Gerald Page

Measuring woody vegetation cover (WVC) is an essential part of ecological monitoring in rangelands. While continental-scale products that map vegetation cover from satellite remote sensing are available, it is uncertain whether these products are well suited for analyses at the sub-catchment and property scale. We evaluated a novel approach to regional-scale mapping of WVC in the southern rangelands of Western Australia, with focus on two ex-pastoral properties. We used random forest regression and a segmented linear regression trained on high resolution satellite imagery and applied to Landsat imagery, and validated them using an independent, high-resolution and multi-year dataset. We compared the performance of our models to that of a broadly used continental-scale fractional cover (FC) dataset available for Australia. We tested the sensitivity of our locally calibrated WVC models to monitor vegetation recovery following chain-clearing in 1973 compared with an adjacent un-cleared patch. Both locally calibrated models were five times more accurate at estimating WVC than the national-scale FC data. The cleared and uncleared plots showed expected trends in WVC (increasing and homeostatic, respectively) using both locally trained models. This study demonstrated that a segmented linear model based on a spectral index can reliably predict WVC at a pastoral-property scale over a multi-annual period. In contrast, continental scale products, while valid to track trends in WVC at larger scales, should be used with caution when guiding local management. The construction of a national product that consolidates regionally-calibrated models of WVC would provide more accurate local information and aggregated national trends.

The Full Carbon Accounting Model (FullCAM) simulates carbon (C) pools of live biomass, standing dead mass, debris and soil, the flows among them and the atmosphere, and the influences of fire and harvesting disturbances under Australian conditions. It is regularly used by governments, landowners, companies and researchers, at continental, regional and local scales. Recently, FullCAM was calibrated for seven categories of native tropical savanna vegetation. However, for non-savanna native vegetation, calibrated parameters are available for only two general vegetation categories, based on whether the annual rainfall exceeds or falls below 500 mm. These two categories are too broad to capture the large range of growth conditions, vegetation structures and species assemblages that occur across Australia’s native woody vegetation. Here, our objective was to improve FullCAM’s ability to model variation in C pools and post-disturbance recovery among eight native vegetation categories, from shrublands to rainforests, for which there were differences in biomass allocation, litterfall and/or decomposition. To do this, we calibrated FullCAM for each vegetation type, including 14 parameters that were calculated directly from field observations and 17 that were calibrated using a dataset containing about 9300 field plots with measurements of at least one woody vegetation C stock. New parameters (compared with the two general parameter sets) reduced bias from 77 to 25 % (averaged across C stocks), and root mean square error from 44 to 30 Mg C ha-1. Model accuracy could be further improved (i) by focusing on sites with a known disturbance history, (ii) calibrating as many vegetation categories as possible (instead of eight categories generalising across many species), and (iii) adding more detail to growth calculations to quantify factors that may not be adequately represented by FullCAM’s growth equation.

A declining spring snowpack is expected to have widespread effects on montane and subalpine forests in western North America and across the globe. The way that tree water demands respond to this change will have important impacts on forest health and downstream water subsidies. Here, we present data from a network of sap velocity sensors and xylem water isotope measurements from three common tree species (Picea engelmannii, Abies lasiocarpa and Populus tremuloides) across a hillslope transect in a subalpine watershed in the Upper Colorado River basin. We use these data to compare tree- and stand-level responses to the historically high spring snowpack but low summer rainfall of 2019 against the low spring snowpack but high summer rainfall amounts of 2021 and 2022. From the sap velocity data, we found that only 40 % of the trees showed an increase in cumulative transpiration in response to the large snowpack year (2019), illustrating the absence of a common response to interannual spring snowpack variability. The trees that increased water use during the year with the large spring snowpack were all found in dense canopy stands – irrespective of species – while trees in open-canopy stands were more reliant on summer rains and, thus, more active during the years with modest snow and higher summer rain amounts. Using the sap velocity data along with supporting measurements of soil moisture and snow depth, we propose three mechanisms that lead to stand density modulating the tree-level response to changing seasonality of precipitation:

1. Topographically mediated convergence zones have consistent access to recharge from snowmelt which supports denser stands with high water demands that are more reliant and sensitive to changing snow.

2. Interception of summer rain in dense stands reduces the throughfall of summer rain to surface soils, limiting the sensitivity of the dense stands to changes in summer rain.

3. Shading in dense stands allows the snowpack to persist deeper into the growing season, providing high local reliance on snow during the fore-summer (early-summer) drought period.

Combining data generated from natural gradients in stand density, like this experiment, with results from controlled forest-thinning experiments can be used to develop a better understanding of the responses of forested ecosystems to futures with reduced spring snowpack.

Australia's Great Western Woodlands are the largest intact temperate woodland ecosystem on Earth, spanning an area the size of the average European country. These woodlands are part of one of the world's biodiversity hotspots and, despite subsisting on just 200–400 mm of rainfall a year, can store considerable amounts of carbon. However, they face growing pressure from a combination of climate change and increasingly frequent and large wildfires, which have burned over a third of these slow-growing, fire-sensitive woodlands in last 50 years alone. To develop conservation strategies that bolster the long-term resilience of this unique ecosystem, we urgently need to understand how much old-growth woodland habitat remains intact and where it is distributed across this vast region. To tackle this challenge, we brought together data from an extensive network of field plots distributed across the region and combined this with information on vegetation 3D structure derived from drone, airborne and spaceborne LiDAR. Using this unique dataset, we developed a novel modelling framework to generate the first high-resolution maps of woodland tree size and age structure across the entire region. We found that 41.2% of the woodland habitat is covered by old-growth stands, equivalent to an area of approximately 39,187 km2. Only 10% of these old-growth woodlands fall within current protected areas managed by the state government. Instead, most remaining old-growth woodlands are found either within the Ngadju Indigenous Protected Area (26.9%) or outside of formal protected areas on leaseholds and privately owned lands (57.2%). Our maps of woodland size and age structure will help guide the targeted management and conservation of the Great Western Woodlands. Moreover, by developing a robust pipeline for integrating LiDAR data from multiple platforms, our study paves the way for mapping the 3D structure and carbon storage of open and heterogeneous woodland ecosystems from space.
•Novel framework fusing drone, airborne and satellite LiDAR for large-scale mapping.•Fires have burned 39% of the world’s largest temperate woodland in just 50 years.•Old-growth woodlands still cover 41% the region, equivalent to 38,715 km2.•Only 10% of old-growth woodlands are in protected areas managed by the government.

Understanding and predicting the relationship between leaf temperature (T-leaf) and air temperature (T-air) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime Tleaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below Tair at higher temperatures (i.e., > similar to 25-30 degrees C) leading to slopes <1 in T-leaf /T-air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (Tcan) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to Tcan/Tair slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the Tcan/Tair relationship. Canopy structure also plays an important role in Tcan dynamics. Future climate warming is likely to lead to even greater Tcan, with attendant impacts on forest carbon cycling and mortality risk.

Canopy temperature T-can is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on T-can and forecasting canopy responses to weather extremes and climate change are difficult due to sparse measurements of T-can at appropriate spatial and temporal scales. Burgeoning observations of T-can from thermal cameras enable evaluation of energy budget theory and better understanding of how environmental controls, leaf traits and canopy structure influence temperature patterns. The canopy scale is relevant for connecting to remote sensing and testing biosphere model predictions. We anticipate that future breakthroughs in understanding of ecosystem responses to climate change will result from multiscale observations of T-can across a range of ecosystems.

Extant conifer species may be susceptible to rapid environmental change owing to their long generation times, but could also be resilient due to high levels of standing genetic diversity. Hybridisation between closely related species can increase genetic diversity and generate novel allelic combinations capable of fuelling adaptive evolution. Our study unravelled the genetic architecture of adaptive evolution in a conifer hybrid zone formed between Pinus strobiformis and P. flexilis. Using a multifaceted approach emphasising the spatial and environmental patterns of linkage disequilibrium and ancestry enrichment, we identified recently introgressed and background genetic variants to be driving adaptive evolution along different environmental gradients. Specifically, recently introgressed variants from P. flexilis were favoured along freeze-related environmental gradients, while background variants were favoured along water availability-related gradients. We posit that such mosaics of allelic variants within conifer hybrid zones will confer upon them greater resilience to ongoing and future environmental change and can be a key resource for conservation efforts.