ORCID Profile
0000-0002-8295-4494
Current Organisation
Oregon State University
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Publisher: Oxford University Press (OUP)
Date: 07-2022
DOI: 10.1093/PNASNEXUS/PGAC115
Abstract: Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts (ii) embrace different ways of knowing and knowledge generation (iii) promote exploration of fundamental science (iv) capitalize on the “firehose” of data for societal benefit and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across erse communities can we effectively undertake research that improves outcomes in our more fiery future.
Publisher: Proceedings of the National Academy of Sciences
Date: 03-04-2023
Publisher: Proceedings of the National Academy of Sciences
Date: 12-09-2022
Abstract: 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 T leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T air at higher temperatures (i.e., ∼25–30°C) leading to slopes 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 ( T can ) 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 T can / T air slopes 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 T can / T air relationship. Canopy structure also plays an important role in T can dynamics. Future climate warming is likely to lead to even greater T can , with attendant impacts on forest carbon cycling and mortality risk.
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 05-2023
Publisher: Wiley
Date: 02-04-2021
DOI: 10.1111/NPH.17321
Abstract: Canopy temperature T
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 05-02-2021
DOI: 10.1038/S42003-020-01632-7
Abstract: 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 ersity. Hybridisation between closely related species can increase genetic ersity 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.
Publisher: Wiley
Date: 10-12-2022
DOI: 10.1111/NPH.18632
Abstract: To what degree plant ecosystems thermoregulate their canopy temperature ( T c ) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability. With global datasets of T c , air temperature ( T a ), and other environmental and biotic variables from FLUXNET and satellites, we tested the ‘limited homeothermy’ hypothesis (indicated by T c & T a regression slope 1 or T c T a around midday) across global extratropics, including temporal and spatial dimensions. Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 or T c T a around midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, their T c – T a difference (Δ T ) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site‐mean Δ T exhibits larger variations than the slope indicator, suggesting Δ T is a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial‐wide Δ T variation (0–6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%). These results demonstrate erse thermoregulation patterns across global extratropics, with most ecosystems negating the ‘limited homeothermy’ hypothesis, but their thermoregulation still occurs, implying that slope 1 or T c T a are not necessary conditions for plant thermoregulation.
Publisher: MDPI AG
Date: 10-12-2020
DOI: 10.3390/RS12244041
Abstract: Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (κ = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert’s assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a ‘growth vigor’ grouping used to identify the degree of impact of the disease was possible with 79.7% (κ = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a ‘new search algorithm’ that combines both the p-value of a 2-s le t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 18-10-2019
Abstract: Bastin
No related grants have been discovered for Christopher Still.