PREDICTING THE INFRARED UAV IMAGERY OVER THE COAST

The infrared (IR) imagery provides additional information to the visible (red-green-blue, RGB) about vegetation, soil, water, mineral, or temperature, and has become essential for various disciplines, such as geology, hydrology, ecology, archeology, meteorology or geography. The integration of the IR sensors, ranging from near-IR (NIR) to thermal-IR through mid-IR, constitutes a baseline for Earth Observation satellites but not for unmanned airborne vehicles (UAV). Given the hyperspatial and hypertemporal characteristics associated with the UAV survey, it is relevant to benefit from the IR waveband in addition to the visible imagery for mapping purposes. This paper proposes to predict the NIR reflectance from RGB digital number predictors collected with a consumer-grade UAV over a structurally and compositionally complex coastal area. An array of 15 000 data, distributed into calibration, validation and test datasets across 15 representative coastal habitats, was used to build and compare the performance of the standard least squares, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models. The NN family surpassed the four other ones, and the best NN model (R2 = 0.67) integrated two hidden layers provided, each, with five nodes of hyperbolic tangent and five nodes of Gaussian activation functions. This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints. At the habitat scale, sedimentary and dry vegetation environments were satisfactorily predicted (R2 > 0.6), contrary to the healthy vegetation (R2 < 0.2). Those innovative findings will be useful for scientists and managers tasked with hyperspatial and hypertemporal mapping.

Hydromorphologic Control of Streambed Fine Particle Standing Stocks Influences In-stream Aerobic Respiration

Fine particulate organic matter (FPOM) accumulated in streambeds is a major component of organic matter budgets in headwater streams and greatly affects productivity and metabolism of stream communities. The spatiotemporal distribution of benthic FPOM in the stream, as well as its quantity and quality, depend on inputs from different source types. These can be natural such as soils, streambanks and riparian vegetation, or anthropogenic such as effluents from wastewater treatment plants (WWTP). In addition, stream flow is a key driver of FPOM dynamics, which influences the balance between its transport and accumulation in the streambed. Yet, the link between FPOM dynamics and its effects on stream metabolism is still largely unknown. The aim of this study was to investigate the influence of stream channel hydromorphology on water transport and streambed accumulation of fine particulate matter (FPM) (mineral and organic fractions), FPOM (organic fraction) and its quality (characterized by %OM, %C, %N and the C:N molar ratio). In addition, we quantified the metabolic activity associated with FPM at the habitat scale, and its potential contribution to whole-reach ecosystem respiration using the resazurin-resorufin bioreactive tracer as a proxy for aerobic respiration. We also characterized water transport and metabolic activity with combined additions of hydrological and bioreactive tracers at the reach scale. The study was conducted in the Cànoves stream (Catalonia, NE Spain) downstream of a WWTP that contains three reaches that were hydromorphologically modified using bioengineering techniques. Slower local velocities at the habitat scale increased accumulation of FPM, but did not influence the spatial variability of its quality. Instead, FPM quality declined further downstream from the WWTP. Accumulation of FPM did not increase metabolic activity, but higher %OM of FPM and lower C:N ratios favored the microbial metabolic activity efficiency (normalized by the gram of FPM). Reach-scale metabolic activity was higher in reaches with higher water exchange rate and longer relative travel times, highlighting hydromorphology as an important driver of microbial metabolic activity at the reach-scale. This demonstrates that the interplay of hydrologic exchange and residence time in streambed sediments associated with the microbial metabolic activity of FPOM can ultimately influence reach-scale metabolic activity.

Disturbance Effects on Spatial Autocorrelation in Biodiversity: An Overview and a Call for Study

The spatially autocorrelated patterns of biodiversity can be an important determinant of ecological processes, functions and delivery of services across spatial scales. Therefore, understanding disturbance effects on spatial autocorrelation in biodiversity is crucial for conservation and restoration planning but remains unclear. In a survey of disturbance versus spatial patterns of biodiversity literature from forests, grasslands and savannah ecosystems, we found that habitat disturbances generally reduce the spatial autocorrelation in species diversity on average by 15.5% and reduce its range (the distance up to which autocorrelation prevails) by 21.4%, in part, due to disturbance-driven changes in environmental conditions, dispersal, species interactions, or a combination of these processes. The observed effect of disturbance, however, varied markedly among the scale of disturbance (patch-scale versus habitat-scale). Surprisingly, few studies have examined disturbance effects on the spatial patterns of functional diversity, and the overall effect was non-significant. Despite major knowledge gaps in certain areas, our analysis offers a much-needed initial insights into the disturbance-driven changes in the spatial patterns of biodiversity, thereby setting the ground for informed discussion on conservation and promotion of spatial heterogeneity in managing natural systems under a changing world.

Technical note: Estimating light-use efficiency of benthic habitats using underwater O₂ eddy covariance

Light-use efficiency defines the ability of primary producers to convert sunlight energy to primary production and is computed as the ratio between the gross primary production and the intercepted photosynthetic active radiation. While this measure has been applied broadly within terrestrial ecology to investigate habitat resource-use efficiency, it remains underused within the aquatic realm. This report provides a conceptual framework to compute hourly and daily light-use efficiency using underwater O2 eddy covariance, a recent technological development that produces habitat-scale rates of primary production under unaltered in situ conditions. The analysis, tested on two benthic flux datasets, documents that hourly light-use efficiency may approach the theoretical limit of 0.125 O2 per photon under low-light conditions, but it decreases rapidly towards the middle of the day and is typically 10-fold lower on a 24 h basis. Overall, light-use efficiency provides a useful measure of habitat functioning and facilitates site comparison in time and space.

Recent Growth and Expansion of Birch Shrubs Across a Low Arctic Landscape in Continental Canada: Are These Responses More a Consequence of the Severely Declining Caribou Herd than of Climate Warming?

The recent widespread expansion of deciduous shrubs across much of the Arctic has been largely attributed to climate warming. This study investigated decadal growth rates of dwarf birch (Betula glandulosa) across a low Arctic landscape in the continental interior of Canada. Detailed birch cover (100 m2 replicate plots) and individual shrub stature measurement datasets for five representative habitat-types were compared between 2006 and 2016, and evaluated in relation to environmental characteristics. Furthermore, dendrochronologically-based annual growth rates were assessed in relation to the 20-year climate record. Birch height, lateral dimensions, and patch groundcover all increased 20–25% relative to 2006 values, but these increases were similar among the habitat-types. Together, the limited evidence of recent warming at this site, the absence of significant habitat-type growth rate differences, and the lack of correlation between annual climate and stem secondary growth strongly suggest that climate change was not the principal cause. Instead, we propose that release from caribou impacts following the recent severe herd decline may explain the net shrub growth. Individual shrub growth rates were correlated with soil nutrient availability, but the latter was highly variable, suggesting that growth rates are primarily determined by fine-scale rather than habitat-scale spatial heterogeneity in nutrient supply. Together, our results demonstrate that birch growth has been enhanced across a variety of habitat-types in the Daring Lake landscape over the decade since 2006, and suggest that the recent severe caribou herd declines may be at least as significant as climate warming in driving birch shrub expansion in the Canadian central low Arctic.

Spatiotemporal co-occurrence and overlap of two sympatric mongoose species in an urban environment

Small carnivores are becoming increasingly common in urban areas. What has received less attention is whether and how resource partitioning among sympatric species in urban areas facilitates their coexistence. We examined the spatial, temporal and combined spatiotemporal occurrence and overlap of co-existing yellow mongoose Cynictis penicillata and slender mongoose Galerella sanguinea in an urban estate in South Africa. The reserve comprised two parts, an Eco-Estate where human residential and natural areas are interspersed and wildlife has greater contact with people, and a Nature Estate, where contact is reduced by palisade fencing between people and natural areas. Using photographic data from camera traps collected over 11 consecutive months, we found a moderate level of spatial overlap between the mongoose species. Differences between the species occurred at a finer habitat scale: yellow mongooses were more common in open habitats located near human residents whereas the slender mongooses were more common in covered areas further away from human residents. The detection probability of the yellow mongoose, however, was greater than that of the slender mongoose, and the occupancy probability of the slender mongoose was reduced in the presence of the yellow mongoose. Although both species demonstrated bimodal diurnal peaks in activity, they varied in their active periods, with temporal overlap being greater during colder than warmer months. No complete spatiotemporal overlap (occurrence in the same place at the same time/within a 10-min period) occurred. This may have been as a result of the difference in detection and occupancy probabilities of the two species. Resource availability (food), however, appears to influence the different habitat selection, space use, and activity patterns of yellow and slender mongoose in the study area. Therefore, we conclude that partitioning along the spatial and somewhat on the temporal dimensions aids in the coexistence of these mongoose species in an urban environment.

Temperature Variability Differs in Urban Agroecosystems across Two Metropolitan Regions

Climatically similar regions may experience different temperature extremes and weather patterns that warrant global comparisons of local microclimates. Urban agroecosystems are interesting sites to examine the multidimensional impacts of climate changes because they rely heavily on human intervention to maintain crop production under different and changing climate conditions. Here, we used urban community gardens across the California Central Coast metropolitan region, USA, and the Melbourne metropolitan region, Australia, to investigate how habitat-scale temperatures differ across climatically similar regions, and how people may be adapting their gardening behaviors to not only regional temperatures, but also to the local weather patterns around them. We show that, while annual means are very similar, there are strong interregional differences in temperature variability likely due to differences in the scale and scope of the temperature measurements, and regional topography. However, the plants growing within these systems are largely the same. The similarities may be due to gardeners’ capacities to adapt their gardening behaviors to reduce the adverse effects of local temperature variability on the productivity of their plot. Thus, gardens can serve as sites where people build their knowledge of local weather patterns and adaptive capacity to climate change and urban heat. Climate-focused studies in urban landscapes should consider how habitat-scale temperature variability is a background for interesting and meaningful social-ecological interactions.

Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments

Very high-resolution satellite imagery (≤5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution (<5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field for WorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff being WorldView’s fine spatial resolution versus RapidEye’s temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.