in local communities and metacommunities. At the local scale, more diverse communities were less variable, but this effect was stronger for aggregate than compositional properties. We found no stabilizing effect of γ-diversity on metacommunity variability, but β-diversity played a strong role in reducing compositional spatial synchrony, which reduced regional variability. Spatial synchrony differed among taxa, suggesting differences in stabilization by spatial processes. However, metacommunity variability was more strongly driven by local variability than by spatial synchrony. Across a broader range of taxa, our results suggest that high γ-diversity does not consistently stabilize aggregate properties at regional scales without sufficient spatial β-diversity to reduce spatial synchrony.

Model-assisted domain estimation of postfire tree regeneration in the western US using nearest neighbor techniques

Many nations administer national forest inventory programs for unbiased estimation of forest attributes over broad spatial and temporal extents. However, management and conservation decisions often demand reliable estimates for finer spatiotemporal domains. In the western US, wildfire activity is expanding and postfire regeneration must contend with a warmer, drier climate. We evaluate the potential of K nearest neighbor (KNN) strategies for estimation of stocking across postfire measurements of Forest Inventory & Analysis plots in 11 western US states, and subsequently for model-assisted (MA) estimation of stocking over domains defined by aggregations of burned areas within individual states and 4-year periods. In particular, we develop and evaluate a form of constrained KNN that allows for unbiased MA domain estimation under simple random sampling by drawing only on measurements external to a domain of interest. KNN strategies based on geographically, radiometrically, and climatically proximate measurements are found to provide more accurate estimates of stocking at the plot level than domain means. Applying the selected external KNN strategy also reduced standard errors of MA domain estimates by 16% over direct domain estimators, but bias correction introduces substantial variability over synthetic estimates. Further applications of the external constraint imposed on KNN are discussed.

Geographic distribution of C4 species and its phylogenetic structure across China

Over the past fifty years, the distribution patterns of C 4 species, across large spatial scales, are largely ignored. Here, we endeavored to examine patterns in the taxonomic and phylogenetic diversity of species with C 4 photosynthetic pathways across the broad spatial extent of China and relate those to climatic gradients. We built a database of all plants with the C 4 photosynthetic pathway in China. We analyzed the geographic distributions, taxonomic diversity, phylogenetic diversity, and phylogenetic structure of all C 4 species, as well as the three families with the most C 4 species (Poaceae, Amaranthaceae and Cyperaceae), and compared their values along temperature and precipitation gradients at two scales—the level of the province and at the 100 x 100 km grid cell. We found 644 C 4 plants (belonging to 23 families 165 genera) in China, with Poaceae (57%), Amaranthaceae (17%), Cyperaceae (13%) accounting for the majority of species. Standardized effect size values of phylogenetic distances were negative overall, indicating that C 4 species showed a phylogenetic clustering pattern. Southern China had the highest species richness and the highest degree of phylogenetic clustering. C 4 tended to be more phylogenetically over-dispersed in regions with colder and/or drier climates, but more clustered in warmer and/or wetter climates. Patterns within individual families were more nuanced. The distribution of C 4 species and its phylogenetic structure across China was constrained by temperature and precipitation. C 4 species showed a phylogenetic clustering pattern across China, while different families showed more nuanced responses to climate variation, suggesting a role for evolutionary history.

Using aerial LiDAR to assess regional availability of potential habitat for a conservation dependent forest bird

Remotely-sensed data can inform conservation efforts that target forest wildlife, however, few spatial data products are able to quantify fine-scale aspects of structural variation within forests. Increased availability of Light Detection and Ranging (LiDAR) datasets that cover broad spatial extents and ownership types (e.g., entire states) provide useful information regarding canopy and understory structure within forested landscapes. The fusion of LiDAR data with field-based species surveys can advance our understanding of species-habitat relationships and improve the effectiveness of conservation programs to recover habitat-limited species. The Golden-winged Warbler (Vermivora chrysoptera) is a forest-dependent songbird that nests in structurally-complex young forest across eastern North America. As with many early-successional obligates, this species has been declining for decades due, in part, to the steady loss of young forest/shrubland nesting habitat. Although conservation programs have begun restoring Golden-winged Warbler habitat, these efforts are currently limited by the inability to identify existing habitat across large spatial extents and diverse ownership patterns. Recent availability of state-wide LiDAR data for Pennsylvania provides an opportunity to overcome this limitation. From 2019 to 20, we surveyed for Golden-winged Warblers and structural vegetation at 837 sites across six forest blocks in eastern Pennsylvania. We combined these data with LiDAR derived forest structural metrics to develop statistical models to predict patterns of occupancy. Golden-winged Warbler occupancy probability was explained by models containing several LiDAR-derived structural metrics (e.g., percentage of first returns between 1 and 5 m in height, structural complexity, etc.). Moreover, models fit with LiDAR-derived covariates predicted occupancy much better than those using only field-measured vegetation covariates (ΔAICc = 53.27). Mapped predictions of Golden-winged Warbler occupancy revealed potential habitat (especially regenerating timber harvests) on both private and public lands. These results demonstrate the efficacy of LiDAR for modeling forest bird habitat associations, and how such data sources can provide a valuable tool for conservation planning.

ForestClim-Bioclimatic variables for microclimate temperatures of European forests.

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.

Minimizing Data Waste: Conservation in the Big Data Era

Minimizing Data Waste: Conservation in the Big Data Era

Functional biogeography of fluvial fishes across the conterminous U.S.A.: Assessing the generalisability of trait–environment relationships over large regions

Abstract Compared to taxonomic approaches, traits‐based approaches are often implemented to gain a more mechanistic understanding of how biotic communities respond to changing environmental conditions, including landscape stressors. Studies evaluating trait–environment relationships have increased in prevalence over the last 2 decades, but such analyses have rarely been conducted across continental extents in which component regions exhibit substantial spatial variation in stressors and natural environmental conditions. Consequently, the role of biogeographic context in analyses of trait–environment relationships remains under studied. Here, we evaluated the generalisability of trait–environment relationships across the conterminous U.S.A. to advance our understanding of the functional biogeography of fluvial fishes. We sought to: (1) identify trait–environment relationships that were consistent regardless of regional differences in natural environmental conditions; and (2) address whether the multivariate structure of trait–environment relationships was comparable across regions. We used RLQ and fourth‐corner analyses to identify significant trait–environment relationships across nine large ecoregions comprising the conterminous U.S.A. These statistical approaches provided a framework to analyse co‐variation among 16 functional traits (e.g., trophic ecology, habitat preference) and 17 environmental variables representing 597 stream fish species whose abundances had been recorded in 45,183 stream reaches between 1990 and 2019. Trait–environment relationships varied among ecoregions such that few trait–environment relationships were consistent. Although some trait–environment relationships were significant in multiple ecoregions, the strength and multivariate structure of such relationships varied. These findings suggest that the functional traits most responsive to changing environmental conditions are determined by distinct biogeographic processes. Our findings add to a growing consensus that functional traits‐based approaches are context dependent. Understanding the complex relationships between functional traits and environmental conditions across large geographic regions can inform ecological theory and improve conservation efforts at broad spatial extents.

Riverine exports of mercury and methylmercury from dammed and undammed rivers of Quebec, Eastern Canada

Coastal rivers represent an important flux of mercury (Hg) to marine systems, but large uncertainties remain due to a lack of field data in key regions of the northern hemisphere. Given the increasing number of dams built on rivers globally and their influence on the Hg cycle, damming may affect broad-scale Hg budgets, but empirical studies on the contribution of dammed vs undammed rivers on fluxes at broad spatial extents remain scarce. Here we have compiled measurements of the concentrations, fluxes and yields of Hg, methylmercury (MeHg), dissolved organic carbon (DOC) concentration and quality at the mouth of 9 dammed and 21 undammed coastal rivers across Quebec, Eastern Canada. Total and methyl Hg concentrations were typically lower for dammed vs. undammed rivers (mean Hg of 1.40 vs. 2.79 ng L−1; mean MeHg of 0.10 vs. 0.13 ng L−1). The yields displayed the same pattern (mean Hg of 0.82 vs. 1.50 g km−2 year−1; mean MeHg of 0.05 vs. 0.07 g km−2 year−1) although dammed river fluxes were typically higher (mean Hg of 25.66 vs. 15.02 kg year−1; mean MeHg of 1.25 vs. 0.48 kg year−1) because of higher discharge. DOC fluxes were good predictors of Hg fluxes and could be used as proxies. We extrapolated these fluxes to the entire province of Quebec and provide a first order estimate of annual Hg flux of 454 ± 72 kg from dammed and of 1879 ± 240 kg from undammed rivers, compared to 33 ± 5 kg and 89 ± 11 kg of MeHg, respectively. These findings are in the lower range of current estimates for other northern regions and suggest that dammed rivers export mercury in amounts that are roughly proportional to river discharge.

Globus automation services: Research process automation across the space–time continuum

Research process automation–the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources–has emerged as an essential element of modern science. We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, flows, and the execution of such flows in heterogeneous research environments. To support flows with broad spatial extent (e.g., from scientific instrument to remote data center) and temporal extent (from seconds to weeks), these Globus automation services feature: (1) cloud hosting for reliable execution of even long-lived flows despite sporadic failures; (2) a simple specification and extensible asynchronous action provider API, for defining and executing a wide variety of actions and flows involving heterogeneous resources; (3) an event-driven execution model for automating execution of flows in response to arbitrary events; and (4) a rich security model enabling authorization delegation mechanisms for secure execution of long-running actions across distributed resources. These services permit researchers to outsource and automate the management of a broad range of research tasks to a reliable, scalable, and secure cloud platform. We present use cases for Globus automation services, describe their design and implementation, present microbenchmark studies, and review experiences applying the services in a range of applications.

Quantifying Lidar Elevation Accuracy: Parameterization and Wavelength Selection for Optimal Ground Classifications Based on Time since Fire/Disturbance

Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification parameterizations for optimal accuracy using TerraScan and LAStools with airborne lidar data collected in three wavelengths: 532 nm, 1064 nm, and 1550 nm in low relief boreal peatland environments. While the focus of the study is on elevation accuracy and losses from fire, the research is also highly pertinent to hydrological modelling, forestry, geomorphological change, etc. The study area includes burned and unburned boreal peatlands south of Fort McMurray, Alberta. Lidar and field validation data were collected in July 2018, following the 2016 Horse River Wildfire. An iterative ground classification analysis was conducted whereby validation points were compared with lidar ground-classified data in five environments: road, unburned, burned with shorter vegetative regeneration (SR), burned with taller vegetative regeneration (TR), and cumulative burned (both SR and TR areas) in each of the three laser emission wavelengths individually, as well as combinations of 1550 nm and 1064 nm and 1550 nm, 1064 nm, and 532 nm. We find an optimal average elevational offset of ~0.00 m in SR areas with a range (RMSE) of ~0.09 m using 532 nm data. Average accuracy remains the same in cumulative burned and TR areas, but RMSE increased to ~0.13 m and ~0.16 m, respectively, using 1550 nm and 1064 nm combined data. Finally, data averages ~0.01 m above the field-measured ground surface in unburned boreal peatland and transition areas (RMSE of ~0.19 m) using all wavelengths combined. We conclude that the ‘best’ offset for depth of burn within boreal peatlands is expected to be ~0.01 m, with single point measurement uncertainties upwards of ~0.25 m (RMSE) in areas of tall, dense vegetation regeneration. The importance of classification parameterization identified in this study also highlights the need for more intelligent adaptative classification routines, which can be used in other environments.

Regional models do not outperform continental models for invasive species

Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents. Location: Continental United States. Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions. Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response. Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.