Motility is a central feature of many microorganisms and provides an efficient strategy to respond to environmental changes. Bacteria and archaea have developed fundamentally different rotary motors enabling their motility, termed flagellum and archaellum, respectively. Bacterial motility along chemical gradients, called chemotaxis, critically relies on the response regulator CheY, which, when phosphorylated, inverses the rotational direction of the flagellum via a switch complex at the base of the motor. The structural difference between archaellum and flagellum and the presence of functional CheY in archaea raises the question of how the CheY protein changed to allow communication with the archaeal motility machinery. Here we show that archaeal CheY shares the overall structure and mechanism of magnesium-dependent phosphorylation with its bacterial counterpart. However, bacterial and archaeal CheY differ in the electrostatic potential of the helix α4. The helix α4 is important in bacteria for interaction with the flagellar switch complex, a structure that is absent in archaea. We demonstrated that phosphorylation-dependent activation, and conserved residues in the archaeal CheY helix α4, are important for interaction with the archaeal-specific adaptor protein CheF. This forms a bridge between the chemotaxis system and the archaeal motility machinery. Conclusively, archaeal CheY proteins conserved the central mechanistic features between bacteria and archaea, but differ in the helix α4 to allow binding to an archaellum-specific interaction partner.

In northern Argentina, the assessment of degraded forests is a big challenge for both science and practice, due to their heterogeneous structure. However, new technologies could contribute to mapping post-disturbance canopy cover and basal area in detail. Therefore, this research assesses whether or not the inclusion of partial cover unmanned aerial vehicle imagery could reduce the classification error of a SPOT6 image used in an area-based inventory. BA was calculated from 77 ground inventory plots over 3944 ha of a forest affected by mixed-severity fires in the Argentinian Yungas. In total, 74% of the area was covered with UAV flights, and canopy height models were calculated to estimate partial canopy cover at three tree height classes. Basal area and partial canopy cover were used to formulate the adjusted canopy cover index, and it was calculated for 70 ground plots and an additional 20 image plots. Four classes of fire severity were created based on basal area and adjusted canopy cover index, and were used to run two supervised classifications over a segmented (algorithm multiresolution) wall-to-wall SPOT6 image. The comparison of the Cohan’s Kappa coefficient of both classifications shows that they are not significantly different (p-value: 0.43). However, the approach based on the adjusted canopy cover index achieved more homogeneous strata (Welch t-test with 95% of confidence). Additionally, UAV-derived canopy height model estimates of tree height were compared with field measurements of 71 alive trees. The canopy height models underestimated tree height with an RMSE ranging from 2.8 to 8.3 m. The best accuracy of the canopy height model was achieved using a larger pixel size (10 m), and for lower stocked plots due to high fire severity.

No abstract