Explore the vast range of titles available.

Connectivity models are useful tools that improve the ability of researchers and managers to plan land use for conservation and preservation. Most connectivity models function in a point-to-point or patch-to-patch fashion, limiting their use for assessing connectivity over very large areas. In large or highly fragmented systems, there may be so many habitat patches of interest that assessing connectivity among all possible combinations is prohibitive. To overcome these conceptual and practical limitations, we hypothesized that minor adaptation of the Circuitscape model can allow the creation of omnidirectional connectivity maps illustrating flow paths and variations in the ease of travel across a large study area. We tested this hypothesis in a 24,300 km(2) study area centered on the Montérégie region near Montréal, Québec. We executed the circuit model in overlapping tiles covering the study region. Current was passed across the surface of each tile in orthogonal directions, and then the tiles were reassembled to create directional and omnidirectional maps of connectivity. The resulting mosaics provide a continuous view of connectivity in the entire study area at the full original resolution. We quantified differences between mosaics created using different tile and buffer sizes and developed a measure of the prominence of seams in mosaics formed with this approach. The mosaics clearly show variations in current flow driven by subtle aspects of landscape composition and configuration. Shown prominently in mosaics are pinch points, narrow corridors where organisms appear to be required to traverse when moving through the landscape. Using modest computational resources, these continuous, fine-scale maps of nearly unlimited size allow the identification of movement paths and barriers that affect connectivity. This effort develops a powerful new application of circuit models by pinpointing areas of importance for conservation, broadening the potential for addressing intriguing questions about resource use, animal distribution, and movement.

Coloured dissolved organic matter (CDOM) is an important water property for lake management. Remote sensing using empirical algorithms has been used to estimate CDOM, with previous studies relying on coordinated field campaigns that coincided with satellite overpass. However, this requirement reduces the maximum possible sample size for model calibration. New satellites and advances in cloud computing platforms offer opportunities to revisit assumptions about methods used for empirical algorithm calibration. Here, we explore the opportunities and limits of using median values of Landsat 8 satellite images across southern Canada to estimate CDOM. We compare models created using an expansive view of satellite image availability with those emphasizing a tight timing between the date of field sampling and the date of satellite overpass. Models trained on median band values from across multiple summer seasons performed better (adjusted R2 = 0.70, N = 233) than models for which imagery was constrained to a 30-day time window (adjusted R2 = 0.45). Model fit improved rapidly when incorporating more images, producing a model at a national scale that performed comparably to others found in more limited spatial extents. This research indicated that dense satellite imagery holds new promise for understanding relationships between in situ CDOM and satellite reflectance data across large areas.

Aim: We tested the energy and metabolic theories for explaining diversity patterns of crustacean Zooplankton in Canadian lakes, and evaluated the influence of regional and local environments on community structure. Location: The 1665 studied lakes are distributed across Canada in 47 ecoprovinces. Methods: Our database included the occurrence of 83 pelagic crustacean species. The regional species richness in each ecoprovince was estimated using the average local species richness per lake and the first-order jackknife diversity index. Using a principal component plot and forward selection in a multiple regression we identified the most important predictors of regional species richness estimates. We tested the predictions of the species richness-energy hypothesis using climatic variables at regional scale, and of the metabolic theory using the inverse of air temperature. To evaluate the influence of regional and local environmental drivers, we carried out a redundancy analysis between crustacean species occurrences and regional climate and lake environmental factors on a subset of 458 lakes. Results: Estimates of pelagic crustacean species richness in Canadian ecoprovinces varied from 3 to 10 species per lake (average local species richness) or 8 to 52 species per ecoprovince (Jackknife diversity index). Our study fully supports the species richness-energy hypothesis and partially the metabolic theory. Mean daily global solar radiation was the most important regional predictor, explaining 51% of the variation in the regional species richness among ecoprovinces. Together, regional climate and local lake environment accounted for 31% of the total variation in community structure. Regional-scale energy variables accounted for 24% of the total explained variation, whereas local-scale lake conditions had less influence (2%). Main conclusions: The richness-energy theory explains diversity patterns of freshwater crustacean zooplankton in Canadian ecoprovinces. Solar radiation is the best predictor explaining regional species richness in ecoprovinces and community structure of pelagic crustaceans in Canadian lakes.

Remote sensing is undergoing a fundamental paradigm shift, in which approaches interpreting one or two images are giving way to a wide array of data-rich applications. These include assessing global forest loss, tracking water resources across Earth’s surface, determining disturbance frequency across decades, and many more. These advances have been greatly facilitated by Google Earth Engine, which provides both image access and a platform for advanced analysis techniques. Within the realm of land-use/land-cover (LULC) classifications, Earth Engine provides the ability to create new classifications and to access major existing data sets that have already been created, particularly at global extents. By overlaying global LULC classifications—the 300-m GlobCover 2009 LULC data set for example—with sharper images like those from Landsat, one can see the promise and limits of these global data sets and platforms to fuse them. Despite the promise in a global classification covering all of the terrestrial surface, GlobCover 2009 may be too coarse for some applications. We asked whether the LULC labeling provided by GlobCover 2009 could be combined with the spatial granularity of the Landsat platform to produce a hybrid classification having the best features of both resources with high accuracy. Here we apply an improvement of the Bayesian Updating of Land Cover (BULC) algorithm that fused unsupervised Landsat classifications to GlobCover 2009, sharpening the result from a 300-m to a 30-m classification. Working with four clear categories in Mato Grosso, Brazil, we refined the resolution of the LULC classification by an order of magnitude while improving the overall accuracy from 69.1 to 97.5%. This “BULC-U” mode, because it uses unsupervised classifications as inputs, demands less region-specific knowledge from analysts and may be significantly easier for non-specialists to use. This technique can provide new information to land managers and others interested in highly accurate classifications at finer scales.

Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, new opportunities are emerging to revisit traditional assumptions concerning empirical calibration methodologies. Using Landsat 8 images with large water clarity datasets from southern Canada, we assess: (1) whether clear regional differences in water clarity algorithm coefficients exist and (2) whether model fit can be improved by expanding temporal matching windows. We found that a single global algorithm effectively represents the empirical relationship between in situ Secchi disk depth (SDD) and the Landsat 8 Blue/Red band ratio across diverse lake types in Canada. We also found that the model fit improved significantly when applying a median filter on data from ever-wider time windows between the date of in situ SDD sample and the date of satellite overpass. The median filter effectively removed the outliers that were likely caused by atmospheric artifacts in the available imagery. Our findings open new discussions on the ability of large datasets and temporal averaging methods to better elucidate the true relationships between in situ water clarity and satellite reflectance data.

Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques that produce datasets complementary to national cropland surveys. In this study, we present a flexible Bayesian data synthesis workflow on Google Earth Engine (GEE) that can be used to fuse optical and synthetic aperture radar data and demonstrate its ability to track agricultural change at national scales. We adapted the previously developed Bayesian Updating of Land Cover (Unsupervised) algorithm (BULC-U) by integrating a shapelet and slope thresholding algorithm to identify the locations and dates of cropland expansion and implemented a tiling scheme to allow the processing of large volumes of imagery. We apply this approach to map annual cropland change from 2000 to 2015 for Zambia (750,000 km2), a country that is experiencing rapid growth in agricultural land. We applied our cropland mapping approach to a time series of unsupervised classifications developed from Landsat 5, 7, 8, Sentinel-1, and ALOS PALSAR within 1476 tiles covering Zambia. The annual cropland changes maps reveal active cropland expansion between 2000 to 2015 in Zambia, especially in the Southern, Central, and Eastern provinces. Our accuracy assessment estimates that we have identified 27.5% to 69.6% of the total cropland expansion from 2000 to 2015 in Zambia (commission errors between 6.1% to 37.6%), depending on the slope threshold. Our results demonstrate the usefulness of Bayesian data fusion and shapelet, slope-based thresholding to synthesize optical and synthetic aperture radar for monitoring agricultural changes in situations where training data are scarce. In addition, the annual cropland maps provide one of the first spatially continuous, annually incremented accounts of cropland growth in this region. Our flexible, cloud-based workflow using GEE enables multi-sensor, national-scale agricultural change monitoring at low cost for users.

In the original publication [...]

Fire refugia are unburned and low severity patches within wildfires that contribute heterogeneity that is important to retaining biodiversity and regenerating forest following fire. With increasingly intense and frequent wildfires in the Pacific Northwest, fire refugia are important for re-establishing populations sensitive to fire and maintaining resilience to future disturbances. Mapping fire refugia and delayed canopy loss is useful for understanding patterns in their distribution. The increasing abundance of satellite data and advanced analysis platforms offer the potential to map fire refugia in high detail. This study uses the Bayesian Updating of Land Cover (BULC-D) algorithm to map fire refugia and delayed canopy loss three years after fire. The algorithm compiles Normalized Burn Ratio data from Sentinel-2 and Landsat 8 and 9 and uses Bayes’ Theorem to map land cover changes. Four wildfires that occurred across Washington State in 2020 were mapped. Additionally, to consider the longevity of ‘durable’ fire refugia, the fire perimeters were analyzed to map delayed canopy loss in the years 2021–2023. The results showed that large losses in fire refugia can occur in the 1–3 years after fire due to delayed effects, but with some patches enduring.

Ecological processes are increasingly well understood over smaller areas, yet information regarding interconnections and the hierarchical nature of ecosystems remains less studied and understood. Information on connectivity over large areas with high resolution source information provides for both local detail and regional context. The emerging capacity to apply circuit theory to create maps of omnidirectional connectivity provides an opportunity for improved and quantitative depictions of forest connectivity, supporting the formation and testing of hypotheses about the density of animal movement, ecosystem structure, and related links to natural and anthropogenic forces. In this research, our goal was to delineate regions where connectivity regimes are similar across the boreal region of Canada using new quantitative analyses for characterizing connectivity over large areas (e.g., millions of hectares). Utilizing the Earth Observation for Sustainable Development of forests (EOSD) circa 2000 Landsat-derived land-cover map, we created and analyzed a national-scale map of omnidirectional forest connectivity at 25m resolution over 10000 tiles of 625 km2 each, spanning the forested regions of Canada. Using image recognition software to detect corridors, pinch points, and barriers to movements at multiple spatial scales in each tile, we developed a simple measure of the structural complexity of connectivity patterns in omnidirectional connectivity maps. We then mapped the Circuitscape resistance distance measure and used it in conjunction with the complexity data to study connectivity characteristics in each forested ecozone. Ecozone boundaries masked substantial systematic patterns in connectivity characteristics that are uncovered using a new classification of connectivity patterns that revealed six clear groups of forest connectivity patterns found in Canada. The resulting maps allow exploration of omnidirectional forest connectivity patterns at full resolution while permitting quantitative analyses of connectivity over broad areas, informing modeling, planning and monitoring efforts.

Boreal forests maintain regionally important biodiversity and globally important ecosystem services, such as carbon storage and freshwater resources. Many boreal systems have limited anthropogenic disturbances and are preserved, in effect, to date largely by their harsh climates and remoteness. As of 2011, almost 10% of Canada is subject to some manner of formal protection, with 4.5% of this protected area found within the boreal zone. The management of existing parks and protected areas (PPAs) is shared amongst many federal, provincial, and territorial jurisdictions. Although there are currently low levels of anthropogenic development in some portions of the boreal zone (especially the north), if expansion of protected areas is of interest, there are challenges to traditional PPA networks that may be more prominent in the boreal zone than elsewhere: (1) the boreal zone is home to charismatic mammal species with area requirements much larger than typical PPAs; (2) the boreal zone is characterized by natural disturbance regimes that impact large areas; and (3) projected changes to climate for the boreal zone are among the greatest in the world, creating temporal considerations for conservation planning exercises. There is currently no PPA assessment specific to boreal Canada. To address this lack of an assessment, we developed a conservation gap analysis of the current PPA system with respect to a variety of environmental surrogates (ecozones, land cover, vegetation productivity, and landscape structure). The amount of formally protected land varied within each surrogate, with few commonly reported features meeting national or international conservation targets. Furthermore, few reserves met the areal requirements that have been previously recommended to protect large mammals or accommodate the disturbance regimes present. We also discuss considerations and implications of area-based versus value-based protection objectives. While recognizing that there are still scientific challenges around understanding and evaluating the effectiveness of PPAs, based upon our review and assessment, the following considerations should inform conservation options for the boreal zone: (1) representation of the distribution of natural features within the PPA network; (2) effective maintenance of habitat requirements and spatial resilience to both cyclical and directional changes in spatial patterns through large, connected reserves; and (3) implementation of sustainable forest management practices (where applicable) throughout the broader landscape, as traditional on-reserve protection is unlikely to be sufficient to meet conservation goals. The Canadian boreal is unique in possessing large tracts of inaccessible forested lands that are not subject to management interventions, thereby offering functions similar to protected lands. The question of how to more formally integrate these lands into the existing PPA network requires further consideration. Further, the important temporal role of landscape dynamics in designing an effective PPA needs to be further studied as well as development of a better understanding of design needs in the context of a changing climate.