ORCID Profile
0000-0002-0151-9037
Current Organisation
University of British Columbia
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Publisher: Wiley
Date: 17-05-2022
DOI: 10.1002/EAP.2603
Abstract: Protected areas (PA) are an effective means of conserving bio ersity and protecting suites of valuable ecosystem services. Currently, many nations and international governments use proportional area protected as a critical metric for assessing progress towards bio ersity conservation. However, the areal and other common metrics do not assess the effectiveness of PA networks, nor do they assess how representative PA are of the ecosystems they aim to protect. Topography, stand structure, and land cover are all key drivers of bio ersity within forest environments, and are well‐suited as indicators to assess the representation of PA. Here, we examine the PA network in British Columbia, Canada, through drivers derived from freely‐available data and remote sensing products across the provincial biogeoclimatic ecosystem classification system. We examine biases in the PA network by elevation, forest disturbances, and forest structural attributes, including height, cover, and biomass by comparing a random s le of protected and unprotected pixels. Results indicate that PA are commonly biased towards high‐elevation and alpine land covers, and that forest structural attributes of the park network are often significantly different in protected versus unprotected areas (426 out of 496 forest structural attributes found to be different p 0.01). Analysis of forest structural attributes suggests that establishing additional PA could ensure representation of various forest structure regimes across British Columbia's ecosystems. We conclude that these approaches using free and open remote sensing data are highly transferable and can be accomplished using consistent datasets to assess PA representations globally.
Publisher: Elsevier BV
Date: 07-2012
Publisher: Elsevier BV
Date: 10-2001
Publisher: Wiley
Date: 18-09-2022
DOI: 10.1111/GCB.16418
Abstract: Forest plantations in Chile occupy more than 2.2 million ha and are responsible for 2.1% of the GDP of the country's economy. The ability to accurately predictions of plantations productivity under current and future climate has an impact can enhance on forest management and industrial wood production. The use of process‐based models to predict forest growth has been instrumental in improving the understanding and quantifying the effects of climate variability, climate change, and the impact of atmospheric CO 2 concentration and management practices on forest growth. This study uses the 3‐PG model to predict future forest productivity Eucalyptus globulus and Pinus radiata . The study integrates climate data from global circulation models used in CMIP5 for scenarios RCP26 and RCP85, digital soil maps for physical and chemical variables. Temporal and spatial tree growth inventories were used to compare with the 3‐PG predictions. The results indicated that forest productivity is predicted to potentially increase stand volume (SV) over the next 50 years by 26% and 24% for the RCP26 scenario and between 73% and 62% for the RCP85 scenario for E. globulus and P. radiata , respectively. The predicted increases can be explained by a combination of higher level of atmospheric CO 2 , air temperatures closer to optimum than current, and increases in tree water use efficiency. If the effect of CO 2 is not considered, the predicted differences of SV for 2070 are 16% and 14% for the RCP26 scenario and 22% and 14% for RCP85 for the two species. While shifts in climate and increasing CO 2 are likely to benefit promote higher productivity, other factors such as lack insufficient availability of soil nutrients, events such as increasing frequency and duration of droughts, longer periods of extreme temperatures, competing vegetation, and occurrence of new pests and diseases may compromise these potential gains.
Publisher: Elsevier BV
Date: 09-2007
Publisher: Elsevier BV
Date: 05-2011
Publisher: Wiley
Date: 20-05-2011
Publisher: Springer Science and Business Media LLC
Date: 24-05-2021
Publisher: Springer Science and Business Media LLC
Date: 09-2007
Publisher: Wiley
Date: 06-02-2023
DOI: 10.1111/NPH.18713
Abstract: Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non‐destructive estimates of leaf traits, but it remains unclear whether general trait‐spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 s les from 103 species. These s les span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least‐squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 0.85 %RMSE 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 = 0.55–0.85 %RMSE = 12.7–19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Publisher: Informa UK Limited
Date: 13-12-2010
Publisher: Informa UK Limited
Date: 05-2015
Publisher: Elsevier BV
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 25-10-2021
Publisher: Elsevier BV
Date: 02-2012
Publisher: Springer Science and Business Media LLC
Date: 15-06-2010
Publisher: Wiley
Date: 04-11-2013
DOI: 10.1111/DDI.12146
Publisher: Springer Science and Business Media LLC
Date: 19-07-2021
Publisher: Springer Science and Business Media LLC
Date: 13-05-2021
DOI: 10.1038/S41559-021-01451-X
Abstract: Monitoring global bio ersity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential bio ersity variables (EBVs) is emerging for monitoring bio ersity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing bio ersity products that can further improve the monitoring of geospatial bio ersity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of bio ersity from satellites. Bio ersity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for ex le, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of bio ersity from local to global scales.
Publisher: Springer Science and Business Media LLC
Date: 24-08-2023
Publisher: American Association for the Advancement of Science (AAAS)
Date: 18-01-2013
Abstract: A global system of harmonized observations is needed to inform scientists and policy-makers.
Publisher: Wiley
Date: 10-04-2023
DOI: 10.1111/NPH.18902
Abstract: Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits, and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.
Publisher: Oxford University Press (OUP)
Date: 15-05-2015
Publisher: Elsevier BV
Date: 09-2014
Publisher: Elsevier BV
Date: 10-2015
Publisher: Elsevier BV
Date: 12-2011
Publisher: Elsevier BV
Date: 08-2005
Start Date: 2018
End Date: 2020
Funder: Australian Research Council
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