Biology subjects

Forbes, B.

Publications and source records attributed to Forbes, B..

2 recordsLinked to original sources

bioRxiv2024

Sediment-associated processes drive spatial variation in ecosystem respiration in the Yakima River basin

Hyporheic zones (HZ) can contribute substantially to total stream ecosystem respiration (ERtot). HZ-focused process-based models may, therefore, effectively predict ERtot across sites, yet this remains untested under variable environmental conditions. Here we evaluate whether spatial variation in HZ respiration predicted via a process-based model explains spatial variation in field-estimates of ERtot across 33 sites in the Yakima River basin in Washington State, USA. We found that HZ respiration predictions did not explain spatial variation in field estimates of ERtot. To investigate further, we partitioned ERtot contributions into water column respiration (ERwc) and sediment-associated respiration (ERsed). ERsed contributed >50% of ERtot at 88% of sites, though relative contributions varied substantially. Despite this dominance, modeled HZ respiration explained neither spatial variation in ERtot nor in ERsed, suggesting that the HZ model alone does not capture the drivers of sediment-associated respiration across these sites. Instead, ERsed spatial variation was primarily explained by gross primary production, stream slope, velocity, and total dissolved nitrogen rather than median grain size, a primary control of HZ respiration predicted by the process-based model. Consistent with recent studies, our results indicate that improving basin-scale ERtot predictions requires integrating hydrologic and biogeochemical processes across hyporheic, benthic, and water column zones.

ecology↗

bioRxiv2022

TLS2trees: a scalable tree segmentation pipeline for TLS data

Above Ground Biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically [≥]1 ha) using inventory measurements and allometry. In recent years, Terrestrial Laser Scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated across 10 plots of 7 forest types; from open savanna to dense tropical rainforest, where a total of 10,557 trees are segmented. TLS2trees segmented trees are compared to 1,281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is {+/-}0.4 m3 and %RMSE is ~60%. To facilitate improvements to the presented methods as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software (FOSS).

ecology↗