Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region.Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Current proportion (dark shade) and expected change in proportion (light shade) of the area covered by vegetation types under a reduced rainfall scenario for the four areas with the same extend as the areas displayed in Figure 5 (Green) and directly around granite outcrops within areas with the same extend as displayed in Figure 7 (Red).

<p>Current proportion (dark shade) and expected change in proportion (light shade) of the area covered by vegetation types under a reduced rainfall scenario for the four areas with the same extend as the areas displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082778#pone-0082778-g005" target="_blank">Figure 5</a> (Green) and directly around granite outcrops within areas with the same extend as displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082778#pone-0082778-g007" target="_blank">Figure 7</a> (Red).</p

Vertical profiles showing the smoothed percentage of voxel fill (PVF) within a 12 by 12 m spatial window (3 by 3 pixels) as a function of height above the ground surface.

<p>Each line indicates a unique PVF, and multiple PVFs were assigned to a structural class if occurring within a ground-truthed vegetation type.</p

Linear regression and leave-one-out (LOO) validation statistics of multiple linear regressions with parameter estimates of environmental factors determining maximum canopy height and ground coverage.

<p>The explanatory variables included annual rainfall (R, mm), probability of a soil deeper than 0.5 m (pDS), elevation range (EV, m) and their interactions, and granitic substrate. Insignificant terms (p<0.05) were excluded from the fitted models. Models evaluated were 1) all individual plots, 2) only on-flow plots, 3) geometric means for each plot type per outcrop and 4) geometric means of structural class polygon attributes covering at least 20% of plot areas.</p><p>Not significant;</p><p>p<0.05;</p><p>p<0.01;</p><p>Transect and elevation range were.</p

Diversity and spatial relationships with topography for current (left hand side) and future (2070) vegetation structure under a 20% rainfall reduction scenario (right hand side) on four granite outcrops (GOs) in the SWAFR.

<p>Areas surrounding GOs are shown from a birds-eye view with an elevation exaggeration of four.</p

Comparison of vegetation structure on and around a granite outcrop in each of eight areas in the Yilgarn Craton (top) and the Albany-Fraser Orogen (bottom), indicatively displayed according to decreasing annual rainfall from left to right.

<p>Comparison of vegetation structure on and around a granite outcrop in each of eight areas in the Yilgarn Craton (top) and the Albany-Fraser Orogen (bottom), indicatively displayed according to decreasing annual rainfall from left to right.</p

Granite outcrop study areas, showing rainfall (mm yr⁻¹, WorldClim dataset), elevation range (ER), mean canopy height and ground coverage for plots in off-flow areas (OF), intermediate sites with on- and off-flow (INT) or in on-flow areas near each outcrop (ON).

<p>Based on extremes in the elevation of plot locations.</p><p>Rainfall appears high, but is probably reasonable, considering nearby Mt Howick rainfall of 379 mm (1994–2012, DAFWA) at approximately the same distance from the coast.</p><p>The abbreviation for GO in parenthesis is as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082778#pone-0082778-g001" target="_blank">Figure 1</a>.</p

Combined effects of annual rainfall and the probability of a soil deeper than 0.5 m on mean canopy height and ground coverage in plots on 16 granite outcrops across the SWAFR.

<p>Each point indicates the mean value derived from all plots with the same structural class. Equations fitted (with x = pSD×R): Ground cover = 25.5+69.6E-3x (R² = 0.75); Canopy height = 0.21+28.4E-4x+2.9E-5x² (R² = 0.93). The right-hand side figures indicate the current and future canopy height and ground cover for these class means.</p

RGB images (A) and structural vegetation classes (B) of current vegetation and projected structural vegetation classes based on a 20% rainfall reduction scenario (C) zoomed to areas surrounding granite at the surface in Chiddarcooping (CHI), Boyagin (BOY) and Mount Frankland (FRA).

<p>RGB images (A) and structural vegetation classes (B) of current vegetation and projected structural vegetation classes based on a 20% rainfall reduction scenario (C) zoomed to areas surrounding granite at the surface in Chiddarcooping (CHI), Boyagin (BOY) and Mount Frankland (FRA).</p