Craig MacDonell

The Janzen‐Connell hypothesis proposed that conspecific negative density or distance dependence (CNDDD) helps maintain the species richness of tropical rainforests through the action of specialist natural enemies that suppress conspecific recruitment beneath canopy trees. Empirical studies have since shown that CNDDD occurs not only in tropical rainforests but in a wide range of other vegetation types. Studies of CNDDD have invariably focused on its implications for species coexistence and diversity; rarely has its potential relevance to successional replacement patterns been explored. Here we present evidence for CNDDD that may shed new light on a long‐standing problem in New Zealand forest dynamics—the failure of relatively shade‐tolerant conifers (in particular Dacrydium cupressinum , Podocarpaceae) to regenerate beneath their own canopies in dense podocarp stands. We mapped tree positions on a 3‐ha plot in a central North Island podocarp/broadleaf forest in which diameter distributions indicated two discrete cohorts of D. cupressinum trees and used pair‐correlation functions to measure hetero‐ and conspecific spatial associations between small and large trees. Small trees (5–60 cm diameter) of D. cupressinum were significantly negatively associated with large conspecific trees of the older cohort at radii of up to 7 m. This pattern cannot readily be explained by light requirements, as small trees of a less shade‐tolerant podocarp ( Phyllocladus trichomanoides ) were distributed independently of large D. cupressinum . Small trees of the only other species well‐represented by large diameters (the broadleaved species Elaeocarpus dentatus , Elaeocarpaceae) were also negatively associated with large conspecific trees, albeit across a narrower range of radii. Our results suggest CNDDD might contribute to the characteristic failure of D. cupressinum to regenerate in central North Island dense podocarp stands, regeneration resuming only when attrition or exogenous disturbance reduces the D. cupressinum overstorey to scattered emergent trees.

Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR (Light Detection and Ranging) sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). We evaluate the capability of a demonstration YellowScan Navigator topo-bathymetric, full waveform LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long reach of the braided River Feshie, Scotland. Ground-truth data, with centimetre accuracy, were collected across wet areas using an echo-sounder, and in wet and dry areas using RTK-GNSS (Real-Time Kinematic Global Navigation Satellite System). The processed point cloud had a density of 62 points/m². Ground-truth mean errors (and standard deviation) across dry gravel bars were 0.06 ± 0.04 m, along shallow channel beds were −0.03 ± 0.12 m and for deep channels were −0.08 m ± 0.23 m. Geomorphic units with a concave three-dimensional shape (pools, troughs), associated with deeper water, had larger negative errors and wider ranges of residuals than planar or convex units. The case study demonstrates the potential of using UAV topo-bathymetric LiDAR to enhance survey efficiency but a need to evaluate spatial error distribution.

Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Over the last decade, considerable advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). Here we evaluate the capability of YellowScan Navigator topo-bathymetric LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long braided riverscape. This sensor uses full waveform returns, to ensure continuity between underwater points and the surrounding terrain, and has a 44° field of view. In August 2024, a point cloud was collected across a 1 km long, 200 m wide reach of the braided River Feshie, Scotland. Ground-truth data were collected across wet areas using a Sontek M9 Acoustic Doppler Current Profiler (ADCP) as an echo-sounder, and also survey-grade RTK-GNSS (Global Navigation Satellite System) in both wet and dry areas. The processed LiDAR point cloud featured over 10 million points, with a density of approximately 62 points / m2. These points were compared to the ground truth data to assess the vertical accuracy of the survey. Ground-truth mean errors (and standard deviation) across dry gravel bars surveyed with RTK-GNSS were 0.06 ± 0.04 m (n=237). Mean errors along the bed of the shallow channels surveyed with RTK-GNSS were -0.04 ± 0.23 m (n=562). Additionally, mean errors for deeper channels measured with the ADCP’s echo sounder were -0.08m ± 0.23 m (n = 2673). Overall, this case study demonstrates the potential of using a new generation of topo-bathymetric LiDAR sensors that can be mounted on UAVs. This has the potential to further enhance field surveys of wet-dry environments by reducing logistical workloads and increasing efficiency of these surveys. Compared to the various techniques for correcting bathymetric data from Structure-from-Motion imagery or point clouds this direct measurement needs less processing to achieve to continuous topo-bathymetric surface.

River restoration is key to realising ambitions to improve the biodiversity of rivers and to contribute to natural flood risk management. However, a dearth of detailed, accurate and consistently acquired, long-term topographic monitoring constrains the available evidence base to evaluate the efficacy of different river restoration approaches. Upland gravel-bed river realignment schemes are emblematic of this challenge. Here, the results from monitoring five contemporary upland river restoration sites in Scotland and the North-West of England are presented. The topography of 9 km of restored reaches at Whit Beck, the River Lyvennet, Swindale Beck, Allt Lorgy and the River Nairn was measured for a period of approximately one decade after each river realignment. The full extent of each scheme was surveyed every 1-3 years, with the frequency dependent on the geomorphic dynamism of the scheme. A variety of geomatics technologies were deployed to survey topography including, robotic total stations, RTK-GNSS, Structure-from-Motion photogrammetry, Terrestrial Laser Scanning and Unmanned Aerial Vehicle LiDAR. This unique dataset has enabled geomorphic change to be mapped and annual sediment fluxes to be quantified. Moreover, the high-resolution topographic datasets enable the geomorphic unit development of each scheme to be mapped using the Geomorphic Unit Toolbox (GUT). Together, this dataset enables three questions to be investigated: (i) what is the geomorphic unit composition of restored rivers?; (ii) how does geomorphic unit diversity develop post-restoration; and (iii) what geomorphic mechanisms are sustaining geomorphic unit diversity? We show that different restoration schemes have contrasting geomorphic unit assemblages, which are influenced by sediment supply, scheme constraints, in-channel and riparian wood and vegetation, and intervention through adaptive management approaches. The sediment budget for Swindale Beck exemplifies the trend in total volumetric topographic change through time; change is greater in the first few years following restoration and then declines once the river has adjusted the imposed boundary conditions. Topographic change initially increases the aerial extent of geomorphic units, the aerial extents of erosion and deposition between surveys then become similar and the extent of the active river channel remains approximately constant. Overall, across all schemes, there is declining geomorphic change with time but geomorphic unit, and thus physical habitat, diversity are maintained. These findings provide strong evidence for how physical habitat diversity and quantity have both increased and been maintained as a consequence of river realignment and should underpin efforts to scale up from demonstration sites to catchment-scale restoration efforts.

Unoccupied aerial vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion (SfM) photogrammetry. Advances in UAV payloads and the advent of solid‐state LiDAR have enabled consumer‐grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV‐acquired images. We evaluate the application of a DJI Zenmuse L1 solid‐state LiDAR sensor on a Matrice 300 RTK UAV to generate digital elevation models (DEMs). To assess flying height (60–80 m) and speed parameters (5–10 ms⁻¹) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter and classify the point clouds to produce a raster DEM product. A dense control network showed that there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multiscale Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of −0.015 m on unvegetated gravel bars. Multiscale Model to Model Cloud Comparison (M3C2) residuals compared UAV LiDAR and Terrestrial Laser Scanner point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid‐state LiDAR was effective at penetrating sparse canopy‐type vegetation but was less penetrable through dense ground‐hugging vegetation (e.g. heather, thick grass). Whilst UAV solid‐state LiDAR needs to be supplemented with bathymetric mapping to produce wet–dry DEMs, by itself, it offers advantages to comparable geomatics technologies for kilometre‐scale surveys. Ten best practice recommendations will assist users of UAV solid‐state LiDAR to produce bare earth DEMs.

Unoccupied Aerial Vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion photogrammetry (SfM). Advances in UAV payloads and the advent of solid-state LiDAR have enabled consumer-grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV acquired images. We evaluate the application of a DJI Zenmuse L1 solid-state LiDAR sensor on a Matrice 300 RTK UAV to generate Digital Elevation Models (DEMs). To assess flying height (60-80 m) and speed parameters (5-10 ms-1) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter, and classify the point clouds to produce a raster DEM product. A dense control network showed there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multi-Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of -0.015 m on unvegetated gravel bars. M3C2 residuals compared UAV LiDAR and TLS point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid-state LiDAR was effective at penetrating sparse canopy-type vegetation but was less penetrable through dense ground-hugging vegetation such as heather or thick grass. Whilst UAV solid-state LiDAR needs to be supplemented with bathymetric mapping to produce spatially continuous wet-dry DEMs, by itself it offers several advantages to comparable geomatics technologies for km-scale surveys. Ten best practice recommendations will assist users of UAV solid-state LiDAR to produce bare earth DEMs in geomorphic environments.

Quantitative descriptions of stream network and river catchment characteristics provide valuable context for enabling geomorphologically-informed sustainable river management. For countries where high-quality topographic data are available, there are opportunities to enable open access availability of baseline products from systematic assessment of morphometric and topographic characteristics. In this study, we present a national-scale assessment of fundamental topographic characteristics of Philippine river systems. We applied a consistent workflow using TopoToolbox V2 to delineate stream networks and river catchments using a nationwide digital elevation model (DEM) acquired in 2013 and generated through airborne Interferometric Synthetic Aperture Radar (IfSAR). We assessed morphometric and topographic characteristics for 128 medium- to large-sized catchments (catchment area > 250 km²) and organised the results in a national-scale geodatabase. The dataset realises the potential of topographic data as part of river management applications, by enabling variations in hydromorphology to be characterised and contextualised. The dataset is used to reveal the diversity of stream networks and river catchments in the Philippines. Catchments have a continuum of shapes (Gravelius compactness coefficient ranges from 1.05 to 3.29) with drainage densities that range from 0.65 to 1.23 km/km². Average catchment slope ranges from 3.1 to 28.1° and average stream slope varies by more than an order of magnitude from 0.004 to 0.107 m/m. Inter-catchment analyses show the distinctive topographic signatures of adjacent river catchments; examples from NW Luzon highlight topographic similarity between catchments whereas examples from Panay Island shown marked topographic differences. These contrasts underline the importance of using place-based analyses for sustainable river management applications. By designing an interactive ArcGIS web-application to display the national-scale geodatabase, we improve data accessibility and enable users to freely access, explore and download the data (https://glasgow-uni.maps.arcgis.com/apps/webappviewer/index.html?id=a88b9ca0919f4400881eab4a26370cee). The national-scale geodatabase provides a baseline understanding of fundamental topographic characteristics in support of varied geomorphological, hydrological and geohazard susceptibility applications.